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The psfmi
package includes the function
pool_performance
, to pool the performance measures of
logistic and Cox regression models. This vignette show you how to use
this function.
The performance of a logistic regression model across multiply imputed datasets can be obtained as follows.
perf <- pool_performance(data=lbpmilr, nimp=5, impvar="Impnr",
formula = Chronic ~ Gender + Pain + Tampascale +
Smoking + Function + Radiation + Age +
Duration + BMI,
cal.plot=TRUE, plot.method="mean",
groups_cal=10, model_type="binomial")
perf
#> $ROC_pooled
#> 95% Low C-statistic 95% Up
#> C-statistic (logit) 0.7878 0.8626 0.9139
#>
#> $coef_pooled
#> (Intercept) Gender Pain Tampascale Smoking Function
#> -5.951990403 -0.300998171 0.533421791 0.104519460 0.168974909 -0.063384729
#> Radiation Age Duration BMI
#> 0.256421438 -0.014809697 -0.001136425 0.006379084
#>
#> $R2_pooled
#> [1] 0.4882147
#>
#> $Brier_Scaled_pooled
#> [1] 0.3946362
#>
#> $nimp
#> [1] 5
#>
#> $HLtest_pooled
#> F_value P(>F) df1 df2
#> [1,] 1.090127 0.3779371 8 85.96895
#>
#> $model_type
#> [1] "binomial"
For a Cox regression model the following code can be used.
perf <- pool_performance(data=lbpmicox, nimp=5, impvar="Impnr",
formula = Surv(Time, Status) ~ Duration + Pain + Tampascale +
factor(Expect_cat) + Function + Radiation + Age ,
cal.plot=FALSE, model_type="survival")
perf
#> $concordance_pooled
#> 95% Low C-statistic 95% Up
#> C-statistic (logit) 0.5733 0.621 0.6664
#>
#> $coef_pooled
#> Duration Pain Tampascale factor(Expect_cat)2
#> -0.007680610 -0.085077440 -0.018125989 0.306105694
#> factor(Expect_cat)3 Function Radiation Age
#> 0.269403151 0.038106572 -0.037816020 -0.008903958
#>
#> $R2_pooled
#> [1] 0.09049936
#>
#> $nimp
#> [1] 5
#>
#> $model_type
#> [1] "survival"
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.