R Under development (unstable) (2014-06-29 r66051) -- "Unsuffered Consequences"

Copyright (C) 2014 The R Foundation for Statistical Computing

Platform: x86_64-unknown-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.

You are welcome to redistribute it under certain conditions.

Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.

Type 'contributors()' for more information and

'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or

'help.start()' for an HTML browser interface to help.

Type 'q()' to quit R.

> 

> set.seed(290875)

> library("party")

Loading required package: grid

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich

Loading required package: strucchange

Loading required package: modeltools

Loading required package: stats4

> if (!require("TH.data"))

+     stop("cannot load package TH.data")

Loading required package: TH.data

> if (!require("coin"))

+     stop("cannot load package coin")

Loading required package: coin

Loading required package: survival

Loading required package: splines

> 

> ### get rid of the NAMESPACE

> attach(asNamespace("party"))

The following objects are masked from package:party:

    cforest, cforest_classical, cforest_control, cforest_unbiased,

    conditionalTree, ctree, ctree_control, ctree_memory, edge_simple,

    mob, mob_control, node_barplot, node_bivplot, node_boxplot,

    node_density, node_hist, node_inner, node_scatterplot, node_surv,

    node_terminal, proximity, ptrafo, reweight, sctest.mob, varimp,

    varimpAUC

> 

> gtctrl <- new("GlobalTestControl")

> tlev <- levels(gtctrl@testtype)

> 

> data(GlaucomaM, package = "TH.data")

> gtree <- ctree(Class ~ ., data = GlaucomaM)

> tree <- gtree@tree

> stopifnot(isequal(tree[[5]][[3]], 0.059))

> predict(gtree)

  [1] normal   normal   normal   normal   normal   normal   normal   normal  

  [9] normal   normal   normal   glaucoma normal   normal   normal   normal  

 [17] normal   normal   normal   normal   normal   normal   normal   normal  

 [25] normal   normal   normal   normal   normal   normal   normal   normal  

 [33] normal   normal   glaucoma normal   normal   normal   normal   normal  

 [41] normal   normal   glaucoma normal   normal   normal   normal   normal  

 [49] normal   normal   normal   normal   normal   normal   normal   normal  

 [57] normal   normal   normal   normal   normal   normal   normal   normal  

 [65] normal   normal   normal   normal   normal   glaucoma normal   normal  

 [73] normal   normal   normal   normal   normal   normal   normal   normal  

 [81] glaucoma normal   normal   normal   normal   normal   normal   normal  

 [89] normal   normal   normal   normal   normal   normal   normal   normal  

 [97] normal   normal   glaucoma glaucoma glaucoma glaucoma normal   normal  

[105] normal   normal   normal   glaucoma glaucoma normal   glaucoma glaucoma

[113] glaucoma glaucoma glaucoma glaucoma glaucoma normal   normal   glaucoma

[121] glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma normal   glaucoma

[129] normal   glaucoma normal   glaucoma glaucoma glaucoma glaucoma glaucoma

[137] glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma glaucoma

[145] glaucoma glaucoma normal   glaucoma glaucoma glaucoma glaucoma normal  

[153] glaucoma glaucoma glaucoma glaucoma normal   glaucoma glaucoma glaucoma

[161] glaucoma glaucoma normal   normal   glaucoma glaucoma normal   glaucoma

[169] glaucoma glaucoma glaucoma glaucoma normal   glaucoma glaucoma glaucoma

[177] normal   glaucoma normal   glaucoma glaucoma glaucoma normal   glaucoma

[185] glaucoma glaucoma normal   glaucoma glaucoma normal   glaucoma normal  

[193] glaucoma glaucoma glaucoma glaucoma

Levels: glaucoma normal

> 

> # print(tree)

> 

> stump <- ctree(Class ~ ., data = GlaucomaM, 

+                control = ctree_control(stump = TRUE))

> print(stump)

     Conditional inference tree with 2 terminal nodes

Response:  Class 

Inputs:  ag, at, as, an, ai, eag, eat, eas, ean, eai, abrg, abrt, abrs, abrn, abri, hic, mhcg, mhct, mhcs, mhcn, mhci, phcg, phct, phcs, phcn, phci, hvc, vbsg, vbst, vbss, vbsn, vbsi, vasg, vast, vass, vasn, vasi, vbrg, vbrt, vbrs, vbrn, vbri, varg, vart, vars, varn, vari, mdg, mdt, mds, mdn, mdi, tmg, tmt, tms, tmn, tmi, mr, rnf, mdic, emd, mv 

Number of observations:  196 

1) vari <= 0.059; criterion = 1, statistic = 71.475

  2)*  weights = 87 

1) vari > 0.059

  3)*  weights = 109 

> 

> data(treepipit, package = "coin")

> 

> tr <- ctree(counts ~ ., data = treepipit)

> tr

     Conditional inference tree with 2 terminal nodes

Response:  counts 

Inputs:  age, coverstorey, coverregen, meanregen, coniferous, deadtree, cbpiles, ivytree, fdist 

Number of observations:  86 

1) coverstorey <= 40; criterion = 0.998, statistic = 13.678

  2)*  weights = 24 

1) coverstorey > 40

  3)*  weights = 62 

> plot(tr)

> 

> 

> data(GlaucomaM, package = "TH.data")

> 

> tr <- ctree(Class ~ ., data = GlaucomaM)

> tr

     Conditional inference tree with 4 terminal nodes

Response:  Class 

Inputs:  ag, at, as, an, ai, eag, eat, eas, ean, eai, abrg, abrt, abrs, abrn, abri, hic, mhcg, mhct, mhcs, mhcn, mhci, phcg, phct, phcs, phcn, phci, hvc, vbsg, vbst, vbss, vbsn, vbsi, vasg, vast, vass, vasn, vasi, vbrg, vbrt, vbrs, vbrn, vbri, varg, vart, vars, varn, vari, mdg, mdt, mds, mdn, mdi, tmg, tmt, tms, tmn, tmi, mr, rnf, mdic, emd, mv 

Number of observations:  196 

1) vari <= 0.059; criterion = 1, statistic = 71.475

  2) vasg <= 0.066; criterion = 1, statistic = 29.265

    3)*  weights = 79 

  2) vasg > 0.066

    4)*  weights = 8 

1) vari > 0.059

  5) tms <= -0.066; criterion = 0.951, statistic = 11.221

    6)*  weights = 65 

  5) tms > -0.066

    7)*  weights = 44 

> plot(tr)

> 

> data(GBSG2, package = "TH.data")  

> 

> GBSG2tree <- ctree(Surv(time, cens) ~ ., data = GBSG2)

> GBSG2tree

     Conditional inference tree with 4 terminal nodes

Response:  Surv(time, cens) 

Inputs:  horTh, age, menostat, tsize, tgrade, pnodes, progrec, estrec 

Number of observations:  686 

1) pnodes <= 3; criterion = 1, statistic = 56.156

  2) horTh == {no}; criterion = 0.965, statistic = 8.113

    3)*  weights = 248 

  2) horTh == {yes}

    4)*  weights = 128 

1) pnodes > 3

  5) progrec <= 20; criterion = 0.999, statistic = 14.941

    6)*  weights = 144 

  5) progrec > 20

    7)*  weights = 166 

> plot(GBSG2tree)

> plot(GBSG2tree, terminal_panel = node_surv(GBSG2tree))

> survfit(Surv(time, cens) ~ as.factor(GBSG2tree@where), data = GBSG2)

Call: survfit(formula = Surv(time, cens) ~ as.factor(GBSG2tree@where), 

    data = GBSG2)

                             records n.max n.start events median 0.95LCL

as.factor(GBSG2tree@where)=3     248   248     248     88   2093    1814

as.factor(GBSG2tree@where)=4     128   128     128     31     NA    2372

as.factor(GBSG2tree@where)=6     144   144     144    103    624     525

as.factor(GBSG2tree@where)=7     166   166     166     77   1701    1174

                             0.95UCL

as.factor(GBSG2tree@where)=3      NA

as.factor(GBSG2tree@where)=4      NA

as.factor(GBSG2tree@where)=6     797

as.factor(GBSG2tree@where)=7    2018

> names(GBSG2)

 [1] "horTh"    "age"      "menostat" "tsize"    "tgrade"   "pnodes"  

 [7] "progrec"  "estrec"   "time"     "cens"    

> 

> tr <- ctree(Surv(time, cens) ~ ., data = GBSG2, 

+             control = ctree_control(teststat = "max", 

+                                     testtype = "Univariate"))

There were 18 warnings (use warnings() to see them)

> tr

     Conditional inference tree with 10 terminal nodes

Response:  Surv(time, cens) 

Inputs:  horTh, age, menostat, tsize, tgrade, pnodes, progrec, estrec 

Number of observations:  686 

1) pnodes <= 3; criterion = 1, statistic = 7.494

  2) horTh == {no}; criterion = 0.996, statistic = 2.848

    3) menostat == {Post}; criterion = 0.978, statistic = 2.286

      4)*  weights = 112 

    3) menostat == {Pre}

      5) age <= 37; criterion = 1, statistic = 3.858

        6)*  weights = 21 

      5) age > 37

        7)*  weights = 115 

  2) horTh == {yes}

    8) progrec <= 74; criterion = 0.975, statistic = 2.241

      9)*  weights = 73 

    8) progrec > 74

      10)*  weights = 55 

1) pnodes > 3

  11) progrec <= 20; criterion = 1, statistic = 3.865

    12) pnodes <= 9; criterion = 0.991, statistic = 2.612

      13)*  weights = 87 

    12) pnodes > 9

      14)*  weights = 57 

  11) progrec > 20

    15) horTh == {no}; criterion = 0.976, statistic = 2.251

      16)*  weights = 101 

    15) horTh == {yes}

      17) menostat == {Post}; criterion = 0.965, statistic = 2.105

        18)*  weights = 45 

      17) menostat == {Pre}

        19)*  weights = 20 

> plot(tr)

> 

> data("mammoexp", package = "TH.data")

> attr(mammoexp$ME, "scores") <- 1:3   

> attr(mammoexp$SYMPT, "scores") <- 1:4

> attr(mammoexp$DECT, "scores") <- 1:3 

> names(mammoexp)[names(mammoexp) == "SYMPT"] <- "symptoms"

> names(mammoexp)[names(mammoexp) == "PB"] <- "benefit"

> 

> names(mammoexp)

[1] "ME"       "symptoms" "benefit"  "HIST"     "BSE"      "DECT"    

> tr <- ctree(ME ~ ., data = mammoexp)

> tr

     Conditional inference tree with 3 terminal nodes

Response:  ME 

Inputs:  symptoms, benefit, HIST, BSE, DECT 

Number of observations:  412 

1) symptoms <= Agree; criterion = 1, statistic = 29.933

  2)*  weights = 113 

1) symptoms > Agree

  3) benefit <= 8; criterion = 0.988, statistic = 9.17

    4)*  weights = 208 

  3) benefit > 8

    5)*  weights = 91 

> plot(tr)

> 

> treeresponse(tr, newdata = mammoexp[1:5,])

[[1]]

[1] 0.3990385 0.3798077 0.2211538

[[2]]

[1] 0.84070796 0.05309735 0.10619469

[[3]]

[1] 0.3990385 0.3798077 0.2211538

[[4]]

[1] 0.6153846 0.2087912 0.1758242

[[5]]

[1] 0.3990385 0.3798077 0.2211538

> 

> ### check different user interfaces

> data("iris")

> x <- as.matrix(iris[,colnames(iris) != "Species"])

> y <- iris[,"Species"]

> newx <- x

> 

> ls <- LearningSample(x, y)

> p1 <- unlist(treeresponse(ctree(Species ~ ., data = iris), newdata = as.data.frame(newx)))

> p2 <- unlist(treeresponse(ctreefit(ls, control = ctree_control()), newdata = as.matrix(newx)))

> stopifnot(identical(max(abs(p1 - p2)), 0))

> 

> set.seed(29)

> p1 <- unlist(treeresponse(cforestfit(ls, control = cforest_unbiased(mtry = 1)), newdata = as.matrix(newx)))

> set.seed(29)

> p2 <- unlist(treeresponse(cforest(Species ~ ., data = iris, control = cforest_unbiased(mtry = 1)), 

+              newdata = as.data.frame(newx)))

> stopifnot(identical(max(abs(p1 - p2)), 0))

> 

> proc.time()

   user  system elapsed 

  2.492   0.112   2.604