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("samr-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('samr')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "samr"
> 
> ### * samr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samr
> ### Title: Significance analysis of microarrays
> ### Aliases: samr
> ### Keywords: univar survival ts nonparametric
> 
> ### ** Examples
> 
> 
> ######### two class unpaired comparison
> # y must take values 1,2
> 
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> y<-c(rep(1,10),rep(2,10))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=100)
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> 
> delta=.4
> samr.plot(samr.obj,delta)
> 
> delta.table <- samr.compute.delta.table(samr.obj)
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> 
> siggenes.table<-samr.compute.siggenes.table(samr.obj,delta, data, delta.table)
> 
> 
> ########### two class paired
> 
> # y must take values  -1, 1, -2,2 etc, with (-k,k) being a pair
> 
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> y=c(-(1:10),1:10)
> 
> d=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(d,  resp.type="Two class paired", nperms=100)
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> 
> 
> 
> #############quantitative response
> 
> # y must take numeric values
> 
> set.seed(84048)
> x=matrix(rnorm(1000*9),ncol=9)
> 
> mu=c(3,2,1,0,0,0,1,2,3)
> b=runif(100)+.5
> x[1:100,]=x[1:100,]+ b
> 
> y=mu
> 
> d=list(x=x,y=y, 
+ geneid=as.character(1:nrow(x)),genenames=paste("gene", as.character(1:nrow(x))))
> 
> samr.obj =samr(d,  resp.type="Quantitative", nperms=50)
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> 
> 
> ########### oneclass
> # y is a vector of ones
> 
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> 
> y<-c(rep(1,20))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="One class", nperms=100)
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> 
> 
> ###########survival data
> # y is numeric; censoring.status=1 for failures, and 0 for censored
> 
> set.seed(84048)
> x=matrix(rnorm(1000*50),ncol=50)
> x[1:50,26:50]= x[1:50,26:50]+2
> x[51:100,26:50]= x[51:100,26:50]-2
> 
> y=abs(rnorm(50))
> y[26:50]=y[26:50]+2
> censoring.status=sample(c(0,1),size=50,replace=TRUE)
> d=list(x=x,y=y,censoring.status=censoring.status,
+ geneid=as.character(1:1000),genenames=paste("gene", as.character(1:1000)))
> 
> samr.obj=samr(d,  resp.type="Survival", nperms=20)
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> 
> ################multi-class example
> # y takes values 1,2,3,...k where k= number of classes
> 
> set.seed(84048)
> x=matrix(rnorm(1000*10),ncol=10)
> x[1:50,6:10]= x[1:50,6:10]+2
> x[51:100,6:10]= x[51:100,6:10]-2
> 
> y=c(rep(1,3),rep(2,3),rep(3,4))
> d=list(x=x,y=y,geneid=as.character(1:1000),genenames=paste("gene", as.character(1:1000))) 
> 
> samr.obj <- samr(d,  resp.type="Multiclass")
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> 
> 
> #################### timecourse data
> 
> # elements of y are of the form  kTimet  where k is the class label and t
> # is the time; in addition, the   suffixes Start or End indicate the first
> # and last observation in a given time course
> # the class label can be that for a two class unpaired, one class or
> # two class paired problem
> 
> set.seed(8332)
> y=paste(c(rep(1,15),rep(2,15)),"Time",rep(c(1,2,3,4,5,1.1,2.5, 3.7, 4.1,5.5),3),sep="")
> start=c(1,6,11,16,21,26)
> for(i in start){
+ y[i]=paste(y[i],"Start",sep="")
+ }
> for(i in  start+4){
+ y[i]=paste(y[i],"End",sep="")
+ }
> x=matrix(rnorm(1000*30),ncol=30)
> x[1:50,16:20]=x[1:50,16:20]+matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> x[1:50,21:25]=x[1:50,21:25]+matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> x[1:50,26:30]=x[1:50,26:30]+matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> 
> x[51:100,16:20]=x[51:100,16:20]-matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> x[51:100,21:25]=x[51:100,21:25]-matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> x[51:100,26:30]=x[51:100,26:30]-matrix(3*c(0,1,2,3,4),ncol=5,nrow=50,byrow=TRUE)
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<- samr(data,  resp.type="Two class unpaired timecourse",
+  nperms=100, time.summary.type="slope")
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> 
> ##################### pattern discovery
> # here there is no outcome y; the desired eigengene is indicated by 
> # the argument eigengene.numbe in the data object
> 
> set.seed(32)
> x=matrix(rnorm(1000*9),ncol=9)
> mu=c(3,2,1,0,0,0,1,2,3)
> b=3*runif(100)+.5
> x[1:100,]=x[1:100,]+ b
> 
> 
> d=list(x=x,eigengene.number=1,
+ geneid=as.character(1:nrow(x)),genenames=paste("gene", as.character(1:nrow(x))))
> 
> samr.obj=samr(d,  resp.type="Pattern discovery", nperms=50)
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> 
> 
> 
> 
> cleanEx(); ..nameEx <- "samr.compute.delta.table"
> 
> ### * samr.compute.delta.table
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samr.compute.delta.table
> ### Title: Compute delta table for SAM analysis
> ### Aliases: samr.compute.delta.table
> ### Keywords: univar survival ts nonparametric
> 
> ### ** Examples
> 
> #generate some example data
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> 
> y<-c(rep(1,10),rep(2,10))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=50)
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> 
> delta.table<- samr.compute.delta.table(samr.obj)
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> 
> 
> 
> 
> cleanEx(); ..nameEx <- "samr.compute.siggenes.table"
> 
> ### * samr.compute.siggenes.table
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samr.compute.siggenes.table
> ### Title: Compute significant genes table
> ### Aliases: samr.compute.siggenes.table
> ### Keywords: univar survival ts nonparametric
> 
> ### ** Examples
> 
> #generate some example data
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> 
> y<-c(rep(1,10),rep(2,10))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=100)
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> 
> delta.table<-samr.compute.delta.table(samr.obj)
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> del<- 0.3
> siggenes.table<- samr.compute.siggenes.table(samr.obj, del, data, delta.table)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "samr.missrate"
> 
> ### * samr.missrate
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samr.missrate
> ### Title: Estimate the miss rate table for a SAM analysis
> ### Aliases: samr.missrate
> ### Keywords: univar survival ts nonparametric
> 
> ### ** Examples
> 
> #generate some example data
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> 
> y<-c(rep(1,10),rep(2,10))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=100)
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> 
> delta.table<-samr.compute.delta.table(samr.obj)
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> del<- 0.3
> siggenes.table<- samr.compute.siggenes.table(samr.obj, del, data, delta.table)
> 
> samr.missrate(samr.obj, del, delta.table)
      Quantiles      Cutpoints          Miss Rate(%)
 [1,] "0 -> 0.05"    "-1.025 -> -0.652" "9.64"      
 [2,] "0.05 -> 0.1"  "-0.652 -> -0.515" "6.06"      
 [3,] "0.1 -> 0.15"  "-0.515 -> -0.444" "28.08"     
 [4,] "0.15 -> 0.2"  "-0.444 -> -0.357" "0"         
 [5,] "0.2 -> 0.25"  "-0.357 -> -0.288" "0.52"      
 [6,] "0.25 -> 0.75" "-0.288 -> 0.253"  "0"         
 [7,] "0.75 -> 0.8"  "0.253 -> 0.324"   "0"         
 [8,] "0.8 -> 0.85"  "0.324 -> 0.388"   "15.8"      
 [9,] "0.85 -> 0.9"  "0.388 -> 0.493"   "0"         
[10,] "0.9 -> 0.95"  "0.493 -> 0.667"   "0"         
[11,] "0.95 -> 1"    "0.667 -> 1.112"   "8.23"      
> 
> 
> 
> cleanEx(); ..nameEx <- "samr.plot"
> 
> ### * samr.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samr.plot
> ### Title: Make Q-Q plot for SAM analysis
> ### Aliases: samr.plot
> ### Keywords: univar survival ts nonparametric
> 
> ### ** Examples
> 
> #generate some example data
> set.seed(100)
> x<-matrix(rnorm(1000*20),ncol=20)
> dd<-sample(1:1000,size=100)
> 
> u<-matrix(2*rnorm(100),ncol=10,nrow=100)
> x[dd,11:20]<-x[dd,11:20]+u
> 
> y<-c(rep(1,10),rep(2,10))
> 
> data=list(x=x,y=y, geneid=as.character(1:nrow(x)),genenames=paste("g",as.character(1:nrow(x)),sep=""), logged2=TRUE)
> 
> samr.obj<-samr(data,  resp.type="Two class unpaired", nperms=50)
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> 
> samr.plot(samr.obj, del=.3)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  36.7 5.78 42.74 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')