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Last updated on 2026-08-10 10:50:43 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.0-40 | 5.76 | 290.94 | 296.70 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 2.0-40 | 4.17 | 244.31 | 248.48 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.0-40 | 197.87 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 2.0-40 | 199.61 | ERROR | |||
| r-devel-windows-x86_64 | 2.0-40 | 9.00 | 272.00 | 281.00 | ERROR | |
| r-patched-linux-x86_64 | 2.0-40 | 5.78 | 282.56 | 288.34 | ERROR | |
| r-release-linux-x86_64 | 2.0-40 | 5.05 | 285.77 | 290.82 | ERROR | |
| r-release-macos-arm64 | 2.0-40 | 1.00 | 78.00 | 79.00 | OK | |
| r-release-macos-x86_64 | 2.0-40 | 4.00 | 336.00 | 340.00 | OK | |
| r-release-windows-x86_64 | 2.0-40 | 8.00 | 282.00 | 290.00 | ERROR | |
| r-oldrel-macos-arm64 | 2.0-40 | 1.00 | 96.00 | 97.00 | OK | |
| r-oldrel-macos-x86_64 | 2.0-40 | 4.00 | 391.00 | 395.00 | OK | |
| r-oldrel-windows-x86_64 | 2.0-40 | 11.00 | 361.00 | 372.00 | ERROR |
Version: 2.0-40
Check: examples
Result: ERROR
Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: SL.glmnet
> ### Title: Elastic net regression, including lasso and ridge
> ### Aliases: SL.glmnet
>
> ### ** Examples
>
>
> # Load a test dataset.
> data(PimaIndiansDiabetes2, package = "mlbench")
Warning in data(PimaIndiansDiabetes2, package = "mlbench") :
data set ‘PimaIndiansDiabetes2’ not found
> data = PimaIndiansDiabetes2
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
SL.biglasso 5.859 0.046 7.878
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [63s/74s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 2.0-40
Check: examples
Result: ERROR
Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: SL.glmnet
> ### Title: Elastic net regression, including lasso and ridge
> ### Aliases: SL.glmnet
>
> ### ** Examples
>
>
> # Load a test dataset.
> data(PimaIndiansDiabetes2, package = "mlbench")
Warning in data(PimaIndiansDiabetes2, package = "mlbench") :
data set ‘PimaIndiansDiabetes2’ not found
> data = PimaIndiansDiabetes2
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
SL.biglasso 5.953 0.096 7.584
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [56s/70s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 2.0-40
Check: examples
Result: ERROR
Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:
> ### Name: SL.glmnet
> ### Title: Elastic net regression, including lasso and ridge
> ### Aliases: SL.glmnet
>
> ### ** Examples
>
>
> # Load a test dataset.
> data(PimaIndiansDiabetes2, package = "mlbench")
Warning in data(PimaIndiansDiabetes2, package = "mlbench") :
data set ‘PimaIndiansDiabetes2’ not found
> data = PimaIndiansDiabetes2
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-x86_64
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [45s/49s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.76778
R squared (OOB): 0.8727013
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08288635
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.37326
R squared (OOB): 0.8773654
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08264651
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [47s/50s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 2.0-40
Check: tests
Result: ERROR
Running 'testthat.R' [57s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
<objectNotFoundError/error/condition>
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-windows-x86_64
Version: 2.0-40
Check: examples
Result: ERROR
Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: SL.glmnet
> ### Title: Elastic net regression, including lasso and ridge
> ### Aliases: SL.glmnet
>
> ### ** Examples
>
>
> # Load a test dataset.
> data(PimaIndiansDiabetes2, package = "mlbench")
Warning in data(PimaIndiansDiabetes2, package = "mlbench") :
data set ‘PimaIndiansDiabetes2’ not found
> data = PimaIndiansDiabetes2
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
SL.biglasso 6.661 0.076 7.814
Flavor: r-patched-linux-x86_64
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [60s/78s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-patched-linux-x86_64
Version: 2.0-40
Check: examples
Result: ERROR
Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: SL.glmnet
> ### Title: Elastic net regression, including lasso and ridge
> ### Aliases: SL.glmnet
>
> ### ** Examples
>
>
> # Load a test dataset.
> data(PimaIndiansDiabetes2, package = "mlbench")
Warning in data(PimaIndiansDiabetes2, package = "mlbench") :
data set ‘PimaIndiansDiabetes2’ not found
> data = PimaIndiansDiabetes2
Error: object 'PimaIndiansDiabetes2' not found
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
SL.biglasso 5.651 0.053 7.402
Flavor: r-release-linux-x86_64
Version: 2.0-40
Check: tests
Result: ERROR
Running ‘testthat.R’ [63s/83s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-linux-x86_64
Version: 2.0-40
Check: tests
Result: ERROR
Running 'testthat.R' [54s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in .jnew("bartMachine.bartMachineRegressionMultThread") :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-windows-x86_64
Version: 2.0-40
Check: tests
Result: ERROR
Running 'testthat.R' [74s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(SuperLearner)
Loading required package: nnls
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.22-7
Super Learner
Version: 2.0-40
Package created on 2025-12-14
>
> test_check("SuperLearner")
Call:
SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,
xgb_grid$names), cvControl = list(V = 2))
Risk Coef
SL.mean_All 1.060266 0.00000000
SL.xgboost_All 1.248342 0.17377091
SL.xgb.1_All 1.046647 0.00000000
SL.xgb.2_All 1.039310 0.48619144
SL.xgb.3_All 1.033972 0.27648732
SL.xgb.4_All 1.046058 0.00000000
SL.xgb.5_All 1.057237 0.00000000
SL.xgb.6_All 1.053070 0.00000000
SL.xgb.7_All 1.054268 0.00000000
SL.xgb.8_All 1.049384 0.06355032
SL.xgb.9_All 1.059925 0.00000000
SL.xgb.10_All 1.059592 0.00000000
SL.xgb.11_All 1.059665 0.00000000
SL.xgb.12_All 1.058975 0.00000000
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
Error in build_bart_machine(X = X, y = y, Xy = Xy, num_trees = num_trees, :
java.lang.UnsupportedClassVersionError: bartMachine/bartMachineRegressionMultThread has been compiled by a more recent version of the Java Runtime (class file version 65.0), this version of the Java Runtime only recognizes class file versions up to 55.0
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0222):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.29
R-squared: 0.72
Signal-to-noise ratio: 2.63
Scale estimate (sigma): 4.826
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0026):
-------------------------------------------------
Nonzero coefficients: 12
Cross-validation error (deviance): 0.66
R-squared: 0.48
Signal-to-noise ratio: 0.94
Prediction error: 0.123
lasso-penalized linear regression with n=506, p=13
At minimum cross-validation error (lambda=0.0362):
-------------------------------------------------
Nonzero coefficients: 11
Cross-validation error (deviance): 23.30
R-squared: 0.72
Signal-to-noise ratio: 2.62
Scale estimate (sigma): 4.827
lasso-penalized logistic regression with n=506, p=13
At minimum cross-validation error (lambda=0.0016):
-------------------------------------------------
Nonzero coefficients: 13
Cross-validation error (deviance): 0.63
R-squared: 0.50
Signal-to-noise ratio: 0.99
Prediction error: 0.132
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 84.62063 0.02136708
SL.biglasso_All 26.01864 0.97863292
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.biglasso"), cvControl = list(V = 2))
Risk Coef
SL.mean_All 0.2346857 0
SL.biglasso_All 0.1039122 1
Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Degrees of Freedom: 505 Total (i.e. Null); 492 Residual
Null Deviance: 42720
Residual Deviance: 11080 AIC: 3028
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
[1] "coefficients" "residuals" "fitted.values"
[4] "effects" "R" "rank"
[7] "qr" "family" "linear.predictors"
[10] "deviance" "aic" "null.deviance"
[13] "iter" "weights" "prior.weights"
[16] "df.residual" "df.null" "y"
[19] "converged" "boundary" "call"
[22] "formula" "terms" "data"
[25] "offset" "control" "method"
[28] "contrasts" "xlevels"
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for gaussian family taken to be 22.51785)
Null deviance: 42716 on 505 degrees of freedom
Residual deviance: 11079 on 492 degrees of freedom
AIC: 3027.6
Number of Fisher Scoring iterations: 2
Call:
glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,
model = model)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.724 0.006446 **
crim -0.040649 0.049796 -0.816 0.414321
zn 0.012134 0.010678 1.136 0.255786
indus -0.040715 0.045615 -0.893 0.372078
chas 0.248209 0.653283 0.380 0.703989
nox -3.601085 2.924365 -1.231 0.218170
rm 1.155157 0.374843 3.082 0.002058 **
age -0.018660 0.009319 -2.002 0.045252 *
dis -0.518934 0.146286 -3.547 0.000389 ***
rad 0.255522 0.061391 4.162 3.15e-05 ***
tax -0.009500 0.003107 -3.057 0.002233 **
ptratio -0.409317 0.103191 -3.967 7.29e-05 ***
black -0.001451 0.002558 -0.567 0.570418
lstat -0.318436 0.054735 -5.818 5.96e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 669.76 on 505 degrees of freedom
Residual deviance: 296.39 on 492 degrees of freedom
AIC: 324.39
Number of Fisher Scoring iterations: 7
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.glm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.glm"))
Risk Coef
SL.mean_All 0.23580362 0.01315872
SL.glm_All 0.09519266 0.98684128
V1
Min. :0.004942
1st Qu.:0.035424
Median :0.196222
Mean :0.375494
3rd Qu.:0.781687
Max. :0.991313
Saving _problems/test-glmnet-11.R
Saving _problems/test-kernelKnn-10.R
Saving _problems/test-knn-10.R
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
lda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Coefficients of linear discriminants:
LD1
crim 0.0012515925
zn 0.0095179029
indus -0.0166376334
chas 0.1399207112
nox -2.9934367740
rm 0.5612713068
age -0.0128420045
dis -0.3095403096
rad 0.0695027989
tax -0.0027771271
ptratio -0.2059853828
black 0.0006058031
lstat -0.0816668897
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
[1] "coefficients" "residuals" "fitted.values" "effects"
[5] "weights" "rank" "assign" "qr"
[9] "df.residual" "xlevels" "call" "terms"
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-15.595 -2.730 -0.518 1.777 26.199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***
crim -1.080e-01 3.286e-02 -3.287 0.001087 **
zn 4.642e-02 1.373e-02 3.382 0.000778 ***
indus 2.056e-02 6.150e-02 0.334 0.738288
chas 2.687e+00 8.616e-01 3.118 0.001925 **
nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***
rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***
age 6.922e-04 1.321e-02 0.052 0.958229
dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***
rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***
tax -1.233e-02 3.760e-03 -3.280 0.001112 **
ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***
black 9.312e-03 2.686e-03 3.467 0.000573 ***
lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.745 on 492 degrees of freedom
Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338
F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16
Call:
stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)
Residuals:
Min 1Q Median 3Q Max
-0.80469 -0.23612 -0.03105 0.23080 1.05224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***
crim 0.0003028 0.0023585 0.128 0.897888
zn 0.0023028 0.0009851 2.338 0.019808 *
indus -0.0040254 0.0044131 -0.912 0.362135
chas 0.0338534 0.0618295 0.548 0.584264
nox -0.7242540 0.2741160 -2.642 0.008501 **
rm 0.1357981 0.0299915 4.528 7.48e-06 ***
age -0.0031071 0.0009480 -3.278 0.001121 **
dis -0.0748924 0.0143135 -5.232 2.48e-07 ***
rad 0.0168160 0.0047612 3.532 0.000451 ***
tax -0.0006719 0.0002699 -2.490 0.013110 *
ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***
black 0.0001466 0.0001928 0.760 0.447370
lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3405 on 492 degrees of freedom
Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065
F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16
Call:
SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 84.74142 0.0134192
SL.lm_All 23.62549 0.9865808
V1
Min. :-3.921
1st Qu.:17.514
Median :22.124
Mean :22.533
3rd Qu.:27.345
Max. :44.376
Call:
SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",
"SL.lm"))
Risk Coef
SL.mean_All 0.2358036 0
SL.lm_All 0.1115499 1
V1
Min. :0.0000
1st Qu.:0.1281
Median :0.3530
Mean :0.3899
3rd Qu.:0.6091
Max. :1.0000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2219962 0
SL.glmnet_All 0.1675829 1
SL.mean_All 0.2491000 0
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2062976 0.00000000
SL.glmnet_All 0.1656934 0.97040848
SL.mean_All 0.2464000 0.02959152
SL.bad_algorithm_All NA 0.00000000
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNLS2", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2071145 0.000000000
SL.glmnet_All 0.1644480 0.996278512
SL.mean_All 0.2491000 0.003721488
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.NNloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.5646032 0.1435759
SL.glmnet_All 0.4860594 0.8564241
SL.mean_All 0.6956962 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All Inf 0
SL.glmnet_All 0.4866903 1
SL.mean_All 0.7160070 0
SL.bad_algorithm_All NA 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_LS", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2058305 0
SL.glmnet_All 0.1542369 1
SL.mean_All 0.2464000 0
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 230.4838 0.1247876
SL.glmnet_All 192.7564 0.8752124
SL.mean_All 274.6155 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 226.0518 0.173761529
SL.glmnet_All 188.4293 0.818098488
SL.mean_All 274.6155 0.000000000
SL.bad_algorithm_All NA 0.008139983
Saving _problems/test-methods-218.R
Saving _problems/test-methods-220.R
Saving _problems/test-methods-227.R
Saving _problems/test-methods-229.R
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,
method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2287439 0.5103394
SL.glmnet_All 0.1693385 0.4896606
SL.mean_All 0.6014610 0.0000000
Error in (function (Y, X, newX, ...) : bad algorithm
Error in (function (Y, X, newX, ...) : bad algorithm
Removing failed learners: SL.bad_algorithm_All
Error in (function (Y, X, newX, ...) : bad algorithm
Call:
SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,
"SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))
Risk Coef
SL.rpart_All 0.2298600 0.3333333
SL.glmnet_All 0.1532062 0.3333333
SL.mean_All 0.5000000 0.3333333
SL.bad_algorithm_All NA 0.0000000
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Call:
qda(X, grouping = Y, prior = prior, method = method, tol = tol,
CV = CV, nu = nu)
Prior probabilities of groups:
0 1
0.6245059 0.3754941
Group means:
crim zn indus chas nox rm age dis
0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307
1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371
rad tax ptratio black lstat
0 11.588608 459.9209 19.19968 340.6392 16.042468
1 6.157895 322.2789 17.21789 383.3425 7.015947
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.60169
R squared (OOB): 0.8746648
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08149979
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Regression
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 5
Variable importance mode: none
Splitrule: variance
OOB prediction error (MSE): 10.39997
R squared (OOB): 0.8770496
Ranger result
Call:
ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)
Type: Probability estimation
Number of trees: 500
Sample size: 506
Number of independent variables: 13
Mtry: 3
Target node size: 1
Variable importance mode: none
Splitrule: gini
OOB prediction error (Brier s.): 0.08457002
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***
crim -1.080e-01 0.032865 -3.2865 1.087e-03 **
zn 4.642e-02 0.013727 3.3816 7.781e-04 ***
indus 2.056e-02 0.061496 0.3343 7.383e-01
chas 2.687e+00 0.861580 3.1184 1.925e-03 **
nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***
rm 3.810e+00 0.417925 9.1161 1.979e-18 ***
age 6.922e-04 0.013210 0.0524 9.582e-01
dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***
rad 3.060e-01 0.066346 4.6129 5.071e-06 ***
tax -1.233e-02 0.003761 -3.2800 1.112e-03 **
ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***
black 9.312e-03 0.002686 3.4668 5.729e-04 ***
lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 42716.3;
residuals df: 492; residuals deviance: 11078.78;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 3027.609; log Likelihood: -1498.804;
RSS: 11078.8; dispersion: 22.51785; iterations: 1;
rank: 14; max tolerance: 1e+00; convergence: FALSE.
Generalized Linear Model of class 'speedglm':
Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)
Coefficients:
------------------------------------------------------------------
Estimate Std. Error z value Pr(>|z|)
(Intercept) 10.682635 3.921395 2.7242 6.446e-03 **
crim -0.040649 0.049796 -0.8163 4.143e-01
zn 0.012134 0.010678 1.1364 2.558e-01
indus -0.040715 0.045615 -0.8926 3.721e-01
chas 0.248209 0.653283 0.3799 7.040e-01
nox -3.601085 2.924365 -1.2314 2.182e-01
rm 1.155157 0.374843 3.0817 2.058e-03 **
age -0.018660 0.009319 -2.0023 4.525e-02 *
dis -0.518934 0.146286 -3.5474 3.891e-04 ***
rad 0.255522 0.061391 4.1622 3.152e-05 ***
tax -0.009500 0.003107 -3.0574 2.233e-03 **
ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***
black -0.001451 0.002558 -0.5674 5.704e-01
lstat -0.318436 0.054735 -5.8178 5.964e-09 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
null df: 505; null deviance: 669.76;
residuals df: 492; residuals deviance: 296.39;
# obs.: 506; # non-zero weighted obs.: 506;
AIC: 324.3944; log Likelihood: -148.1972;
RSS: 1107.5; dispersion: 1; iterations: 7;
rank: 14; max tolerance: 7.55e-12; convergence: TRUE.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
(Intercept) crim zn indus chas nox
3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01
rm age dis rad tax ptratio
3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01
black lstat
9.312e-03 -5.248e-01
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 36.459488 5.103459 7.144 3.283e-12 ***
crim -0.108011 0.032865 -3.287 1.087e-03 **
zn 0.046420 0.013727 3.382 7.781e-04 ***
indus 0.020559 0.061496 0.334 7.383e-01
chas 2.686734 0.861580 3.118 1.925e-03 **
nox -17.766611 3.819744 -4.651 4.246e-06 ***
rm 3.809865 0.417925 9.116 1.979e-18 ***
age 0.000692 0.013210 0.052 9.582e-01
dis -1.475567 0.199455 -7.398 6.013e-13 ***
rad 0.306049 0.066346 4.613 5.071e-06 ***
tax -0.012335 0.003761 -3.280 1.112e-03 **
ptratio -0.952747 0.130827 -7.283 1.309e-12 ***
black 0.009312 0.002686 3.467 5.729e-04 ***
lstat -0.524758 0.050715 -10.347 7.777e-23 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 4.745298 on 492 degrees of freedom;
observations: 506; R^2: 0.741; adjusted R^2: 0.734;
F-statistic: 108.1 on 13 and 492 df; p-value: 0.
Linear Regression Model of class 'speedlm':
Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)
Coefficients:
------------------------------------------------------------------
coef se t p.value
(Intercept) 1.667540 0.366239 4.553 6.670e-06 ***
crim 0.000303 0.002358 0.128 8.979e-01
zn 0.002303 0.000985 2.338 1.981e-02 *
indus -0.004025 0.004413 -0.912 3.621e-01
chas 0.033853 0.061829 0.548 5.843e-01
nox -0.724254 0.274116 -2.642 8.501e-03 **
rm 0.135798 0.029992 4.528 7.483e-06 ***
age -0.003107 0.000948 -3.278 1.121e-03 **
dis -0.074892 0.014313 -5.232 2.482e-07 ***
rad 0.016816 0.004761 3.532 4.515e-04 ***
tax -0.000672 0.000270 -2.490 1.311e-02 *
ptratio -0.049838 0.009389 -5.308 1.677e-07 ***
black 0.000147 0.000193 0.760 4.474e-01
lstat -0.019759 0.003639 -5.429 8.912e-08 ***
-------------------------------------------------------------------
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
---
Residual standard error: 0.340537 on 492 degrees of freedom;
observations: 506; R^2: 0.519; adjusted R^2: 0.506;
F-statistic: 40.86 on 13 and 492 df; p-value: 0.
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
══ Skipped tests (4) ═══════════════════════════════════════════════════════════
• empty test (4): , , ,
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────
Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found
── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────
Expected `min(sl_bad$SL.predict)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────
Expected `max(sl_bad$SL.predict)` <= 1.
Actual comparison: NA > 1.0
── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────
Expected `min(pred$pred)` >= 0.
Actual comparison: NA < 0.0
── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────
Expected `max(pred$pred)` <= 1.
Actual comparison: NA > 1.0
[ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-windows-x86_64
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