296 lines (260 with data), 10.7 kB
R Under development (unstable) (2014-06-29 r66051) -- "Unsuffered Consequences"
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>
> 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
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