R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("party-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('party')
Loading required package: survival
Loading required package: splines
Loading required package: grid
Loading required package: modeltools
Loading required package: coin
Loading required package: mvtnorm
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "BinaryTree-class"
> 
> ### * BinaryTree-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BinaryTree Class
> ### Title: Class "BinaryTree"
> ### Aliases: BinaryTree-class weights weights-methods
> ###   weights,BinaryTree-method where where-methods where,BinaryTree-method
> ###   response response-methods response,BinaryTree-method nodes
> ###   nodes-methods nodes,BinaryTree,integer-method
> ###   nodes,BinaryTree,numeric-method treeresponse treeresponse-methods
> ###   treeresponse,BinaryTree-method
> ### Keywords: classes
> 
> ### ** Examples
> 
>   data(airquality)
>   airq <- subset(airquality, !is.na(Ozone))
>   airct <- ctree(Ozone ~ ., data = airq,   
+                  controls = ctree_control(maxsurrogate = 3))
> 
>   ### distribution of responses in the terminal nodes
>   plot(airq$Ozone ~ as.factor(where(airct)))
> 
>   ### get all terminal nodes from the tree
>   nodes(airct, unique(where(airct)))
[[1]]
5)*  weights = 48 

[[2]]
3)*  weights = 10 

[[3]]
6)*  weights = 21 

[[4]]
9)*  weights = 10 

[[5]]
8)*  weights = 27 

> 
>   ### extract weights and compute predictions
>   pmean <- sapply(weights(airct), function(w) weighted.mean(airq$Ozone, w))
> 
>   ### the same as
>   drop(Predict(airct))
  [1] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917
  [9] 55.60000 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917
 [17] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 31.14286
 [25] 55.60000 18.47917 31.14286 55.50000 55.50000 31.14286 18.47917 18.47917
 [33] 18.47917 18.47917 18.47917 82.77778 55.50000 31.14286 82.77778 55.50000
 [41] 82.77778 82.77778 82.77778 82.77778 18.47917 31.14286 31.14286 55.60000
 [49] 31.14286 82.77778 82.77778 55.50000 55.60000 82.77778 82.77778 31.14286
 [57] 55.50000 82.77778 82.77778 82.77778 31.14286 55.60000 31.14286 31.14286
 [65] 82.77778 82.77778 82.77778 82.77778 55.50000 82.77778 55.50000 31.14286
 [73] 31.14286 18.47917 55.60000 18.47917 31.14286 31.14286 18.47917 18.47917
 [81] 31.14286 55.60000 82.77778 55.50000 82.77778 82.77778 82.77778 82.77778
 [89] 82.77778 82.77778 82.77778 82.77778 55.50000 31.14286 31.14286 18.47917
 [97] 18.47917 31.14286 18.47917 55.60000 18.47917 18.47917 55.60000 18.47917
[105] 18.47917 18.47917 31.14286 18.47917 18.47917 31.14286 18.47917 18.47917
[113] 55.60000 18.47917 18.47917 18.47917
> 
>   ### or
>   unlist(treeresponse(airct))
  [1] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917
  [9] 55.60000 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917
 [17] 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 18.47917 31.14286
 [25] 55.60000 18.47917 31.14286 55.50000 55.50000 31.14286 18.47917 18.47917
 [33] 18.47917 18.47917 18.47917 82.77778 55.50000 31.14286 82.77778 55.50000
 [41] 82.77778 82.77778 82.77778 82.77778 18.47917 31.14286 31.14286 55.60000
 [49] 31.14286 82.77778 82.77778 55.50000 55.60000 82.77778 82.77778 31.14286
 [57] 55.50000 82.77778 82.77778 82.77778 31.14286 55.60000 31.14286 31.14286
 [65] 82.77778 82.77778 82.77778 82.77778 55.50000 82.77778 55.50000 31.14286
 [73] 31.14286 18.47917 55.60000 18.47917 31.14286 31.14286 18.47917 18.47917
 [81] 31.14286 55.60000 82.77778 55.50000 82.77778 82.77778 82.77778 82.77778
 [89] 82.77778 82.77778 82.77778 82.77778 55.50000 31.14286 31.14286 18.47917
 [97] 18.47917 31.14286 18.47917 55.60000 18.47917 18.47917 55.60000 18.47917
[105] 18.47917 18.47917 31.14286 18.47917 18.47917 31.14286 18.47917 18.47917
[113] 55.60000 18.47917 18.47917 18.47917
> 
>   ### don't use the mean but the median as prediction in each terminal node
>   pmedian <- sapply(weights(airct), function(w) 
+                                         median(airq$Ozone[rep(1:nrow(airq), w)]))
> 
>   plot(airq$Ozone, pmean, col = "red")
>   points(airq$Ozone, pmedian, col = "blue")
> 
> 
> 
> cleanEx(); ..nameEx <- "ctree"
> 
> ### * ctree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Conditional Trees
> ### Title: Conditional Trees
> ### Aliases: ctree conditionalTree
> ### Keywords: tree
> 
> ### ** Examples
> 
> 
>     ### regression
>     data(airquality)
>     airq <- subset(airquality, !is.na(Ozone))
>     airct <- ctree(Ozone ~ ., data = airq, 
+                    controls = ctree_control(maxsurrogate = 3))
>     airct

	 Conditional tree with 5 terminal nodes

Response:  Ozone 
Inputs:  Solar.R, Wind, Temp, Month, Day 
Number of observations:  116 

1) Temp <= 82; criterion = 1, statistic = 56.086
  2) Wind <= 6.9; criterion = 0.998, statistic = 12.969
    3)*  weights = 10 
  2) Wind > 6.9
    4) Temp <= 77; criterion = 0.997, statistic = 11.599
      5)*  weights = 48 
    4) Temp > 77
      6)*  weights = 21 
1) Temp > 82
  7) Wind <= 8.6; criterion = 0.997, statistic = 11.712
    8)*  weights = 27 
  7) Wind > 8.6
    9)*  weights = 10 
>     plot(airct)
>     mean((airq$Ozone - predict(airct))^2)
[1] 409.8803
> 
>     ### classification
>     irisct <- ctree(Species ~ .,data = iris)
>     irisct

	 Conditional tree with 4 terminal nodes

Response:  Species 
Inputs:  Sepal.Length, Sepal.Width, Petal.Length, Petal.Width 
Number of observations:  150 

1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264
  2)*  weights = 50 
1) Petal.Length > 1.9
  3) Petal.Width <= 1.7; criterion = 1, statistic = 67.894
    4) Petal.Length <= 4.8; criterion = 0.999, statistic = 13.865
      5)*  weights = 46 
    4) Petal.Length > 4.8
      6)*  weights = 8 
  3) Petal.Width > 1.7
    7)*  weights = 46 
>     plot(irisct)
>     table(predict(irisct), iris$Species)
            
             setosa versicolor virginica
  setosa         50          0         0
  versicolor      0         49         5
  virginica       0          1        45
> 
>     ### estimated class probabilities, a list
>     tr <- treeresponse(irisct, newdata = iris[1:10,])
> 
>     ### ordinal regression
>     data(mammoexp) 
>     mammoct <- ctree(ME ~ ., data = mammoexp) 
Warning: no admissible split found
>     plot(mammoct)
> 
>     ### estimated class probabilities
>     treeresponse(mammoct, newdata = mammoexp[1:10,])
[[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

[[6]]
[1] 0.3990385 0.3798077 0.2211538

[[7]]
[1] 0.3990385 0.3798077 0.2211538

[[8]]
[1] 0.3990385 0.3798077 0.2211538

[[9]]
[1] 0.84070796 0.05309735 0.10619469

[[10]]
[1] 0.3990385 0.3798077 0.2211538

> 
>     ### survival analysis
>     if (require(ipred)) {
+         data(GBSG2, package = "ipred")
+         GBSG2ct <- ctree(Surv(time, cens) ~ .,data = GBSG2)
+         plot(GBSG2ct)
+         treeresponse(GBSG2ct, newdata = GBSG2[1:2,])        
+     }
Loading required package: ipred
Loading required package: rpart
Loading required package: MASS
Loading required package: mlbench
Loading required package: nnet
Loading required package: class
[[1]]
Call: survival:::survfit(formula = resp, weights = w[[i]], subset = w[[i]] > 
    0)

      n  events  median 0.95LCL 0.95UCL 
    248      88    2093    1814     Inf 

[[2]]
Call: survival:::survfit(formula = resp, weights = w[[i]], subset = w[[i]] > 
    0)

      n  events  median 0.95LCL 0.95UCL 
    166      77    1701    1174    2018 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "ctree.memory"
> 
> ### * ctree.memory
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Memory Allocation
> ### Title: Memory Allocation
> ### Aliases: ctree_memory
> ### Keywords: misc
> 
> ### ** Examples
> 
> 
>     ### setup learning sample
>     data(airquality)
>     airq <- subset(airquality, !is.na(Ozone))
>     ls <- dpp(conditionalTree, Ozone ~ ., data = airq)
> 
>     ### setup memory and controls 
>     mem <- ctree_memory(ls)
>     ct <- ctree_control(teststattype = "maxabs")
> 
>     ### fit 50 trees on bootstrap samples
>     bs <- rmultinom(50, nrow(airq), rep(1, nrow(airq))/nrow(airq))
>     storage.mode(bs) <- "double"
>     cfit <- conditionalTree@fit
>     system.time(ens <- apply(bs, 2, function(w) cfit(ls, ct, weights = w, 
+                                                      fitmem = mem)))
[1] 0.86 0.01 0.86 0.00 0.00
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mammoexp"
> 
> ### * mammoexp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mammoexp
> ### Title: Mammography Experience Study
> ### Aliases: mammoexp
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
>   data(mammoexp)
>   ### fit tree with attached scores (but those are the default values anyway)
>   ctree(ME ~ .,data = mammoexp, 
+         scores = list(ME = 1:3, SYMPT = 1:4, DECT = 1:3))
Warning: no admissible split found

	 Conditional tree with 3 terminal nodes

Response:  ME 
Inputs:  SYMPT, PB, HIST, BSE, DECT 
Number of observations:  412 

1) SYMPT <= Agree; criterion = 1, statistic = 29.933
  2)*  weights = 113 
1) SYMPT > Agree
  3) PB <= 8; criterion = 0.988, statistic = 9.17
    4)*  weights = 208 
  3) PB > 8
    5)*  weights = 91 
> 
> 
> 
> cleanEx(); ..nameEx <- "panelfunctions"
> 
> ### * panelfunctions
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Panel Generating Functions
> ### Title: Visualization of Binary Regression Trees
> ### Aliases: node_inner node_terminal edge_simple node_surv node_barplot
> ###   node_boxplot node_hist node_density
> ### Keywords: hplot
> 
> ### ** Examples
> 
> 
>   data(airquality)
>   airq <- subset(airquality, !is.na(Ozone))
>   airct <- ctree(Ozone ~ ., data = airq)
> 
>   ### very simple; the mean is given in each terminal node
>   plot(airct, type = "simple")
> 
>   ### density estimates
>   plot(airct, terminal_panel = node_density)
>     
>   ### histograms 
>   plot(airct, terminal_panel = node_hist)
> 
>   ### boxplots
>   plot(airct, terminal_panel = node_boxplot, drop_terminal = TRUE)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.BinaryTree"
> 
> ### * plot.BinaryTree
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Plot BinaryTree
> ### Title: Visualization of Binary Regression Trees
> ### Aliases: plot.BinaryTree
> ### Keywords: hplot
> 
> ### ** Examples
> 
> 
>   data(airquality)
>   airq <- subset(airquality, !is.na(Ozone))
>   airct <- ctree(Ozone ~ ., data = airq)
> 
>   ### boxplots in each node
>   plot(airct, terminal_panel = node_boxplot, drop_terminal = TRUE)
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  10.87 0.24 11.24 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')