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("superpc-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('superpc')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "superpc.cv"
> 
> ### * superpc.cv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.cv
> ### Title: Cross-validation for supervised principal components
> ### Aliases: superpc.cv
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> a<- superpc.train(data, type="survival")
> aa<-superpc.cv(a,data)

fold= 1
1Loading required package: survival
Loading required package: splines
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fold= 2
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fold= 10
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> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.decorrelate"
> 
> ### * superpc.decorrelate
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.decorrelate
> ### Title: Decorrelate features with respect to competing predictors
> ### Aliases: superpc.decorrelate
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*20),ncol=20)
> y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> censoring.status<- sample(c(rep(1,17),rep(0,3)))
> censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
> competing.predictors=list(pred1=rnorm(20), pred2=as.factor(sample(c(1,2),replace=TRUE,size=20)))
> featurenames <- paste("feature",as.character(1:1000),sep="")
> 
> # decorrelate x
> foo<-superpc.decorrelate(x,competing.predictors)
> 
> xnew<-t(foo$res)
> 
> # now use xnew in superpc
> 
> data<-list(x=xnew,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> # etc. Remember to decorrelate test data in the same way, before making predictions.
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.fit.to.outcome"
> 
> ### * superpc.fit.to.outcome
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.fit.to.outcome
> ### Title: Fit predictive model using outcome of supervised principal
> ###   components
> ### Aliases: superpc.fit.to.outcome
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*20),ncol=20)
> y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> censoring.status<- sample(c(rep(1,17),rep(0,3)))
> censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
> 
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> 
> a<- superpc.train(data, type="survival")
> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> superpc.fit.to.outcome(a, data, fit$v.pred)
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
Call:
coxph(formula = Surv(y, censoring.status) ~ ., data = temp.list, 
    control = coxph.control(iter.max = iter.max))

  n= 20 
        coef exp(coef) se(coef)    z     p
score.1 6.13       460     1.58 3.89 1e-04

        exp(coef) exp(-coef) lower .95 upper .95
score.1       460    0.00217      20.9     10128

Rsquare= 0.834   (max possible= 0.977 )
Likelihood ratio test= 35.9  on 1 df,   p=2.04e-09
Wald test            = 15.1  on 1 df,   p=0.000101
Score (logrank) test = 21.7  on 1 df,   p=3.18e-06

$results
Call:
coxph(formula = Surv(y, censoring.status) ~ ., data = temp.list, 
    control = coxph.control(iter.max = iter.max))


        coef exp(coef) se(coef)    z     p
score.1 6.13       460     1.58 3.89 1e-04

Likelihood ratio test=35.9  on 1 df, p=2.04e-09  n= 20 

$teststat.table
     test statistic df  p-value
[1,]           35.9  1 2.04e-09

$coeftable
        coef exp(coef) se(coef)    z     p
score.1 6.13       460     1.58 3.89 1e-04

> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.listfeatures"
> 
> ### * superpc.listfeatures
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.listfeatures
> ### Title: Return a list of the important predictors
> ### Aliases: superpc.listfeatures
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> #generate some data
> 
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> fit.red<- superpc.predict.red(a,data, data.test, .6)
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> superpc.listfeatures(data, a,  fit.red, num.features=20)
      Importance-score Raw-score Name        
 [1,] "8.502"          "1.816"   "feature632"
 [2,] "-7.935"         "-2.131"  "feature59" 
 [3,] "-7.367"         "-1.884"  "feature60" 
 [4,] "-7.17"          "-1.769"  "feature732"
 [5,] "6.43"           "1.801"   "feature322"
 [6,] "-6.294"         "-1.081"  "feature294"
 [7,] "6.265"          "1.262"   "feature216"
 [8,] "5.981"          "1.492"   "feature2"  
 [9,] "5.843"          "1.31"    "feature441"
[10,] "5.642"          "1.446"   "feature874"
[11,] "5.624"          "1.091"   "feature298"
[12,] "5.368"          "1.249"   "feature133"
[13,] "-5.248"         "-1.071"  "feature783"
[14,] "5.223"          "1.23"    "feature291"
[15,] "-5.098"         "-0.985"  "feature134"
[16,] "5.097"          "1.228"   "feature218"
[17,] "-5.076"         "-0.977"  "feature425"
[18,] "4.916"          "0.928"   "feature847"
[19,] "-4.907"         "-1.255"  "feature861"
[20,] "4.891"          "1.205"   "feature507"
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.lrtest.curv"
> 
> ### * superpc.lrtest.curv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.lrtest.curv
> ### Title: Compute values of likelihood ratio test from supervised
> ###   principal components fit
> ### Aliases: superpc.lrtest.curv
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*20),ncol=20)
> y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> censoring.status<- sample(c(rep(1,17),rep(0,3)))
> censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> aa<- superpc.lrtest.curv(a, data, data.test)

1234Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
5Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
6Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
7Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
8Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
9Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
10Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
11Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
12Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
13Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
14Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
15Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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	 Ran out of iterations and did not converge
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> superpc.plot.lrtest(aa)
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.plot.lrtest"
> 
> ### * superpc.plot.lrtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.plot.lrtest
> ### Title: Plot likelhiood ratio test statistics
> ### Aliases: superpc.plot.lrtest
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> 
> a<- superpc.train(data, type="survival")
> aa<-superpc.cv(a, data)

fold= 1
1Loading required package: survival
Loading required package: splines
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> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> bb<-superpc.lrtest.curv(a,data,data.test)

12345Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
6Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
7Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
8Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
9Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
10Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
11Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
12Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
13Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
14Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
151617181920
> 
> superpc.plot.lrtest(bb)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.plotcv"
> 
> ### * superpc.plotcv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.plotcv
> ### Title: Plot output from superpc.cv
> ### Aliases: superpc.plotcv
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> a<- superpc.train(data, type="survival")
> aa<-superpc.cv(a,data)

fold= 1
1Loading required package: survival
Loading required package: splines
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fold= 2
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fold= 3
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> 
> superpc.plotcv(aa)
[1] TRUE
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.plotred.lrtest"
> 
> ### * superpc.plotred.lrtest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.plotred.lrtest
> ### Title: Plot likelihood ratio test statistics from supervised principal
> ###   components predictor
> ### Aliases: superpc.plotred.lrtest
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> a<- superpc.train(data, type="survival")
> aa<-superpc.cv(a, data)

fold= 1
1Loading required package: survival
Loading required package: splines
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fold= 2
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fold= 3
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> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> fit.red<- superpc.predict.red(a, data, data.test, .6)
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> 
> fit.redcv<- superpc.predict.red.cv(fit.red, aa,  data,  .6)
1
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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2
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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3
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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4
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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5
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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6
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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7
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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8
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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9
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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10
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> 
> superpc.plotred.lrtest(fit.redcv)
[1] TRUE
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.predict"
> 
> ### * superpc.predict
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.predict
> ### Title: Form principal components predictor from a trained superpc
> ###   object
> ### Aliases: superpc.predict
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*20),ncol=20)
> y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
> censoring.status<- sample(c(rep(1,17),rep(0,3)))
> censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1)
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> plot(fit$v.pred,ytest)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.predict.red"
> 
> ### * superpc.predict.red
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.predict.red
> ### Title: Feature selection for supervised principal components
> ### Aliases: superpc.predict.red
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> 
> a<- superpc.train(data, type="survival")
> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> fit.red<- superpc.predict.red(a,data, data.test, threshold=.6)
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> superpc.plotred.lrtest(fit.red)
[1] TRUE
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.predict.red.cv"
> 
> ### * superpc.predict.red.cv
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.predict.red.cv
> ### Title: Cross-validation of feature selection for supervised principal
> ###   components
> ### Aliases: superpc.predict.red.cv
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> 
> set.seed(332)
> #generate some data
> 
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> ytest<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
> 
> 
> a<- superpc.train(data, type="survival")
> aa<-superpc.cv(a, data)

fold= 1
1Loading required package: survival
Loading required package: splines
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fold= 2
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fold= 3
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fold= 4
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fold= 5
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fold= 6
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fold= 7
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fold= 8
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fold= 9
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fold= 10
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> 
> fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1, prediction.type="continuous")
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> fit.red<- superpc.predict.red(a,data, data.test, threshold= .6)
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> 
> fit.redcv<- superpc.predict.red.cv(fit.red, aa,  data, threshold= .6)
1
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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2
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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3
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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4
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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5
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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6
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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7
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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8
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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9
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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10
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
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> 
> superpc.plotred.lrtest(fit.redcv)
[1] TRUE
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.predictionplot"
> 
> ### * superpc.predictionplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.predictionplot
> ### Title: Plot outcome predictions from superpc
> ### Aliases: superpc.predictionplot
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> superpc.predictionplot(a,data,data,threshold=1)
Loading required package: survival
Loading required package: splines
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
Warning in fitter(X, Y, strats, offset, init, control, weights = weights,  : 
	 Ran out of iterations and did not converge
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.rainbowplot"
> 
> ### * superpc.rainbowplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.rainbowplot
> ### Title: Make rainbow plot of superpc and compeiting predictors
> ### Aliases: superpc.rainbowplot
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> set.seed(332)
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ 5*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> 
> ytest<- 10+svd(x[1:60,])$v[,1]+ 5*rnorm(40)
> censoring.status.test<- sample(c(rep(1,30),rep(0,10)))
> 
> competing.predictors.test=list(pred1=rnorm(40), pred2=as.factor(sample(c(1,2),replace
+ =TRUE,size=40)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> data.test=list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames=featurenames)
> 
> sample.labels=paste("te",as.character(1:40),sep="")
> 
> a<- superpc.train(data, type="survival")
> pred=superpc.predict(a,data,data.test,threshold=.25, n.components=1)$v.pred
Loading required package: survival
Loading required package: splines
> 
> superpc.rainbowplot(data,pred, sample.labels,competing.predictors=competing.predictors.test)
NULL
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "superpc.train"
> 
> ### * superpc.train
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: superpc.train
> ### Title: Prediction by supervised principal components
> ### Aliases: superpc.train
> ### Keywords: regression survival
> 
> ### ** Examples
> 
> #generate some example data
> set.seed(332)
> x<-matrix(rnorm(1000*40),ncol=40)
> y<-10+svd(x[1:60,])$v[,1]+ .1*rnorm(40)
> censoring.status<- sample(c(rep(1,30),rep(0,10)))
> 
> featurenames <- paste("feature",as.character(1:1000),sep="")
> data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
> 
> a<- superpc.train(data, type="survival")
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  89.94 6.61 102.41 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')