Last updated on 2026-02-08 13:50:01 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.2-24 | 15.42 | 266.61 | 282.03 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.2-25 | 10.90 | 205.75 | 216.65 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.2-25 | 25.00 | 460.32 | 485.32 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.2-25 | 24.00 | 463.85 | 487.85 | OK | |
| r-devel-windows-x86_64 | 1.2-24 | 19.00 | 375.00 | 394.00 | OK | |
| r-patched-linux-x86_64 | 1.2-24 | 15.03 | 254.76 | 269.79 | ERROR | |
| r-release-linux-x86_64 | 1.2-24 | 12.42 | 252.56 | 264.98 | ERROR | |
| r-release-macos-arm64 | 1.2-25 | 4.00 | 76.00 | 80.00 | OK | |
| r-release-macos-x86_64 | 1.2-25 | 12.00 | 717.00 | 729.00 | OK | |
| r-release-windows-x86_64 | 1.2-24 | 18.00 | 362.00 | 380.00 | OK | |
| r-oldrel-macos-arm64 | 1.2-25 | 3.00 | 83.00 | 86.00 | OK | |
| r-oldrel-macos-x86_64 | 1.2-25 | 13.00 | 763.00 | 776.00 | OK | |
| r-oldrel-windows-x86_64 | 1.2-24 | 24.00 | 470.00 | 494.00 | OK |
Version: 1.2-24
Check: tests
Result: ERROR
Running ‘bugfixes.R’ [6s/7s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘constparty.R’ [3s/4s]
Running ‘regtest-MIA.R’ [2s/2s]
Comparing ‘regtest-MIA.Rout’ to ‘regtest-MIA.Rout.save’ ... OK
Running ‘regtest-cforest.R’ [12s/18s]
Comparing ‘regtest-cforest.Rout’ to ‘regtest-cforest.Rout.save’ ... OK
Running ‘regtest-ctree.R’ [2s/3s]
Comparing ‘regtest-ctree.Rout’ to ‘regtest-ctree.Rout.save’ ... OK
Running ‘regtest-glmtree.R’ [37s/44s]
Comparing ‘regtest-glmtree.Rout’ to ‘regtest-glmtree.Rout.save’ ... OK
Running ‘regtest-honesty.R’ [2s/3s]
Running ‘regtest-lmtree.R’ [3s/4s]
Running ‘regtest-nmax.R’ [2s/3s]
Comparing ‘regtest-nmax.Rout’ to ‘regtest-nmax.Rout.save’ ... OK
Running ‘regtest-node.R’ [2s/2s]
Comparing ‘regtest-node.Rout’ to ‘regtest-node.Rout.save’ ... OK
Running ‘regtest-party-random.R’ [3s/3s]
Running ‘regtest-party.R’ [5s/4s]
Comparing ‘regtest-party.Rout’ to ‘regtest-party.Rout.save’ ... OK
Running ‘regtest-split.R’ [2s/2s]
Comparing ‘regtest-split.Rout’ to ‘regtest-split.Rout.save’ ... OK
Running ‘regtest-weights.R’ [2s/3s]
Comparing ‘regtest-weights.Rout’ to ‘regtest-weights.Rout.save’ ... OK
Running the tests in ‘tests/constparty.R’ failed.
Complete output:
> ### R code from vignette source 'constparty.Rnw'
>
> ### test here after removal of RWeka dependent code
>
> ###################################################
> ### code chunk number 1: setup
> ###################################################
> options(width = 70)
> library("partykit")
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
> library("XML") ### for pmmlTreeModel
> set.seed(290875)
>
>
> ###################################################
> ### code chunk number 2: Titanic
> ###################################################
> data("Titanic", package = "datasets")
> ttnc <- as.data.frame(Titanic)
> ttnc <- ttnc[rep(1:nrow(ttnc), ttnc$Freq), 1:4]
> names(ttnc)[2] <- "Gender"
>
>
> ###################################################
> ### code chunk number 3: rpart
> ###################################################
> library("rpart")
> (rp <- rpart(Survived ~ ., data = ttnc, model = TRUE))
n= 2201
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 2201 711 No (0.6769650 0.3230350)
2) Gender=Male 1731 367 No (0.7879838 0.2120162)
4) Age=Adult 1667 338 No (0.7972406 0.2027594) *
5) Age=Child 64 29 No (0.5468750 0.4531250)
10) Class=3rd 48 13 No (0.7291667 0.2708333) *
11) Class=1st,2nd 16 0 Yes (0.0000000 1.0000000) *
3) Gender=Female 470 126 Yes (0.2680851 0.7319149)
6) Class=3rd 196 90 No (0.5408163 0.4591837) *
7) Class=1st,2nd,Crew 274 20 Yes (0.0729927 0.9270073) *
>
>
> ###################################################
> ### code chunk number 4: rpart-party
> ###################################################
> (party_rp <- as.party(rp))
Model formula:
Survived ~ Class + Gender + Age
Fitted party:
[1] root
| [2] Gender in Male
| | [3] Age in Adult: No (n = 1667, err = 20.3%)
| | [4] Age in Child
| | | [5] Class in 3rd: No (n = 48, err = 27.1%)
| | | [6] Class in 1st, 2nd: Yes (n = 16, err = 0.0%)
| [7] Gender in Female
| | [8] Class in 3rd: No (n = 196, err = 45.9%)
| | [9] Class in 1st, 2nd, Crew: Yes (n = 274, err = 7.3%)
Number of inner nodes: 4
Number of terminal nodes: 5
>
>
> ###################################################
> ### code chunk number 5: rpart-plot-orig
> ###################################################
> plot(rp)
> text(rp)
>
>
> ###################################################
> ### code chunk number 6: rpart-plot
> ###################################################
> plot(party_rp)
>
>
> ###################################################
> ### code chunk number 7: rpart-pred
> ###################################################
> all.equal(predict(rp), predict(party_rp, type = "prob"),
+ check.attributes = FALSE)
[1] TRUE
>
>
> ###################################################
> ### code chunk number 8: rpart-fitted
> ###################################################
> str(fitted(party_rp))
'data.frame': 2201 obs. of 2 variables:
$ (fitted) : int 5 5 5 5 5 5 5 5 5 5 ...
$ (response): Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
>
>
> ###################################################
> ### code chunk number 9: rpart-prob
> ###################################################
> prop.table(do.call("table", fitted(party_rp)), 1)
(response)
(fitted) No Yes
3 0.7972406 0.2027594
5 0.7291667 0.2708333
6 0.0000000 1.0000000
8 0.5408163 0.4591837
9 0.0729927 0.9270073
>
>
> ###################################################
> ### code chunk number 10: J48
> ###################################################
> #if (require("RWeka")) {
> # j48 <- J48(Survived ~ ., data = ttnc)
> #} else {
> # j48 <- rpart(Survived ~ ., data = ttnc)
> #}
> #print(j48)
> #
> #
> ####################################################
> #### code chunk number 11: J48-party
> ####################################################
> #(party_j48 <- as.party(j48))
> #
> #
> ####################################################
> #### code chunk number 12: J48-plot
> ####################################################
> #plot(party_j48)
> #
> #
> ####################################################
> #### code chunk number 13: J48-pred
> ####################################################
> #all.equal(predict(j48, type = "prob"), predict(party_j48, type = "prob"),
> # check.attributes = FALSE)
>
>
> ###################################################
> ### code chunk number 14: PMML-Titantic
> ###################################################
> ttnc_pmml <- file.path(system.file("pmml", package = "partykit"),
+ "ttnc.pmml")
> (ttnc_quest <- pmmlTreeModel(ttnc_pmml))
Model formula:
Survived ~ Gender + Class + Age
Fitted party:
[1] root
| [2] Gender in Female
| | [3] Class in 3rd, Crew: Yes (n = 219, err = 49.8%)
| | [4] Class in 1st, 2nd
| | | [5] Class in 2nd: Yes (n = 106, err = 12.3%)
| | | [6] Class in 1st: Yes (n = 145, err = 2.8%)
| [7] Gender in Male
| | [8] Class in 3rd, 2nd, Crew
| | | [9] Age in Child: No (n = 59, err = 40.7%)
| | | [10] Age in Adult
| | | | [11] Class in 3rd, Crew
| | | | | [12] Class in Crew: No (n = 862, err = 22.3%)
| | | | | [13] Class in 3rd: No (n = 462, err = 16.2%)
| | | | [14] Class in 2nd: No (n = 168, err = 8.3%)
| | [15] Class in 1st: No (n = 180, err = 34.4%)
Number of inner nodes: 7
Number of terminal nodes: 8
>
>
> ###################################################
> ### code chunk number 15: PMML-Titanic-plot1
> ###################################################
> plot(ttnc_quest)
>
>
> ###################################################
> ### code chunk number 16: ttnc2-reorder
> ###################################################
> ttnc2 <- ttnc[, names(ttnc_quest$data)]
> for(n in names(ttnc2)) {
+ if(is.factor(ttnc2[[n]])) ttnc2[[n]] <- factor(
+ ttnc2[[n]], levels = levels(ttnc_quest$data[[n]]))
+ }
>
>
> ###################################################
> ### code chunk number 17: PMML-Titanic-augmentation
> ###################################################
> ttnc_quest2 <- party(ttnc_quest$node,
+ data = ttnc2,
+ fitted = data.frame(
+ "(fitted)" = predict(ttnc_quest, ttnc2, type = "node"),
+ "(response)" = ttnc2$Survived,
+ check.names = FALSE),
+ terms = terms(Survived ~ ., data = ttnc2)
+ )
> ttnc_quest2 <- as.constparty(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 18: PMML-Titanic-plot2
> ###################################################
> plot(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 19: PMML-write
> ###################################################
> library("pmml")
Error in library("pmml") : there is no package called 'pmml'
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 1.2-24
Check: re-building of vignette outputs
Result: NOTE
Note: skipping ‘constparty.Rnw’ due to unavailable dependencies: 'pmml'
Flavors: r-devel-linux-x86_64-debian-clang, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 1.2-24
Check: tests
Result: ERROR
Running ‘bugfixes.R’ [6s/7s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘constparty.R’ [3s/4s]
Running ‘regtest-MIA.R’ [2s/3s]
Comparing ‘regtest-MIA.Rout’ to ‘regtest-MIA.Rout.save’ ... OK
Running ‘regtest-cforest.R’ [12s/16s]
Comparing ‘regtest-cforest.Rout’ to ‘regtest-cforest.Rout.save’ ... OK
Running ‘regtest-ctree.R’ [2s/3s]
Comparing ‘regtest-ctree.Rout’ to ‘regtest-ctree.Rout.save’ ... OK
Running ‘regtest-glmtree.R’ [35s/51s]
Comparing ‘regtest-glmtree.Rout’ to ‘regtest-glmtree.Rout.save’ ... OK
Running ‘regtest-honesty.R’ [2s/2s]
Running ‘regtest-lmtree.R’ [3s/4s]
Running ‘regtest-nmax.R’ [2s/3s]
Comparing ‘regtest-nmax.Rout’ to ‘regtest-nmax.Rout.save’ ... OK
Running ‘regtest-node.R’ [2s/2s]
Comparing ‘regtest-node.Rout’ to ‘regtest-node.Rout.save’ ... OK
Running ‘regtest-party-random.R’ [2s/3s]
Running ‘regtest-party.R’ [5s/5s]
Comparing ‘regtest-party.Rout’ to ‘regtest-party.Rout.save’ ... OK
Running ‘regtest-split.R’ [2s/3s]
Comparing ‘regtest-split.Rout’ to ‘regtest-split.Rout.save’ ... OK
Running ‘regtest-weights.R’ [2s/2s]
Comparing ‘regtest-weights.Rout’ to ‘regtest-weights.Rout.save’ ... OK
Running the tests in ‘tests/constparty.R’ failed.
Complete output:
> ### R code from vignette source 'constparty.Rnw'
>
> ### test here after removal of RWeka dependent code
>
> ###################################################
> ### code chunk number 1: setup
> ###################################################
> options(width = 70)
> library("partykit")
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
> library("XML") ### for pmmlTreeModel
> set.seed(290875)
>
>
> ###################################################
> ### code chunk number 2: Titanic
> ###################################################
> data("Titanic", package = "datasets")
> ttnc <- as.data.frame(Titanic)
> ttnc <- ttnc[rep(1:nrow(ttnc), ttnc$Freq), 1:4]
> names(ttnc)[2] <- "Gender"
>
>
> ###################################################
> ### code chunk number 3: rpart
> ###################################################
> library("rpart")
> (rp <- rpart(Survived ~ ., data = ttnc, model = TRUE))
n= 2201
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 2201 711 No (0.6769650 0.3230350)
2) Gender=Male 1731 367 No (0.7879838 0.2120162)
4) Age=Adult 1667 338 No (0.7972406 0.2027594) *
5) Age=Child 64 29 No (0.5468750 0.4531250)
10) Class=3rd 48 13 No (0.7291667 0.2708333) *
11) Class=1st,2nd 16 0 Yes (0.0000000 1.0000000) *
3) Gender=Female 470 126 Yes (0.2680851 0.7319149)
6) Class=3rd 196 90 No (0.5408163 0.4591837) *
7) Class=1st,2nd,Crew 274 20 Yes (0.0729927 0.9270073) *
>
>
> ###################################################
> ### code chunk number 4: rpart-party
> ###################################################
> (party_rp <- as.party(rp))
Model formula:
Survived ~ Class + Gender + Age
Fitted party:
[1] root
| [2] Gender in Male
| | [3] Age in Adult: No (n = 1667, err = 20.3%)
| | [4] Age in Child
| | | [5] Class in 3rd: No (n = 48, err = 27.1%)
| | | [6] Class in 1st, 2nd: Yes (n = 16, err = 0.0%)
| [7] Gender in Female
| | [8] Class in 3rd: No (n = 196, err = 45.9%)
| | [9] Class in 1st, 2nd, Crew: Yes (n = 274, err = 7.3%)
Number of inner nodes: 4
Number of terminal nodes: 5
>
>
> ###################################################
> ### code chunk number 5: rpart-plot-orig
> ###################################################
> plot(rp)
> text(rp)
>
>
> ###################################################
> ### code chunk number 6: rpart-plot
> ###################################################
> plot(party_rp)
>
>
> ###################################################
> ### code chunk number 7: rpart-pred
> ###################################################
> all.equal(predict(rp), predict(party_rp, type = "prob"),
+ check.attributes = FALSE)
[1] TRUE
>
>
> ###################################################
> ### code chunk number 8: rpart-fitted
> ###################################################
> str(fitted(party_rp))
'data.frame': 2201 obs. of 2 variables:
$ (fitted) : int 5 5 5 5 5 5 5 5 5 5 ...
$ (response): Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
>
>
> ###################################################
> ### code chunk number 9: rpart-prob
> ###################################################
> prop.table(do.call("table", fitted(party_rp)), 1)
(response)
(fitted) No Yes
3 0.7972406 0.2027594
5 0.7291667 0.2708333
6 0.0000000 1.0000000
8 0.5408163 0.4591837
9 0.0729927 0.9270073
>
>
> ###################################################
> ### code chunk number 10: J48
> ###################################################
> #if (require("RWeka")) {
> # j48 <- J48(Survived ~ ., data = ttnc)
> #} else {
> # j48 <- rpart(Survived ~ ., data = ttnc)
> #}
> #print(j48)
> #
> #
> ####################################################
> #### code chunk number 11: J48-party
> ####################################################
> #(party_j48 <- as.party(j48))
> #
> #
> ####################################################
> #### code chunk number 12: J48-plot
> ####################################################
> #plot(party_j48)
> #
> #
> ####################################################
> #### code chunk number 13: J48-pred
> ####################################################
> #all.equal(predict(j48, type = "prob"), predict(party_j48, type = "prob"),
> # check.attributes = FALSE)
>
>
> ###################################################
> ### code chunk number 14: PMML-Titantic
> ###################################################
> ttnc_pmml <- file.path(system.file("pmml", package = "partykit"),
+ "ttnc.pmml")
> (ttnc_quest <- pmmlTreeModel(ttnc_pmml))
Model formula:
Survived ~ Gender + Class + Age
Fitted party:
[1] root
| [2] Gender in Female
| | [3] Class in 3rd, Crew: Yes (n = 219, err = 49.8%)
| | [4] Class in 1st, 2nd
| | | [5] Class in 2nd: Yes (n = 106, err = 12.3%)
| | | [6] Class in 1st: Yes (n = 145, err = 2.8%)
| [7] Gender in Male
| | [8] Class in 3rd, 2nd, Crew
| | | [9] Age in Child: No (n = 59, err = 40.7%)
| | | [10] Age in Adult
| | | | [11] Class in 3rd, Crew
| | | | | [12] Class in Crew: No (n = 862, err = 22.3%)
| | | | | [13] Class in 3rd: No (n = 462, err = 16.2%)
| | | | [14] Class in 2nd: No (n = 168, err = 8.3%)
| | [15] Class in 1st: No (n = 180, err = 34.4%)
Number of inner nodes: 7
Number of terminal nodes: 8
>
>
> ###################################################
> ### code chunk number 15: PMML-Titanic-plot1
> ###################################################
> plot(ttnc_quest)
>
>
> ###################################################
> ### code chunk number 16: ttnc2-reorder
> ###################################################
> ttnc2 <- ttnc[, names(ttnc_quest$data)]
> for(n in names(ttnc2)) {
+ if(is.factor(ttnc2[[n]])) ttnc2[[n]] <- factor(
+ ttnc2[[n]], levels = levels(ttnc_quest$data[[n]]))
+ }
>
>
> ###################################################
> ### code chunk number 17: PMML-Titanic-augmentation
> ###################################################
> ttnc_quest2 <- party(ttnc_quest$node,
+ data = ttnc2,
+ fitted = data.frame(
+ "(fitted)" = predict(ttnc_quest, ttnc2, type = "node"),
+ "(response)" = ttnc2$Survived,
+ check.names = FALSE),
+ terms = terms(Survived ~ ., data = ttnc2)
+ )
> ttnc_quest2 <- as.constparty(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 18: PMML-Titanic-plot2
> ###################################################
> plot(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 19: PMML-write
> ###################################################
> library("pmml")
Error in library("pmml") : there is no package called 'pmml'
Execution halted
Flavor: r-patched-linux-x86_64
Version: 1.2-24
Check: tests
Result: ERROR
Running ‘bugfixes.R’ [5s/7s]
Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ... OK
Running ‘constparty.R’ [3s/4s]
Running ‘regtest-MIA.R’ [2s/3s]
Comparing ‘regtest-MIA.Rout’ to ‘regtest-MIA.Rout.save’ ... OK
Running ‘regtest-cforest.R’ [12s/13s]
Comparing ‘regtest-cforest.Rout’ to ‘regtest-cforest.Rout.save’ ... OK
Running ‘regtest-ctree.R’ [2s/3s]
Comparing ‘regtest-ctree.Rout’ to ‘regtest-ctree.Rout.save’ ... OK
Running ‘regtest-glmtree.R’ [35s/41s]
Comparing ‘regtest-glmtree.Rout’ to ‘regtest-glmtree.Rout.save’ ... OK
Running ‘regtest-honesty.R’ [2s/4s]
Running ‘regtest-lmtree.R’ [3s/4s]
Running ‘regtest-nmax.R’ [2s/3s]
Comparing ‘regtest-nmax.Rout’ to ‘regtest-nmax.Rout.save’ ... OK
Running ‘regtest-node.R’ [2s/3s]
Comparing ‘regtest-node.Rout’ to ‘regtest-node.Rout.save’ ... OK
Running ‘regtest-party-random.R’ [2s/3s]
Running ‘regtest-party.R’ [5s/5s]
Comparing ‘regtest-party.Rout’ to ‘regtest-party.Rout.save’ ... OK
Running ‘regtest-split.R’ [2s/2s]
Comparing ‘regtest-split.Rout’ to ‘regtest-split.Rout.save’ ... OK
Running ‘regtest-weights.R’ [2s/3s]
Comparing ‘regtest-weights.Rout’ to ‘regtest-weights.Rout.save’ ... OK
Running the tests in ‘tests/constparty.R’ failed.
Complete output:
> ### R code from vignette source 'constparty.Rnw'
>
> ### test here after removal of RWeka dependent code
>
> ###################################################
> ### code chunk number 1: setup
> ###################################################
> options(width = 70)
> library("partykit")
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
> library("XML") ### for pmmlTreeModel
> set.seed(290875)
>
>
> ###################################################
> ### code chunk number 2: Titanic
> ###################################################
> data("Titanic", package = "datasets")
> ttnc <- as.data.frame(Titanic)
> ttnc <- ttnc[rep(1:nrow(ttnc), ttnc$Freq), 1:4]
> names(ttnc)[2] <- "Gender"
>
>
> ###################################################
> ### code chunk number 3: rpart
> ###################################################
> library("rpart")
> (rp <- rpart(Survived ~ ., data = ttnc, model = TRUE))
n= 2201
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 2201 711 No (0.6769650 0.3230350)
2) Gender=Male 1731 367 No (0.7879838 0.2120162)
4) Age=Adult 1667 338 No (0.7972406 0.2027594) *
5) Age=Child 64 29 No (0.5468750 0.4531250)
10) Class=3rd 48 13 No (0.7291667 0.2708333) *
11) Class=1st,2nd 16 0 Yes (0.0000000 1.0000000) *
3) Gender=Female 470 126 Yes (0.2680851 0.7319149)
6) Class=3rd 196 90 No (0.5408163 0.4591837) *
7) Class=1st,2nd,Crew 274 20 Yes (0.0729927 0.9270073) *
>
>
> ###################################################
> ### code chunk number 4: rpart-party
> ###################################################
> (party_rp <- as.party(rp))
Model formula:
Survived ~ Class + Gender + Age
Fitted party:
[1] root
| [2] Gender in Male
| | [3] Age in Adult: No (n = 1667, err = 20.3%)
| | [4] Age in Child
| | | [5] Class in 3rd: No (n = 48, err = 27.1%)
| | | [6] Class in 1st, 2nd: Yes (n = 16, err = 0.0%)
| [7] Gender in Female
| | [8] Class in 3rd: No (n = 196, err = 45.9%)
| | [9] Class in 1st, 2nd, Crew: Yes (n = 274, err = 7.3%)
Number of inner nodes: 4
Number of terminal nodes: 5
>
>
> ###################################################
> ### code chunk number 5: rpart-plot-orig
> ###################################################
> plot(rp)
> text(rp)
>
>
> ###################################################
> ### code chunk number 6: rpart-plot
> ###################################################
> plot(party_rp)
>
>
> ###################################################
> ### code chunk number 7: rpart-pred
> ###################################################
> all.equal(predict(rp), predict(party_rp, type = "prob"),
+ check.attributes = FALSE)
[1] TRUE
>
>
> ###################################################
> ### code chunk number 8: rpart-fitted
> ###################################################
> str(fitted(party_rp))
'data.frame': 2201 obs. of 2 variables:
$ (fitted) : int 5 5 5 5 5 5 5 5 5 5 ...
$ (response): Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
>
>
> ###################################################
> ### code chunk number 9: rpart-prob
> ###################################################
> prop.table(do.call("table", fitted(party_rp)), 1)
(response)
(fitted) No Yes
3 0.7972406 0.2027594
5 0.7291667 0.2708333
6 0.0000000 1.0000000
8 0.5408163 0.4591837
9 0.0729927 0.9270073
>
>
> ###################################################
> ### code chunk number 10: J48
> ###################################################
> #if (require("RWeka")) {
> # j48 <- J48(Survived ~ ., data = ttnc)
> #} else {
> # j48 <- rpart(Survived ~ ., data = ttnc)
> #}
> #print(j48)
> #
> #
> ####################################################
> #### code chunk number 11: J48-party
> ####################################################
> #(party_j48 <- as.party(j48))
> #
> #
> ####################################################
> #### code chunk number 12: J48-plot
> ####################################################
> #plot(party_j48)
> #
> #
> ####################################################
> #### code chunk number 13: J48-pred
> ####################################################
> #all.equal(predict(j48, type = "prob"), predict(party_j48, type = "prob"),
> # check.attributes = FALSE)
>
>
> ###################################################
> ### code chunk number 14: PMML-Titantic
> ###################################################
> ttnc_pmml <- file.path(system.file("pmml", package = "partykit"),
+ "ttnc.pmml")
> (ttnc_quest <- pmmlTreeModel(ttnc_pmml))
Model formula:
Survived ~ Gender + Class + Age
Fitted party:
[1] root
| [2] Gender in Female
| | [3] Class in 3rd, Crew: Yes (n = 219, err = 49.8%)
| | [4] Class in 1st, 2nd
| | | [5] Class in 2nd: Yes (n = 106, err = 12.3%)
| | | [6] Class in 1st: Yes (n = 145, err = 2.8%)
| [7] Gender in Male
| | [8] Class in 3rd, 2nd, Crew
| | | [9] Age in Child: No (n = 59, err = 40.7%)
| | | [10] Age in Adult
| | | | [11] Class in 3rd, Crew
| | | | | [12] Class in Crew: No (n = 862, err = 22.3%)
| | | | | [13] Class in 3rd: No (n = 462, err = 16.2%)
| | | | [14] Class in 2nd: No (n = 168, err = 8.3%)
| | [15] Class in 1st: No (n = 180, err = 34.4%)
Number of inner nodes: 7
Number of terminal nodes: 8
>
>
> ###################################################
> ### code chunk number 15: PMML-Titanic-plot1
> ###################################################
> plot(ttnc_quest)
>
>
> ###################################################
> ### code chunk number 16: ttnc2-reorder
> ###################################################
> ttnc2 <- ttnc[, names(ttnc_quest$data)]
> for(n in names(ttnc2)) {
+ if(is.factor(ttnc2[[n]])) ttnc2[[n]] <- factor(
+ ttnc2[[n]], levels = levels(ttnc_quest$data[[n]]))
+ }
>
>
> ###################################################
> ### code chunk number 17: PMML-Titanic-augmentation
> ###################################################
> ttnc_quest2 <- party(ttnc_quest$node,
+ data = ttnc2,
+ fitted = data.frame(
+ "(fitted)" = predict(ttnc_quest, ttnc2, type = "node"),
+ "(response)" = ttnc2$Survived,
+ check.names = FALSE),
+ terms = terms(Survived ~ ., data = ttnc2)
+ )
> ttnc_quest2 <- as.constparty(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 18: PMML-Titanic-plot2
> ###################################################
> plot(ttnc_quest2)
>
>
> ###################################################
> ### code chunk number 19: PMML-write
> ###################################################
> library("pmml")
Error in library("pmml") : there is no package called 'pmml'
Execution halted
Flavor: r-release-linux-x86_64
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