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("limma-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('limma')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "LargeDataObject"
> 
> ### * LargeDataObject
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LargeDataObject-class
> ### Title: Large Data Object - class
> ### Aliases: LargeDataObject-class show,LargeDataObject-method
> ### Keywords: classes data
> 
> ### ** Examples
> 
> #  see normalizeBetweenArrays
> 
> 
> 
> cleanEx(); ..nameEx <- "PrintLayout"
> 
> ### * PrintLayout
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PrintLayout
> ### Title: Print Layout - class
> ### Aliases: PrintLayout-class
> ### Keywords: classes data
> 
> ### ** Examples
> 
> #  Settings for Swirl and ApoAI example data sets in User's Guide
> 
> printer <- list(ngrid.r=4, ngrid.c=4, nspot.r=22, nspot.c=24, ndups=1, spacing=1, npins=16, start="topleft")
> 
> #  Typical settings at the Australian Genome Research Facility
> 
> #  Full pin set, duplicates side-by-side on same row
> printer <- list(ngrid.r=12, ngrid.c=4, nspot.r=20, nspot.c=20, ndups=2, spacing=1, npins=48, start="topright")
> 
> #  Half pin set, duplicates in top and lower half of slide
> printer <- list(ngrid.r=12, ngrid.c=4, nspot.r=20, nspot.c=20, ndups=2, spacing=9600, npins=24, start="topright")
> 
> 
> 
> cleanEx(); ..nameEx <- "TestResults"
> 
> ### * TestResults
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: TestResults-class
> ### Title: Matrix of Test Results - class
> ### Aliases: TestResults-class show,TestResults-method summary.TestResults
> ### Keywords: classes htest
> 
> ### ** Examples
> ## Not run: 
> ##D #  Assume a data object MA and a design matrix
> ##D fit <- lmFit(MA, design)
> ##D fit <- eBayes(fit)
> ##D results <- decideTests(fit)
> ##D summary(results)
> ## End(Not run)
> 
> 
> cleanEx(); ..nameEx <- "arrayWeights"
> 
> ### * arrayWeights
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: arrayWeights
> ### Title: Array Quality Weights
> ### Aliases: arrayWeights
> ### Keywords: regression models
> 
> ### ** Examples
> 
> ## Not run: 
> ##D array.wts <- arrayWeights(MA, design)
> ##D fit.wts <- lmFit(MA, design, weights=array.wts)
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "arrayWeightsQuick"
> 
> ### * arrayWeightsQuick
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: arrayWeightsQuick
> ### Title: Array Quality Weights
> ### Aliases: arrayWeightsQuick
> ### Keywords: regression
> 
> ### ** Examples
> 
> ## Not run: 
> ##D fit <- lmFit(y, design)
> ##D arrayWeightsQuick(y, fit)
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "auROC"
> 
> ### * auROC
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: auROC
> ### Title: Area Under Receiver Operating Curve
> ### Aliases: auROC
> ### Keywords: htest
> 
> ### ** Examples
> 
> auROC(c(1,1,0,0,0))
[1] 1
> truth <- rbinom(30,size=1,prob=0.2)
> stat <- rchisq(30,df=2)
> auROC(truth,stat)
[1] 0.6041667
> 
> 
> 
> cleanEx(); ..nameEx <- "backgroundcorrect"
> 
> ### * backgroundcorrect
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: backgroundCorrect
> ### Title: Correct Intensities for Background
> ### Aliases: backgroundCorrect
> ### Keywords: models
> 
> ### ** Examples
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
[1] -1  0  1  2

$G
[1] -1  0  1  2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
[1] 0.5 0.5 1.0 2.0

$G
[1] 0.5 0.5 1.0 2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
[1] 4 5 6 7

$G
[1] 4 5 6 7

> 
> 
> 
> cleanEx(); ..nameEx <- "blockDiag"
> 
> ### * blockDiag
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: blockDiag
> ### Title: Block Diagonal Matrix
> ### Aliases: blockDiag
> ### Keywords: array
> 
> ### ** Examples
> 
> a <- matrix(1,3,2)
> b <- matrix(2,2,2)
> blockDiag(a,b)
  1 2 1 2
1 1 1 0 0
2 1 1 0 0
3 1 1 0 0
1 0 0 2 2
2 0 0 2 2
> 
> 
> 
> cleanEx(); ..nameEx <- "cbind"
> 
> ### * cbind
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cbind
> ### Title: Combine RGList or MAList Objects
> ### Aliases: cbind.RGList cbind.MAList rbind.RGList rbind.MAList
> ### Keywords: manip
> 
> ### ** Examples
> 
> M <- A <- matrix(11:14,4,2)
> rownames(M) <- rownames(A) <- c("a","b","c","d")
> colnames(M) <- colnames(A) <- c("A1","A2")
> MA1 <- new("MAList",list(M=M,A=A))
> 
> M <- A <- matrix(21:24,4,2)
> rownames(M) <- rownames(A) <- c("a","b","c","d")
> colnames(M) <- colnames(A) <- c("B1","B2")
> MA2 <- new("MAList",list(M=M,A=A))
> 
> cbind(MA1,MA2)
An object of class "MAList"
$M
  A1 A2 B1 B2
a 11 11 21 21
b 12 12 22 22
c 13 13 23 23
d 14 14 24 24

$A
  A1 A2 B1 B2
a 11 11 21 21
b 12 12 22 22
c 13 13 23 23
d 14 14 24 24

> 
> 
> 
> cleanEx(); ..nameEx <- "channel2M"
> 
> ### * channel2M
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: designI2M
> ### Title: Convert Individual Channel Design Matrix to M-A Format
> ### Aliases: designI2M designI2A
> ### Keywords: regression
> 
> ### ** Examples
> 
> X <- cbind(1,c(1,1,1,1,0,0,0,0),c(0,0,0,0,1,1,1,1))
> designI2M(X)
     [,1] [,2] [,3]
[1,]    0    0    0
[2,]    0    0    0
[3,]    0    0    0
[4,]    0    0    0
> designI2A(X)
     [,1] [,2] [,3]
[1,]    1    1    0
[2,]    1    1    0
[3,]    1    0    1
[4,]    1    0    1
> 
> 
> 
> cleanEx(); ..nameEx <- "classifytests"
> 
> ### * classifytests
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: classifyTests
> ### Title: Treat Simultaneous T-Tests as Classification Problem
> ### Aliases: FStat classifyTestsF classifyTestsT classifyTestsP
> ### Keywords: htest
> 
> ### ** Examples
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> 
> # See also the examples for contrasts.fit and vennDiagram
> 
> 
> 
> cleanEx(); ..nameEx <- "contrasts.fit"
> 
> ### * contrasts.fit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: contrasts.fit
> ### Title: Compute Contrasts from Linear Model Fit
> ### Aliases: contrasts.fit
> ### Keywords: htest
> 
> ### ** Examples
> 
> #  Simulate gene expression data: 6 microarrays and 100 genes
> #  with one gene differentially expressed in first 3 arrays
> M <- matrix(rnorm(100*6,sd=0.3),100,6)
> M[1,1:3] <- M[1,1:3] + 2
> #  Design matrix corresponds to oneway layout, columns are orthogonal
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> fit <- lmFit(M,design=design)
> #  Would like to consider original two estimates plus difference between first 3 and last 3 arrays
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit2 <- contrasts.fit(fit,contrast.matrix)
> fit2 <- eBayes(fit2)
> #  Large values of eb$t indicate differential expression
> results <- classifyTestsF(fit2)
> vennCounts(results)
     First3 Last3 Last3-First3 Counts
[1,]      0     0            0     97
[2,]      0     0            1      1
[3,]      0     1            0      1
[4,]      0     1            1      0
[5,]      1     0            0      0
[6,]      1     0            1      1
[7,]      1     1            0      0
[8,]      1     1            1      0
attr(,"class")
[1] "VennCounts"
> 
> 
> 
> cleanEx(); ..nameEx <- "controlStatus"
> 
> ### * controlStatus
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: controlStatus
> ### Title: Set Status of each Spot from List of Spot Types
> ### Aliases: controlStatus
> ### Keywords: IO
> 
> ### ** Examples
> 
> genes <- data.frame(ID=c("Control","Control","Control","Control","AA1","AA2","AA3","AA4"),
+ Name=c("Ratio 1","Ratio 2","House keeping 1","House keeping 2","Gene 1","Gene 2","Gene 3","Gene 4"))
> types <- data.frame(SpotType=c("Gene","Ratio","Housekeeping"),ID=c("*","Control","Control"),Name=c("*","Ratio*","House keeping*"),col=c("black","red","blue"))
> status <- controlStatus(types,genes)
Matching patterns for: ID Name 
Found 8 Gene 
Found 2 Ratio 
Found 2 Housekeeping 
Setting attributes: values col 
> 
> 
> 
> cleanEx(); ..nameEx <- "convest"
> 
> ### * convest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: convest
> ### Title: Estimate Proportion of True Null Hypotheses
> ### Aliases: convest
> ### Keywords: htest
> 
> ### ** Examples
> 
> # First simulate data, use no.genes genes and no.ind individuals,
> # with given value of pi0. Draw from normal distribution with mean=0
> # (true null) and mean=mean.diff (false null).
> 
> no.genes <- 1000
> no.ind <- 20
> pi0 <- 0.9
> mean.diff <- 1
> n1 <- round(pi0*no.ind*no.genes)
> n2 <- round((1-pi0)*no.ind*no.genes)
> x <- matrix(c(rnorm(n1,mean=0),rnorm(n2,mean=mean.diff)),ncol=no.ind,byrow=TRUE)
> 
> # calculate p-values using your favorite method, e.g.
> pvals <- ebayes(lm.series(x))$p.value
> 
> # run the convex decreasing density estimator to estimate pi0
> convest(pvals,niter=100,doplot=interactive())
[1] 0.8967085
> 
> 
> 
> cleanEx(); ..nameEx <- "dim"
> 
> ### * dim
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dim
> ### Title: Retrieve the Dimensions of an RGList, MAList or MArrayLM Object
> ### Aliases: dim.RGList dim.MAList dim.MArrayLM length.RGList length.MAList
> ###   length.MArrayLM
> ### Keywords: array
> 
> ### ** Examples
> 
> M <- A <- matrix(11:14,4,2)
> rownames(M) <- rownames(A) <- c("a","b","c","d")
> colnames(M) <- colnames(A) <- c("A1","A2")
> MA <- new("MAList",list(M=M,A=A))
> dim(M)
[1] 4 2
> ncol(M)
[1] 2
> nrow(M)
[1] 4
> length(M)
[1] 8
> 
> 
> 
> cleanEx(); ..nameEx <- "dupcor"
> 
> ### * dupcor
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: dupcor.series
> ### Title: Correlation Between Duplicates
> ### Aliases: duplicateCorrelation dupcor.series
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> #  See gls.series for an example
> 
> 
> 
> cleanEx(); ..nameEx <- "ebayes"
> 
> ### * ebayes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ebayes
> ### Title: Empirical Bayes Statistics for Differential Expression
> ### Aliases: ebayes eBayes
> ### Keywords: htest
> 
> ### ** Examples
> 
> #  Simulate gene expression data,
> #  6 microarrays and 100 genes with one gene differentially expressed
> set.seed(2004); invisible(runif(100))
> M <- matrix(rnorm(100*6,sd=0.3),100,6)
> M[1,] <- M[1,] + 1
> fit <- lmFit(M)
> 
> #  Ordinary t-statistic
> par(mfrow=c(1,2))
> ordinary.t <- fit$coef / fit$stdev.unscaled / fit$sigma
> qqt(ordinary.t,df=fit$df.residual,main="Ordinary t")
> abline(0,1)
> 
> #  Moderated t-statistic
> eb <- eBayes(fit)
> qqt(eb$t,df=eb$df.prior+eb$df.residual,main="Moderated t")
> abline(0,1)
> #  Points off the line may be differentially expressed
> par(mfrow=c(1,1))
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "geneSetTest"
> 
> ### * geneSetTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: geneSetTest
> ### Title: Gene Set Test
> ### Aliases: geneSetTest
> ### Keywords: htest
> 
> ### ** Examples
> 
> sel <- c(2,4,5)
> stat <- -9:9
> geneSetTest(sel,stat,nsim=100)
[1] 0
> geneSetTest(sel,stat,ranks.only=TRUE)
[1] 0.03302374
> 
> 
> 
> cleanEx(); ..nameEx <- "getSpacing"
> 
> ### * getSpacing
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: getSpacing
> ### Title: Get Numerical Spacing
> ### Aliases: getSpacing
> ### Keywords: IO
> 
> ### ** Examples
> 
> getSpacing("columns",list(ngrid.r=2,ngrid.c=2,nspot.r=20,nspot.c=19))
[1] 1
> getSpacing("rows",list(ngrid.r=2,ngrid.c=2,nspot.r=20,nspot.c=19))
[1] 19
> getSpacing("topbottom",list(ngrid.r=2,ngrid.c=2,nspot.r=20,nspot.c=19))
[1] 760
> 
> 
> 
> cleanEx(); ..nameEx <- "getlayout"
> 
> ### * getlayout
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: getLayout
> ### Title: Extract the Print Layout of an Array from the GAL File
> ### Aliases: getLayout getLayout2
> ### Keywords: IO
> 
> ### ** Examples
> 
> # gal <- readGAL()
> # layout <- getLayout(gal)
> 
> 
> 
> cleanEx(); ..nameEx <- "gls.series"
> 
> ### * gls.series
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gls.series
> ### Title: Generalized Least Squares for Series of Microarrays
> ### Aliases: gls.series
> ### Keywords: models regression
> 
> ### ** Examples
> 
> M <- matrix(rnorm(10*6),10,6)
> dupcor <- duplicateCorrelation(M)
Loading required package: statmod

Attaching package: 'statmod'


	The following object(s) are masked from package:limma :

	 matvec vecmat 

> fit <- gls.series(M,correlation=dupcor$consensus.correlation)
> 
> 
> 
> cleanEx(); ..nameEx <- "heatdiagram"
> 
> ### * heatdiagram
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: heatdiagram
> ### Title: Stemmed Heat Diagram
> ### Aliases: heatdiagram heatDiagram
> ### Keywords: hplot
> 
> ### ** Examples
> 
> library(sma)
> data(MouseArray)
> MA <- normalizeWithinArrays(mouse.data,layout=mouse.setup)
> design <- cbind(c(1,1,1,0,0,0),c(0,0,0,1,1,1))
> fit <- lmFit(MA,design=design)
> contrasts.mouse <- cbind(Control=c(1,0),Mutant=c(0,1),Difference=c(-1,1))
> fit <- eBayes(contrasts.fit(fit,contrasts=contrasts.mouse))
> results <- decideTests(fit,method="global",p=0.1)
> heatDiagram(results,fit$coef,primary="Difference")
> 
> 
> 
> cleanEx(); ..nameEx <- "helpMethods"
> 
> ### * helpMethods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: helpMethods
> ### Title: Prompt for Method Help Topics
> ### Aliases: helpMethods
> ### Keywords: methods
> 
> ### ** Examples
> 
> ## Not run: helpMethods(show)
> 
> 
> 
> cleanEx(); ..nameEx <- "imageplot"
> 
> ### * imageplot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: imageplot
> ### Title: Image Plot of Microarray Statistics
> ### Aliases: imageplot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> M <- rnorm(8*4*16*16)
> imageplot(M,layout=list(ngrid.r=8,ngrid.c=4,nspot.r=16,nspot.c=16))
> 
> 
> 
> cleanEx(); ..nameEx <- "intraspotCorrelation"
> 
> ### * intraspotCorrelation
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: intraspotCorrelation
> ### Title: Intra-Spot Correlation for Two Color Data
> ### Aliases: intraspotCorrelation
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> #  See lmscFit
> 
> 
> 
> cleanEx(); ..nameEx <- "isfullrank"
> 
> ### * isfullrank
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: is.fullrank
> ### Title: Check for Full Column Rank
> ### Aliases: is.fullrank nonEstimable
> ### Keywords: algebra
> 
> ### ** Examples
> 
> # TRUE
> is.fullrank(1)
[1] TRUE
> is.fullrank(cbind(1,0:1))
[1] TRUE
> 
> # FALSE
> is.fullrank(0)
[1] FALSE
> is.fullrank(matrix(1,2,2))
[1] FALSE
> nonEstimable(matrix(1,2,2))
[1] "2"
> 
> 
> 
> cleanEx(); ..nameEx <- "isnumeric"
> 
> ### * isnumeric
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: isNumeric
> ### Title: Test for Numeric Argument
> ### Aliases: isNumeric
> ### Keywords: programming
> 
> ### ** Examples
> 
> isNumeric(3)
[1] TRUE
> isNumeric("a")
[1] FALSE
> x <- data.frame(a=c(1,1),b=c(0,1))
> isNumeric(x)   # TRUE
[1] TRUE
> is.numeric(x)  # FALSE
[1] FALSE
> 
> 
> 
> cleanEx(); ..nameEx <- "kooperberg"
> 
> ### * kooperberg
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kooperberg
> ### Title: Kooperberg Model-Based Background Correction
> ### Aliases: kooperberg
> ### Keywords: models
> 
> ### ** Examples
> 
> #  This is example code for reading and background correcting GenePix data
> #  given GenePix Results (gpr) files in the working directory (data not
> #  provided).
> ## Not run: 
> ##D genepixFiles <- dir(pattern="*\\.gpr$") # get the names of the GenePix image analysis output files in the current directory
> ##D read.series(genepixFiles, suffix=NULL, skip=26, sep="\t") # read the files into data.frames, assuming headers of 26 lines
> ##D layout <- getLayout(get(genepixFiles[1]))
> ##D RGmodel <- kooperberg(genepixFiles, layout=layout) # model-based background correction
> ##D MA <- normalizeWithinArrays(RGModel) # normalize the data
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "limmaUsersGuide"
> 
> ### * limmaUsersGuide
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: limmaUsersGuide
> ### Title: View Limma User's Guide
> ### Aliases: limmaUsersGuide
> ### Keywords: documentation
> 
> ### ** Examples
> 
> limmaUsersGuide(view=FALSE)
[1] "/CRANPkg/check/limma.Rcheck/limma/doc/usersguide.pdf"
> 
> 
> 
> cleanEx(); ..nameEx <- "lm.series"
> 
> ### * lm.series
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lm.series
> ### Title: Linear Model for Series of Arrays
> ### Aliases: lm.series
> ### Keywords: models regression
> 
> ### ** Examples
> 
> #  Simulate gene expression data,
> #  6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays
> M <- matrix(rnorm(100*6,sd=0.3),100,6)
> M[1,1:3] <- M[1,1:3] + 2
> #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> fit <- lm.series(M,design=design)
> eb <- ebayes(fit)
> #  Large values of eb$t indicate differential expression
> qqt(eb$t[,1],df=fit$df+eb$df.prior)
> abline(0,1)
> 
> 
> 
> cleanEx(); ..nameEx <- "lmscFit"
> 
> ### * lmscFit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: lmscFit
> ### Title: Fit Linear Model to Individual Channels of Two-Color Data
> ### Aliases: lmscFit
> ### Keywords: models regression
> 
> ### ** Examples
> 
> library(sma)
> # Subset of data from ApoAI case study in Limma User's Guide
> data(MouseArray)
> # Avoid non-positive intensities
> RG <- backgroundCorrect(mouse.data,method="half")
> MA <- normalizeWithinArrays(RG,mouse.setup)
> MA <- normalizeBetweenArrays(MA,method="Aq")
> # Randomly choose 500 genes for this example
> i <- sample(1:nrow(MA),500)
> MA <- MA[i,]
> targets <- data.frame(Cy3=I(rep("Pool",6)),Cy5=I(c("WT","WT","WT","KO","KO","KO")))
> targets.sc <- targetsA2C(targets)
> targets.sc$Target <- factor(targets.sc$Target,levels=c("Pool","WT","KO"))
> design <- model.matrix(~Target,data=targets.sc)
> corfit <- intraspotCorrelation(MA,design)
Loading required package: statmod

Attaching package: 'statmod'


	The following object(s) are masked from package:limma :

	 matvec vecmat 

> fit <- lmscFit(MA,design,correlation=corfit$consensus)
> cont.matrix <- cbind(KOvsWT=c(0,-1,1))
> fit2 <- contrasts.fit(fit,cont.matrix)
> fit2 <- eBayes(fit2)
> topTable(fit2,adjust="fdr")
             M         A         t   P.Value         B
42  -0.5457321  9.171580 -3.716858 0.5721393 -3.789711
263 -0.5400095 10.242886 -3.399984 0.5721393 -3.876275
236  0.6901788  8.849650  3.373107 0.5721393 -3.883915
20  -0.6917441 11.456040 -3.187763 0.5721393 -3.937813
1   -0.4515620  9.460569 -3.170588 0.5721393 -3.942912
398 -0.9212524 10.773470 -3.079665 0.5721393 -3.970191
486  0.8371904 10.603394  3.007018 0.5721393 -3.992317
59  -0.3530359  8.902725 -3.005250 0.5721393 -3.992859
202 -0.7007627  9.632033 -3.004588 0.5721393 -3.993062
141  0.3611022  8.599806  2.782030 0.7139269 -4.062557
> 
> 
> 
> cleanEx(); ..nameEx <- "loessfit"
> 
> ### * loessfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: loessFit
> ### Title: Fast Simple Loess
> ### Aliases: loessFit
> ### Keywords: models
> 
> ### ** Examples
> 
> y <- rnorm(1000)
> x <- rnorm(1000)
> w <- rep(1,1000)
> # The following are equivalent apart from execution time
> # and interpolation inaccuracies
> system.time(fit <- loessFit(y,x)$fitted)
[1] 0.01 0.00 0.01 0.00 0.00
> system.time(fit <- loessFit(y,x,w)$fitted)
[1] 0.07 0.00 0.07 0.00 0.00
> system.time(fit <- fitted(loess(y~x,weights=w,span=0.3,family="symmetric",iterations=4)))
Warning: k-d tree limited by memory. ncmax= 1000
[1] 0.08 0.00 0.08 0.00 0.00
> # The same but with sorted x-values
> system.time(fit <- lowess(x,y,f=0.3)$y)
[1] 0.01 0.00 0.01 0.00 0.00
> 
> 
> 
> cleanEx(); ..nameEx <- "ma3x3"
> 
> ### * ma3x3
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ma3x3
> ### Title: Two dimensional Moving Averages with 3x3 Window
> ### Aliases: ma3x3.matrix ma3x3.spottedarray
> ### Keywords: smooth
> 
> ### ** Examples
> 
> x <- matrix(c(2,5,3,1,6,3,10,12,4,6,4,8,2,1,9,0),4,4)
> ma3x3.matrix(x,FUN="mean")
         [,1]     [,2]     [,3]     [,4]
[1,] 4.000000 4.333333 3.666667 3.250000
[2,] 4.833333 4.777778 5.000000 4.333333
[3,] 5.666667 5.777778 5.888889 4.666667
[4,] 6.500000 6.333333 7.166667 5.250000
> ma3x3.matrix(x,FUN="min")
     [,1] [,2] [,3] [,4]
[1,]    2    2    1    1
[2,]    2    2    1    1
[3,]    1    1    0    0
[4,]    1    1    0    0
> 
> 
> 
> cleanEx(); ..nameEx <- "makeContrasts"
> 
> ### * makeContrasts
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: makeContrasts
> ### Title: Construct Matrix of Custom Contrasts
> ### Aliases: makeContrasts
> ### Keywords: regression
> 
> ### ** Examples
> 
> makeContrasts(B-A,C-B,C-A,levels=c("A","B","C"))
  B - A C - B C - A
A    -1     0    -1
B     1    -1     0
C     0     1     1
> makeContrasts("A","B","B-A",levels=c("A","B"))
  A B B-A
A 1 0  -1
B 0 1   1
> 
> 
> 
> cleanEx(); ..nameEx <- "makeunique"
> 
> ### * makeunique
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: makeUnique
> ### Title: Make Values of Character Vector Unique
> ### Aliases: makeUnique
> ### Keywords: character
> 
> ### ** Examples
> 
> x <- c("a","a","b")
> makeUnique(x)
[1] "a1" "a2" "b" 
> 
> 
> 
> cleanEx(); ..nameEx <- "matvec"
> 
> ### * matvec
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: matvec
> ### Title: Multiply a Matrix by a Vector
> ### Aliases: matvec vecmat
> ### Keywords: array algebra
> 
> ### ** Examples
> 
> A <- matrix(1:12,3,4)
> A
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> matvec(A,c(1,2,3,4))
     [,1] [,2] [,3] [,4]
[1,]    1    8   21   40
[2,]    2   10   24   44
[3,]    3   12   27   48
> vecmat(c(1,2,3),A)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    4   10   16   22
[3,]    9   18   27   36
> 
> 
> 
> cleanEx(); ..nameEx <- "merge"
> 
> ### * merge
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: merge.RGList
> ### Title: Merge RGList or MAList Data Objects
> ### Aliases: merge.RGList merge.MAList
> ### Keywords: manip
> 
> ### ** Examples
> 
> M <- A <- matrix(11:14,4,2)
> rownames(M) <- rownames(A) <- c("a","a","b","c")
> MA1 <- new("MAList",list(M=M,A=A))
> 
> M <- A <- matrix(21:24,4,2)
> rownames(M) <- rownames(A) <- c("b","a","a","c")
> MA2 <- new("MAList",list(M=M,A=A))
> 
> merge(MA1,MA2)
An object of class "MAList"
$M
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$A
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(MA2,MA1)
An object of class "MAList"
$M
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$A
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> 
> 
> cleanEx(); ..nameEx <- "modelMatrix"
> 
> ### * modelMatrix
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: modelMatrix
> ### Title: Construct Design Matrix
> ### Aliases: modelMatrix uniqueTargets
> ### Keywords: regression
> 
> ### ** Examples
> 
> targets <- cbind(Cy3=c("Ref","Control","Ref","Treatment"),Cy5=c("Control","Ref","Treatment","Ref"))
> rownames(targets) <- paste("Array",1:4)
> 
> parameters <- cbind(C=c(-1,1,0),T=c(-1,0,1))
> rownames(parameters) <- c("Ref","Control","Treatment")
> 
> modelMatrix(targets, parameters)
Found unique target names:
 Control Ref Treatment 
         C  T
Array 1  1  0
Array 2 -1  0
Array 3  0  1
Array 4  0 -1
> modelMatrix(targets, ref="Ref")
Found unique target names:
 Control Ref Treatment 
        Control Treatment
Array 1       1         0
Array 2      -1         0
Array 3       0         1
Array 4       0        -1
> 
> 
> 
> cleanEx(); ..nameEx <- "modifyWeights"
> 
> ### * modifyWeights
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: modifyWeights
> ### Title: modifyWeights
> ### Aliases: modifyWeights
> ### Keywords: hplot
> 
> ### ** Examples
> 
> w <- matrix(runif(6*3),6,3)
> status <- c("Gene","Gene","Ratio_Control","Ratio_Control","Gene","Gene")
> modifyWeights(w,status,values="Ratio_Control",multipliers=0)
          [,1]      [,2]      [,3]
[1,] 0.2655087 0.9446753 0.6870228
[2,] 0.3721239 0.6607978 0.3841037
[3,] 0.0000000 0.0000000 0.0000000
[4,] 0.0000000 0.0000000 0.0000000
[5,] 0.2016819 0.2059746 0.7176185
[6,] 0.8983897 0.1765568 0.9919061
> 
> 
> 
> cleanEx(); ..nameEx <- "mrlm"
> 
> ### * mrlm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rlm
> ### Title: Robust Linear Model for Series of Microarrays
> ### Aliases: rlm.series mrlm
> ### Keywords: models regression
> 
> ### ** Examples
> 
> #  Simulate gene expression data,
> #  6 microarrays and 100 genes with one gene differentially expressed in first 3 arrays
> M <- matrix(rnorm(100*6,sd=0.3),100,6)
> M[1,1:3] <- M[1,1:3] + 2
> #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> fit <- mrlm(M,design=design)
Loading required package: MASS
Warning in rlm.default(x = X, y = y, weights = w, ...) : 
	 rlm failed to converge in 20 steps
Warning in rlm.default(x = X, y = y, weights = w, ...) : 
	 rlm failed to converge in 20 steps
Warning in rlm.default(x = X, y = y, weights = w, ...) : 
	 rlm failed to converge in 20 steps
Warning in rlm.default(x = X, y = y, weights = w, ...) : 
	 rlm failed to converge in 20 steps
Warning in rlm.default(x = X, y = y, weights = w, ...) : 
	 rlm failed to converge in 20 steps
> eb <- ebayes(fit)
> #  Large values of eb$t indicate differential expression
> qqt(eb$t[,1],df=fit$df+eb$df.prior)
> abline(0,1)
> 
> 
> 
> cleanEx(); ..nameEx <- "normalizeRobustSpline"
> 
> ### * normalizeRobustSpline
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normalizeRobustSpline
> ### Title: Normalize Single Microarray Using Shrunk Robust Splines
> ### Aliases: normalizeRobustSpline
> ### Keywords: models
> 
> ### ** Examples
> 
> library(sma)
> data(MouseArray)
> M <- m.spot(mouse1)
> A <- a.spot(mouse1)
> M <- normalizeRobustSpline(M,A,mouse.setup)
Loading required package: MASS
Loading required package: splines
> 
> 
> 
> cleanEx(); ..nameEx <- "normalizeWithinArrays"
> 
> ### * normalizeWithinArrays
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normalizeWithinArrays
> ### Title: Normalize Within Arrays
> ### Aliases: normalizeWithinArrays MA.RG RG.MA
> ### Keywords: models
> 
> ### ** Examples
> 
> #  See normalizeBetweenArrays
> 
> 
> 
> cleanEx(); ..nameEx <- "normalizebetweenarrays"
> 
> ### * normalizebetweenarrays
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normalizeBetweenArrays
> ### Title: Normalize Between Arrays
> ### Aliases: normalizeBetweenArrays
> ### Keywords: models multivariate
> 
> ### ** Examples
> 
> library(sma)
> data(MouseArray)
> MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> plot.scale.box(MA$M)
> 
> #  Between array scale normalization as in Yang et al (2001):
> MA <- normalizeBetweenArrays(MA,method="scale")
> print(MA)
An object of class "MAList"
$M
            [,1]        [,2]       [,3]        [,4]       [,5]       [,6]
[1,] -0.22060913 -0.97047013 -0.7132995  0.05299212 -0.4035381  0.8835727
[2,]  0.06737471  0.13335374  0.2444237  0.74556284  0.6523577  1.3419787
[3,] -0.23126298 -0.81105738 -0.8420205 -0.09262048 -0.4281592 -0.3253819
[4,] -0.17305532 -0.07044322 -0.3286331  0.20358545  0.2037528 -0.1023735
[5,] -0.84106756 -0.10980624 -0.1029215 -0.04902437  0.2820152 -0.2241410
6379 more rows ...

$A
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]
[1,] 11.332980 11.198841 11.337353  9.693899 11.196822 10.506374
[2,] 11.245664 11.074098 11.051345 10.931562 11.273305 10.008818
[3,] 10.113995 10.923628 12.322088  9.875351 11.096463 10.829522
[4,]  8.390963  9.019036  8.720987  9.774672  8.826249  9.113240
[5,]  8.684837  9.017042  8.406961  9.477079  8.739632  8.557627
6379 more rows ...

> show(MA)
An object of class "MAList"
$M
            [,1]        [,2]       [,3]        [,4]       [,5]       [,6]
[1,] -0.22060913 -0.97047013 -0.7132995  0.05299212 -0.4035381  0.8835727
[2,]  0.06737471  0.13335374  0.2444237  0.74556284  0.6523577  1.3419787
[3,] -0.23126298 -0.81105738 -0.8420205 -0.09262048 -0.4281592 -0.3253819
[4,] -0.17305532 -0.07044322 -0.3286331  0.20358545  0.2037528 -0.1023735
[5,] -0.84106756 -0.10980624 -0.1029215 -0.04902437  0.2820152 -0.2241410
6379 more rows ...

$A
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]
[1,] 11.332980 11.198841 11.337353  9.693899 11.196822 10.506374
[2,] 11.245664 11.074098 11.051345 10.931562 11.273305 10.008818
[3,] 10.113995 10.923628 12.322088  9.875351 11.096463 10.829522
[4,]  8.390963  9.019036  8.720987  9.774672  8.826249  9.113240
[5,]  8.684837  9.017042  8.406961  9.477079  8.739632  8.557627
6379 more rows ...

> plot.scale.box(MA$M)
> 
> #  One can get the same results using the matrix method:
> M <- normalizeBetweenArrays(MA$M,method="scale")
> plot.scale.box(M)
> 
> #  MpAq normalization as in Yang and Thorne (2003):
> MpAq <- normalizeWithinArrays(mouse.data, mouse.setup)
> MpAq <- normalizeBetweenArrays(MpAq, method="Aq")
> plotDensities(MpAq)
> 
> 
> 
> cleanEx(); ..nameEx <- "normalizeprintorder"
> 
> ### * normalizeprintorder
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normalizeForPrintorder
> ### Title: Print-Order Normalization
> ### Aliases: normalizeForPrintorder normalizeForPrintorder.rg
> ###   plotPrintorder
> ### Keywords: models
> 
> ### ** Examples
> 
> library(sma)
> data(MouseArray)
> plotPrintorder(mouse.data,mouse.setup,slide=1,separate=TRUE)
> RG <- normalizeForPrintorder(mouse.data,mouse.setup)
> 
> 
> 
> cleanEx(); ..nameEx <- "normalizescale"
> 
> ### * normalizescale
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normalizeScale
> ### Title: Normalize Columns of a Matrix to have the Same Scale
> ### Aliases: normalizeMedianDeviations normalizeMedians
> ### Keywords: array
> 
> ### ** Examples
> 
> M <- cbind(Array1=rnorm(10),Array2=2*rnorm(10))
> normalizeMedianDeviations(M)
          Array1      Array2
 [1,] -0.9704239  1.95185021
 [2,]  0.2844773  0.50332391
 [3,] -1.2944514 -0.80207942
 [4,]  2.4712096 -2.85938370
 [5,]  0.5104322  1.45239053
 [6,] -1.2709671 -0.05801347
 [7,]  0.7550642 -0.02090314
 [8,]  1.1437203  1.21858040
 [9,]  0.8919285  1.06027300
[10,] -0.4730695  0.76678189
> 
> A <- cbind(Array1=rlnorm(10),Array2=2*rlnorm(10))
> normalizeMedians(A)
         Array1    Array2
 [1,] 3.4266990 5.6928256
 [2,] 2.9884544 1.3201422
 [3,] 1.4728289 2.1558795
 [4,] 0.1869841 1.3864162
 [5,] 2.5407156 0.3691573
 [6,] 1.2923875 0.9661198
 [7,] 1.1697903 0.9863315
 [8,] 0.3140722 1.3788003
 [9,] 0.8474444 4.3953736
[10,] 2.0762463 3.1383680
> 
> 
> 
> cleanEx(); ..nameEx <- "normexpfit"
> 
> ### * normexpfit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: normexp.fit
> ### Title: Fit Normal+Exp Convolution Model to Observed Intensities
> ### Aliases: normexp.fit
> ### Keywords: models
> 
> ### ** Examples
> 
> f <- c(2,3,1,10,3,20,5,6)
> b <- c(2,2,2,2,2,2,2,2)
> out <- normexp.fit(f,b)
> 
> 
> 
> cleanEx(); ..nameEx <- "plotDensities"
> 
> ### * plotDensities
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotDensities
> ### Title: Individual-channel Densities Plot
> ### Aliases: plotDensities
> ### Keywords: hplot
> 
> ### ** Examples
> 
> library(sma)
> data(MouseArray)
> 
> #  no normalization but background correction is done
> MA.n <- MA.RG(mouse.data)
> 
> #  Default settings for plotDensities.
> plotDensities(MA.n)
> 
> #  One can reproduce the default settings.
> plotDensities(MA.n,arrays=c(1:6),groups=c(rep(1,6),rep(2,6)),
+ col=c("red","green"))
> 
> #  Color R and G individual-channels by blue and purple.
> plotDensities(MA.n,arrays=NULL,groups=NULL,col=c("blue","purple"))
> 
> #  Indexing individual-channels using singlechannels (arrays=NULL).
> plotDensities(MA.n,singlechannels=c(1,2,7))
> 
> #  Change the default colors from c("red","green") to c("pink","purple")
> plotDensities(MA.n,singlechannels=c(1,2,7),col=c("pink","purple"))
> 
> #  Specified too many colors since groups=NULL defaults to two groups.
> plotDensities(MA.n,singlechannels=c(1,2,7),col=c("pink","purple","blue"))
Warning in plotDensities(MA.n, singlechannels = c(1, 2, 7), col = c("pink",  : 
	 number of groups=2 not equal to number of col
> 
> #  Three individual-channels, three groups, three colors.
> plotDensities(MA.n,singlechannels=c(1,2,7),groups=c(1,2,3),
+ col=c("pink","purple","blue"))
> 
> #  Three individual-channels, one group, one color.
> plotDensities(MA.n,singlechannels=c(1,2,7),groups=c(1,1,1),
+ col=c("purple"))
> 
> #  All individual-channels, three groups (ctl,tmt,reference), three colors.
> plotDensities(MA.n,singlechannels=c(1:12),
+ groups=c(rep(1,3),rep(2,3),rep(3,6)),col=c("darkred","red","green"))
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "plotma"
> 
> ### * plotma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotMA
> ### Title: MA-Plot
> ### Aliases: plotMA
> ### Keywords: hplot
> 
> ### ** Examples
> 
> MA <- new("MAList")
> MA$A <- runif(300,4,16)
> MA$M <- rt(300,df=3)
> status <- rep("Gene",300)
> status[1:3] <- "M=0"
> MA$M[1:3] <- 0
> status[4:6] <- "M=3"
> MA$M[4:6] <- 3
> status[7:9] <- "M=-3"
> MA$M[7:9] <- -3
> plotMA(MA,main="MA-Plot with Simulated Data",status=status,values=c("M=0","M=3","M=-3"),col=c("blue","red","green"))
> 
> #  Same as above
> attr(status,"values") <- c("M=0","M=3","M=-3")
> attr(status,"col") <- c("blue","red","green")
> plotMA(MA,main="MA-Plot with Simulated Data",status=status)
> 
> #  Same as above
> MA$genes$Status <- status
> plotMA(MA,main="MA-Plot with Simulated Data")
> 
> 
> 
> cleanEx(); ..nameEx <- "poolvar"
> 
> ### * poolvar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: poolVar
> ### Title: Pool Sample Variances with Unequal Variances
> ### Aliases: poolVar
> ### Keywords: htest
> 
> ### ** Examples
> 
> #  Welch's t-test with unequal variances
> x <- rnorm(10,mean=1,sd=2)
> y <- rnorm(20,mean=2,sd=1)
> s2 <- c(var(x),var(y))
> n <- c(10,20)
> out <- poolVar(var=s2,n=n)
> tstat <- (mean(x)-mean(y)) / sqrt(out$var*out$multiplier)
> pvalue <- 2*pt(-abs(tstat),df=out$df)
> #  Equivalent to t.test(x,y)
> 
> 
> 
> cleanEx(); ..nameEx <- "printorder"
> 
> ### * printorder
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: printorder
> ### Title: Identify Order in which Spots were Printed
> ### Aliases: printorder
> ### Keywords: IO
> 
> ### ** Examples
> 
> printorder(list(ngrid.r=2,ngrid.c=2,nspot.r=12,nspot.c=8))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
 [51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
 [76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
[151] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
[176] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
[251] 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
[276] 84 85 86 87 88 89 90 91 92 93 94 95 96  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62
[351] 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87
[376] 88 89 90 91 92 93 94 95 96

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  2  2  2  2  2  2  2  2  2  2  2  2  4  4  4  4  4  4  4  4  4  4  4  4  6
 [26]  6  6  6  6  6  6  6  6  6  6  6  8  8  8  8  8  8  8  8  8  8  8  8 10 10
 [51] 10 10 10 10 10 10 10 10 10 10 12 12 12 12 12 12 12 12 12 12 12 12 14 14 14
 [76] 14 14 14 14 14 14 14 14 14 16 16 16 16 16 16 16 16 16 16 16 16  1  1  1  1
[101]  1  1  1  1  1  1  1  1  3  3  3  3  3  3  3  3  3  3  3  3  5  5  5  5  5
[126]  5  5  5  5  5  5  5  7  7  7  7  7  7  7  7  7  7  7  7  9  9  9  9  9  9
[151]  9  9  9  9  9  9 11 11 11 11 11 11 11 11 11 11 11 11 13 13 13 13 13 13 13
[176] 13 13 13 13 13 15 15 15 15 15 15 15 15 15 15 15 15  2  2  2  2  2  2  2  2
[201]  2  2  2  2  4  4  4  4  4  4  4  4  4  4  4  4  6  6  6  6  6  6  6  6  6
[226]  6  6  6  8  8  8  8  8  8  8  8  8  8  8  8 10 10 10 10 10 10 10 10 10 10
[251] 10 10 12 12 12 12 12 12 12 12 12 12 12 12 14 14 14 14 14 14 14 14 14 14 14
[276] 14 16 16 16 16 16 16 16 16 16 16 16 16  1  1  1  1  1  1  1  1  1  1  1  1
[301]  3  3  3  3  3  3  3  3  3  3  3  3  5  5  5  5  5  5  5  5  5  5  5  5  7
[326]  7  7  7  7  7  7  7  7  7  7  7  9  9  9  9  9  9  9  9  9  9  9  9 11 11
[351] 11 11 11 11 11 11 11 11 11 11 13 13 13 13 13 13 13 13 13 13 13 13 15 15 15
[376] 15 15 15 15 15 15 15 15 15

$plate.c
  [1]  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1
 [26]  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3
 [51]  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5
 [76]  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7
[101]  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9
[126] 11 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11
[151] 13 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  1  3  5  7  9 11 13
[176] 15 17 19 21 23  1  3  5  7  9 11 13 15 17 19 21 23  2  4  6  8 10 12 14 16
[201] 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18
[226] 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20
[251] 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22
[276] 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24
[301]  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2
[326]  4  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4
[351]  6  8 10 12 14 16 18 20 22 24  2  4  6  8 10 12 14 16 18 20 22 24  2  4  6
[376]  8 10 12 14 16 18 20 22 24

$plateposition
  [1] "p1B01" "p1B03" "p1B05" "p1B07" "p1B09" "p1B11" "p1B13" "p1B15" "p1B17"
 [10] "p1B19" "p1B21" "p1B23" "p1D01" "p1D03" "p1D05" "p1D07" "p1D09" "p1D11"
 [19] "p1D13" "p1D15" "p1D17" "p1D19" "p1D21" "p1D23" "p1F01" "p1F03" "p1F05"
 [28] "p1F07" "p1F09" "p1F11" "p1F13" "p1F15" "p1F17" "p1F19" "p1F21" "p1F23"
 [37] "p1H01" "p1H03" "p1H05" "p1H07" "p1H09" "p1H11" "p1H13" "p1H15" "p1H17"
 [46] "p1H19" "p1H21" "p1H23" "p1J01" "p1J03" "p1J05" "p1J07" "p1J09" "p1J11"
 [55] "p1J13" "p1J15" "p1J17" "p1J19" "p1J21" "p1J23" "p1L01" "p1L03" "p1L05"
 [64] "p1L07" "p1L09" "p1L11" "p1L13" "p1L15" "p1L17" "p1L19" "p1L21" "p1L23"
 [73] "p1N01" "p1N03" "p1N05" "p1N07" "p1N09" "p1N11" "p1N13" "p1N15" "p1N17"
 [82] "p1N19" "p1N21" "p1N23" "p1P01" "p1P03" "p1P05" "p1P07" "p1P09" "p1P11"
 [91] "p1P13" "p1P15" "p1P17" "p1P19" "p1P21" "p1P23" "p1A01" "p1A03" "p1A05"
[100] "p1A07" "p1A09" "p1A11" "p1A13" "p1A15" "p1A17" "p1A19" "p1A21" "p1A23"
[109] "p1C01" "p1C03" "p1C05" "p1C07" "p1C09" "p1C11" "p1C13" "p1C15" "p1C17"
[118] "p1C19" "p1C21" "p1C23" "p1E01" "p1E03" "p1E05" "p1E07" "p1E09" "p1E11"
[127] "p1E13" "p1E15" "p1E17" "p1E19" "p1E21" "p1E23" "p1G01" "p1G03" "p1G05"
[136] "p1G07" "p1G09" "p1G11" "p1G13" "p1G15" "p1G17" "p1G19" "p1G21" "p1G23"
[145] "p1I01" "p1I03" "p1I05" "p1I07" "p1I09" "p1I11" "p1I13" "p1I15" "p1I17"
[154] "p1I19" "p1I21" "p1I23" "p1K01" "p1K03" "p1K05" "p1K07" "p1K09" "p1K11"
[163] "p1K13" "p1K15" "p1K17" "p1K19" "p1K21" "p1K23" "p1M01" "p1M03" "p1M05"
[172] "p1M07" "p1M09" "p1M11" "p1M13" "p1M15" "p1M17" "p1M19" "p1M21" "p1M23"
[181] "p1O01" "p1O03" "p1O05" "p1O07" "p1O09" "p1O11" "p1O13" "p1O15" "p1O17"
[190] "p1O19" "p1O21" "p1O23" "p1B02" "p1B04" "p1B06" "p1B08" "p1B10" "p1B12"
[199] "p1B14" "p1B16" "p1B18" "p1B20" "p1B22" "p1B24" "p1D02" "p1D04" "p1D06"
[208] "p1D08" "p1D10" "p1D12" "p1D14" "p1D16" "p1D18" "p1D20" "p1D22" "p1D24"
[217] "p1F02" "p1F04" "p1F06" "p1F08" "p1F10" "p1F12" "p1F14" "p1F16" "p1F18"
[226] "p1F20" "p1F22" "p1F24" "p1H02" "p1H04" "p1H06" "p1H08" "p1H10" "p1H12"
[235] "p1H14" "p1H16" "p1H18" "p1H20" "p1H22" "p1H24" "p1J02" "p1J04" "p1J06"
[244] "p1J08" "p1J10" "p1J12" "p1J14" "p1J16" "p1J18" "p1J20" "p1J22" "p1J24"
[253] "p1L02" "p1L04" "p1L06" "p1L08" "p1L10" "p1L12" "p1L14" "p1L16" "p1L18"
[262] "p1L20" "p1L22" "p1L24" "p1N02" "p1N04" "p1N06" "p1N08" "p1N10" "p1N12"
[271] "p1N14" "p1N16" "p1N18" "p1N20" "p1N22" "p1N24" "p1P02" "p1P04" "p1P06"
[280] "p1P08" "p1P10" "p1P12" "p1P14" "p1P16" "p1P18" "p1P20" "p1P22" "p1P24"
[289] "p1A02" "p1A04" "p1A06" "p1A08" "p1A10" "p1A12" "p1A14" "p1A16" "p1A18"
[298] "p1A20" "p1A22" "p1A24" "p1C02" "p1C04" "p1C06" "p1C08" "p1C10" "p1C12"
[307] "p1C14" "p1C16" "p1C18" "p1C20" "p1C22" "p1C24" "p1E02" "p1E04" "p1E06"
[316] "p1E08" "p1E10" "p1E12" "p1E14" "p1E16" "p1E18" "p1E20" "p1E22" "p1E24"
[325] "p1G02" "p1G04" "p1G06" "p1G08" "p1G10" "p1G12" "p1G14" "p1G16" "p1G18"
[334] "p1G20" "p1G22" "p1G24" "p1I02" "p1I04" "p1I06" "p1I08" "p1I10" "p1I12"
[343] "p1I14" "p1I16" "p1I18" "p1I20" "p1I22" "p1I24" "p1K02" "p1K04" "p1K06"
[352] "p1K08" "p1K10" "p1K12" "p1K14" "p1K16" "p1K18" "p1K20" "p1K22" "p1K24"
[361] "p1M02" "p1M04" "p1M06" "p1M08" "p1M10" "p1M12" "p1M14" "p1M16" "p1M18"
[370] "p1M20" "p1M22" "p1M24" "p1O02" "p1O04" "p1O06" "p1O08" "p1O10" "p1O12"
[379] "p1O14" "p1O16" "p1O18" "p1O20" "p1O22" "p1O24"

> 
> 
> 
> cleanEx(); ..nameEx <- "protectMetachar"
> 
> ### * protectMetachar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: protectMetachar
> ### Title: Protect Metacharacters
> ### Aliases: protectMetachar
> ### Keywords: character
> 
> ### ** Examples
> 
> # without protectMetachar, this would be no match
> grep(protectMetachar("Ch1 (mean)"),"Ch1 (mean)")
[1] 1
> 
> 
> 
> cleanEx(); ..nameEx <- "qqt"
> 
> ### * qqt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qqt
> ### Title: Student's t Quantile-Quantile Plot
> ### Aliases: qqt
> ### Keywords: distribution
> 
> ### ** Examples
> 
> y <- rt(50,df=4)
> qqt(y,df=4)
> 
> 
> 
> cleanEx(); ..nameEx <- "qualwt"
> 
> ### * qualwt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: QualityWeights
> ### Title: Spot Quality Weights
> ### Aliases: QualityWeights wtarea wtflags wtIgnore.Filter
> ### Keywords: regression
> 
> ### ** Examples
> 
> #  Read in spot output files from current directory and give full weight to 165
> #  pixel spots.  Note: for this example to run you must set fnames to the names
> #  of actual spot output files (data not provided).
> ## Not run: 
> ##D RG <- read.maimages(fnames,source="spot",wt.fun=wtarea(165))
> ##D #  Spot will be downweighted according to weights found in RG
> ##D MA <- normalizeWithinArrays(RG,layout)
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "read.maimages"
> 
> ### * read.maimages
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.maimages
> ### Title: Read RGList from Image Analysis Output Files
> ### Aliases: read.maimages read.imagene
> ### Keywords: file
> 
> ### ** Examples
> 
> #  Read all .gpr files from current working directory
> #  and give weight 0.1 to spots with negative flags
> 
> ## Not run: 
> ##D files <- dir(pattern="*\\.gpr$")
> ##D RG <- read.maimages(files,"genepix",wt.fun=wtflags(0.1))
> ## End(Not run)
> 
> #  Read all .spot files from current working director and down-weight
> #  spots smaller or larger than 150 pixels
> 
> ## Not run: 
> ##D files <- dir(pattern="*\\.spot$")
> ##D RG <- read.maimages(files,"spot",wt.fun=wtarea(150))
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "readgal"
> 
> ### * readgal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: readGAL
> ### Title: Read a GAL file
> ### Aliases: readGAL
> ### Keywords: IO
> 
> ### ** Examples
> 
> # readGAL()
> # will read in the first GAL file (with suffix ".gal")
> # found in the current working directory
> 
> 
> 
> cleanEx(); ..nameEx <- "removeext"
> 
> ### * removeext
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: removeExt
> ### Title: Remove Common Extension from File Names
> ### Aliases: removeExt
> ### Keywords: character file
> 
> ### ** Examples
> 
> x <- c("slide1.spot","slide2.spot","slide3.spot")
> removeExt(x)
[1] "slide1" "slide2" "slide3"
> 
> 
> 
> cleanEx(); ..nameEx <- "splitName"
> 
> ### * splitName
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: splitName
> ### Title: Split Composite Gene Names
> ### Aliases: splitName
> ### Keywords: character
> 
> ### ** Examples
> 
> x <- c("AA196000;actinin, alpha 3",
+ "AA464163;acyl-Coenzyme A dehydrogenase, very long chain",
+ "3E7;W15277;No Annotation")
> splitName(x)
$Name
[1] "AA196000"   "AA464163"   "3E7;W15277"

$Annotation
[1] "actinin, alpha 3"                              
[2] "acyl-Coenzyme A dehydrogenase, very long chain"
[3] "No Annotation"                                 

> 
> 
> 
> cleanEx(); ..nameEx <- "squeezeVar"
> 
> ### * squeezeVar
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: squeezeVar
> ### Title: Smooth Sample Variances
> ### Aliases: squeezeVar
> ### Keywords: htest
> 
> ### ** Examples
> 
> s2 <- rchisq(20,df=5)/5
> squeezeVar(s2, df=5)
$var.prior
[1] 0.974112

$df.prior
[1] 22.36542

$var.post
 [1] 0.8847212 1.1120548 1.1033975 0.9882973 1.1826976 1.0285712 1.0078684
 [8] 0.9124394 0.8713805 0.8851412 0.9130092 0.9376922 0.8646479 1.0395013
[15] 1.0101285 1.0527134 1.0343166 0.9501080 0.8089935 0.9989853

> 
> 
> 
> cleanEx(); ..nameEx <- "subsetting"
> 
> ### * subsetting
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: subsetting
> ### Title: Subset RGList, MAList or MArrayLM Objects
> ### Aliases: [.RGList [.MAList [.MArrayLM
> ### Keywords: manip
> 
> ### ** Examples
> 
> M <- A <- matrix(11:14,4,2)
> rownames(M) <- rownames(A) <- c("a","b","c","d")
> colnames(M) <- colnames(A) <- c("A","B")
> MA <- new("MAList",list(M=M,A=A))
> MA[1:2,]
An object of class "MAList"
$M
   A  B
a 11 11
b 12 12

$A
   A  B
a 11 11
b 12 12

> MA[1:2,2]
An object of class "MAList"
$M
   B
a 11
b 12

$A
   B
a 11
b 12

> MA[,2]
An object of class "MAList"
$M
   B
a 11
b 12
c 13
d 14

$A
   B
a 11
b 12
c 13
d 14

> 
> 
> 
> cleanEx(); ..nameEx <- "targetsA2C"
> 
> ### * targetsA2C
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: targetsA2C
> ### Title: Convert Two-Color Targets Dataframe from One-Row-Per-Array to
> ###   One-Row-Per-Channel
> ### Aliases: targetsA2C array2channel
> ### Keywords: htest
> 
> ### ** Examples
> 
> targets <- data.frame(FileName=c("file1.gpr","file2.gpr"),Cy3=c("WT","KO"),Cy5=c("KO","WT"))
> targetsA2C(targets)
    channel.col  FileName Target
1.1           1 file1.gpr     WT
1.2           2 file1.gpr     KO
2.1           1 file2.gpr     KO
2.2           2 file2.gpr     WT
> 
> 
> 
> cleanEx(); ..nameEx <- "toptable"
> 
> ### * toptable
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: toptable
> ### Title: Table of Top Genes from Linear Model Fit
> ### Aliases: toptable topTable
> ### Keywords: htest
> 
> ### ** Examples
> 
> #  Simulate gene expression data,
> #  6 microarrays and 100 genes with first gene differentially expressed
> M <- matrix(rnorm(100*6,sd=0.3),100,6)
> M[1,1:3] <- M[1,1:3] + 2
> #  Design matrix includes two treatments, one for first 3 and one for last 3 arrays
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> fit <- lmFit(M, design=design)
> fit <- eBayes(fit)
> topTable(fit)
            M           A         t      P.Value         B
1   1.9162581  1.06039996 10.679009 1.276272e-24 49.435947
14 -0.4235604 -0.28080041 -2.360436 1.000000e+00 -4.582482
61  0.3777731 -0.02414989  2.105271 1.000000e+00 -5.149957
84 -0.3692048 -0.11059707 -2.057521 1.000000e+00 -5.248946
32 -0.3580603 -0.17397464 -1.995414 1.000000e+00 -5.374301
46 -0.3458479 -0.39706051 -1.927357 1.000000e+00 -5.507256
6   0.3444480  0.30875016  1.919555 1.000000e+00 -5.522202
28 -0.3113900 -0.12713827 -1.735328 1.000000e+00 -5.857523
78  0.2817352  0.24649089  1.570066 1.000000e+00 -6.129562
83  0.2722270  0.14331360  1.517079 1.000000e+00 -6.211025
> 
> 
> 
> cleanEx(); ..nameEx <- "trigammainverse"
> 
> ### * trigammainverse
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trigammaInverse
> ### Title: Inverse Trigamma Function
> ### Aliases: trigammaInverse
> ### Keywords: math
> 
> ### ** Examples
> 
> y <- trigammaInverse(5)
> trigamma(y)
[1] 5
> 
> 
> 
> cleanEx(); ..nameEx <- "trimWhiteSpace"
> 
> ### * trimWhiteSpace
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: trimWhiteSpace
> ### Title: Trim Leading and Trailing White Space
> ### Aliases: trimWhiteSpace
> ### Keywords: character
> 
> ### ** Examples
> 
> x <- c("a "," b ")
> trimWhiteSpace(x)
[1] "a" "b"
> 
> 
> 
> cleanEx(); ..nameEx <- "uniquegenelist"
> 
> ### * uniquegenelist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: uniquegenelist
> ### Title: Eliminate Duplicate Names from the Gene List
> ### Aliases: uniquegenelist
> ### Keywords: array
> 
> ### ** Examples
> 
> genelist <- c("A","A","B","B","C","C","D","D")
> uniquegenelist(genelist,ndups=2)
[1] "A" "B" "C" "D"
> genelist <- c("A","B","A","B","C","D","C","D")
> uniquegenelist(genelist,ndups=2,spacing=2)
[1] "A" "B" "C" "D"
> 
> 
> 
> cleanEx(); ..nameEx <- "unwrapdups"
> 
> ### * unwrapdups
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: unwrapdups
> ### Title: Unwrap Duplicate Spot Values from Rows into Columns
> ### Aliases: unwrapdups
> ### Keywords: array
> 
> ### ** Examples
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> 
> 
> cleanEx(); ..nameEx <- "venn"
> 
> ### * venn
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: venn
> ### Title: Venn Diagrams
> ### Aliases: vennCounts vennDiagram
> ### Keywords: htest
> 
> ### ** Examples
> 
> tstat <- matrix(rt(300,df=10),100,3)
> tstat[1:33,] <- tstat[1:33,]+2
> clas <- classifyTestsF(tstat,df=10,p.value=0.05)
> a <- vennCounts(clas)
> print(a)
     Group 1 Group 2 Group 3 Counts
[1,]       0       0       0     74
[2,]       0       0       1      2
[3,]       0       1       0      3
[4,]       0       1       1      4
[5,]       1       0       0      0
[6,]       1       0       1      2
[7,]       1       1       0      7
[8,]       1       1       1      8
attr(,"class")
[1] "VennCounts"
> vennDiagram(a)
> 
> 
> 
> cleanEx(); ..nameEx <- "zscore"
> 
> ### * zscore
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: zscore
> ### Title: z-score equivalents
> ### Aliases: zscoreGamma zscoreT tZscore
> ### Keywords: distribution
> 
> ### ** Examples
> 
> zscoreGamma(1, shape=1, scale=1)
[1] 0.3374750
> zscoreT(2, df=3)
[1] 1.478306
> tZscore(2, df=3)
[1] 3.306822
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  84.76 2.71 88.18 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')