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("boost-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('boost')
Loading required package: rpart
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "adaboost"
> 
> ### * adaboost
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: adaboost
> ### Title: adaboost
> ### Aliases: adaboost
> ### Keywords: classif
> 
> ### ** Examples
> 
> data(leukemia, package = "boost")
> 
> ## Dividing the leukemia dataset into training and test data
> xlearn <- leukemia.x[c(1:20, 34:38),]
> ylearn <- leukemia.y[c(1:20, 34:38)]
> xtest  <- leukemia.x[21:33,]
> ytest  <- leukemia.y[21:33]
> 
> ## Classification with adaboost
> fit <- adaboost(xlearn, ylearn, xtest, presel=50, mfinal=20)
> summarize(fit, ytest)

Minimal mcr:   0 achieved after 8 boosting step(s)
Fixed mcr:     0 achieved after 20 boosting step(s)
> 
> 
> 
> cleanEx(); ..nameEx <- "bagboost"
> 
> ### * bagboost
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bagboost
> ### Title: bagboost
> ### Aliases: bagboost
> ### Keywords: classif
> 
> ### ** Examples
> 
> data(leukemia, package = "boost")
> 
> ## Dividing the leukemia dataset into training and test data
> xlearn <- leukemia.x[c(1:20, 34:38),]
> ylearn <- leukemia.y[c(1:20, 34:38)]
> xtest  <- leukemia.x[21:33,]
> ytest  <- leukemia.y[21:33]
> 
> ## Classification with bagboost
> fit <- bagboost(xlearn, ylearn, xtest, presel=50, mfinal=20, bag=5)
> summarize(fit, ytest)

Minimal mcr:   0 achieved after 2 boosting step(s)
Fixed mcr:     0 achieved after 20 boosting step(s)
> 
> 
> 
> cleanEx(); ..nameEx <- "l2boost"
> 
> ### * l2boost
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: l2boost
> ### Title: l2boost
> ### Aliases: l2boost
> ### Keywords: classif
> 
> ### ** Examples
> 
> data(leukemia, package = "boost")
> 
> ## Dividing the leukemia dataset into training and test data
> xlearn <- leukemia.x[c(1:20, 34:38),]
> ylearn <- leukemia.y[c(1:20, 34:38)]
> xtest  <- leukemia.x[21:33,]
> ytest  <- leukemia.y[21:33]
> 
> ## Classification with l2boost
> fit <- l2boost(xlearn, ylearn, xtest, presel=50, mfinal=20)
> summarize(fit, ytest)

Minimal mcr:   0.0769 achieved after 4 boosting step(s)
Fixed mcr:     0.0769 achieved after 20 boosting step(s)
> 
> 
> 
> cleanEx(); ..nameEx <- "leukemia"
> 
> ### * leukemia
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: leukemia
> ### Title: A part of the famous AML/ALL-leukemia dataset
> ### Aliases: leukemia leukemia.x leukemia.y leukemia.z
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(leukemia)
> str(leukemia.x)
 num [1:38, 1:250] 3.15 3.10 2.98 3.24 3.19 ...
> str(leukemia.y)
 num [1:38] 0 0 0 0 0 0 0 0 0 0 ...
> str(leukemia.z)
 num [1:38] 0 1 1 0 0 1 0 0 1 1 ...
> par(mfrow=c(1,2))
> plot(leukemia.x[,56], leukemia.y)
> plot(leukemia.x[,174],leukemia.z)
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "logitboost"
> 
> ### * logitboost
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: logitboost
> ### Title: logitboost
> ### Aliases: logitboost
> ### Keywords: classif
> 
> ### ** Examples
> 
> data(leukemia, package = "boost")
> 
> ## Dividing the leukemia dataset into training and test data
> xlearn <- leukemia.x[c(1:20, 34:38),]
> ylearn <- leukemia.y[c(1:20, 34:38)]
> xtest  <- leukemia.x[21:33,]
> ytest  <- leukemia.y[21:33]
> 
> ## Classification with logitboost
> fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20)
> summarize(fit, ytest)

Minimal mcr:   0 achieved after 9 boosting step(s)
Fixed mcr:     0 achieved after 20 boosting step(s)
> 
> 
> 
> cleanEx(); ..nameEx <- "simulator"
> 
> ### * simulator
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simulator
> ### Title: simulator
> ### Aliases: simulator
> ### Keywords: classif
> 
> ### ** Examples
> 
> set.seed(21)
> data(leukemia)
> 
> ## Simulation of gene expression data
> simu <- simulator(leukemia.x, leukemia.y, nos=200)
> 
> ## Defining training and test data
> xlearn <- simu$x[1:150,]
> ylearn <- simu$y[1:150]
> xtest  <- simu$x[151:200,]
> ytest  <- simu$y[151:200]
> 
> ## Classification with logitboost
> fit <- logitboost(xlearn, ylearn, xtest, mfinal=20, presel=50)
> summarize(fit, ytest)

Minimal mcr:   0 achieved after 3 boosting step(s)
Fixed mcr:     0 achieved after 20 boosting step(s)
> 
> 
> 
> cleanEx(); ..nameEx <- "summarize"
> 
> ### * summarize
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summarize
> ### Title: Summarize the output of classification with boosting functions
> ### Aliases: summarize
> ### Keywords: classif
> 
> ### ** Examples
> 
> data(leukemia, package = "boost")
> 
> ## Dividing the leukemia dataset into training and test data
> xlearn <- leukemia.x[c(1:20, 34:38),]
> ylearn <- leukemia.y[c(1:20, 34:38)]
> xtest  <- leukemia.x[21:33,]
> ytest  <- leukemia.y[21:33]
> 
> ## Classification with logitboost
> fit <- logitboost(xlearn, ylearn, xtest, presel=50, mfinal=20)
> summarize(fit, ytest)

Minimal mcr:   0 achieved after 9 boosting step(s)
Fixed mcr:     0 achieved after 20 boosting step(s)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  11.2 0.4 11.67 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')