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("sma-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('sma')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "id2image"
> 
> ### * id2image
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: id2image
> ### Title: Converting an id tag to a Set of Image Coordinates and Vice
> ###   Versa
> ### Aliases: id2image image2id
> ### Keywords: attribute
> 
> ### ** Examples
> data(MouseArray)
> # mouse.setup <- init.grid()
> 
> id2image(1024, mouse.setup)
[1]  1  3 11 16
> ## You will see: [1]  1 3 11 16
> ## the grid in which gene 1024 can be found, is in row 1, column 3
> ## and the gene is located in row 11, column 16 of this particular grid.
>  
> image2id(c(2,4,6,8), mouse.setup)
[1] 2906
> ## You will see: [1] 2906
> ## the gene located in row 6, column 8 in the grid that is in row 2 and
> ## column 4 is the 2906th gene of the data set.  
> 
> 
> 
> cleanEx(); ..nameEx <- "init.addinfo"
> 
> ### * init.addinfo
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.addinfo
> ### Title: Adding Information to a Data Structure for Multi-slide
> ###   Microarray Experiments
> ### Aliases: init.addinfo
> ### Keywords: manip
> 
> ### ** Examples
> 
> ## mouse.data <- init.addinfo("mouse", "area")
> 
> 
> 
> cleanEx(); ..nameEx <- "init.ctl.index"
> 
> ### * init.ctl.index
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.ctl.index
> ### Title: Generates co-ordinates of spots.
> ### Aliases: init.ctl.index
> ### Keywords: manip
> 
> ### ** Examples
> 
>    x <- init.ctl.index(1:4, 1:4, 1:2, 15:20)
> ## Generates the 4 co-ordinates index to spots in the first 2 rows,
> ## columns 15 to 20 of every print-tips groups.
> 
> 
> 
> cleanEx(); ..nameEx <- "init.data"
> 
> ### * init.data
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.data
> ### Title: Creating a Data Structure for Multi-slide Microarray Experiments
> ### Aliases: init.data
> ### Keywords: attribute
> 
> ### ** Examples
> 
> ## mouse.data <- init.data()
> 
> ## Here is what you should see:
> ## Are you creating a new data matrix or adding new array data
> ## to a prexisting data matrix? 
> ## Enter "n" for creating and "a" for adding new array data: n
> ## Do the names of all your datasets have the following format: 
> ## prefix1, prefix2, prefix3?, ... Here prefix can be any name, 
> ## but the suffixes must be integers 1,2, ..., # of arrays. 
> ## Enter "y" for yes, "n" for no: y
> ## Enter the prefix:mouse
> ## Enter the number of arrays to be processed:6
> ## Enter the name of Cy3 raw data: Gmean
> ## Enter the name of Cy3 background: morphG
> ## Enter the name of Cy5 raw data: Rmean
> ## Enter the name of Cy5 background: morphR
> ## Finished creating new dataset.
> 
> 
> 
> cleanEx(); ..nameEx <- "init.grid"
> 
> ### * init.grid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.grid
> ### Title: Initialization of Grid Parameters
> ### Aliases: init.grid
> ### Keywords: misc
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> 
> ## Here is what you should see:
> # Enter number of rows of grids per image (ngrid.r): 4
> # Enter number of columns of grids per image (ngrid.c): 4
> # Enter number of rows of spots per grid (nspot.r): 19  
> # Enter number of columns of spots per grid (nspot.c): 21
> # Initialization complete
> 
> 
> 
> cleanEx(); ..nameEx <- "init.names"
> 
> ### * init.names
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.names
> ### Title: Set and Read the Names of Experimental Data.
> ### Aliases: init.name.exp init.show.exp
> ### Keywords: attribute
> 
> ### ** Examples
> 
> ## init.name.exp() ## To create the look-up table.
> 
> ## This is what you should see:
> ## Are you creating a new batch.exp file or adding new data names
> ## to a prexisting batch.exp file? 
> ## Enter "n" for creating  and "a" for adding new data names: n
> ## Enter the batch name for the new .exp file: mouse1
> ## Enter the number of names of files to be entered: 2
> ##  Enter the R name of your  1 th dataset: m1
> ##  Enter the actual file name including the full path name for m1 ?
> ##  ~/path/image1.data 
> ##  Enter the R name of your  2 th dataset: m2
> ##  Enter the actual file name including the full path name for m2 ?
> ##  ~/path/image2.data 
> ## Finished adding names to .exp file.
> ## NULL
> 
> ## View the look-up table.
> ## init.show.exp("mouse1") 
> ## 
> ##    Name in R           Filename
> ## 1        m1    ~/path/image1.data
> ## 2        m2    ~/path/image2.data
> 
> 
> 
> cleanEx(); ..nameEx <- "init.read.exp"
> 
> ### * init.read.exp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: init.read.exp
> ### Title: Reads the Output of the Computed Statistics
> ### Aliases: init.read.exp
> ### Keywords: file
> 
> ### ** Examples
> 
> ## apoa1.info <- init.read.exp("apoa1", 1) ## obtains the matrix
> ## of 30 measurements for all the genes spotted on slide 1 of the MouseArray
> ## experiment.
> 
> 
> cleanEx(); ..nameEx <- "is.odd"
> 
> ### * is.odd
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: is.odd
> ### Title: Determining if a Value is Odd or Even
> ### Aliases: is.odd is.even
> ### Keywords: logic
> 
> ### ** Examples
> 
> is.odd(4)
[1] FALSE
> ## FALSE
> is.even(100)
[1] TRUE
> ## TRUE
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.bayesian"
> 
> ### * plot.bayesian
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.bayesian
> ### Title: Plots lodscore vs effect estimate (the output of stat.bayesian()
> ###   or stat.bay.est()).
> ### Aliases: plot.bayesian
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid() 
> ## mouse.data <- init.data() ## see \emph{init.data} 
> ## mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> mouse.bayesian<-stat.bayesian(M=mouse.lratio$M)
> 
> plot.bayesian(mouse.bayesian)
> plot.bayesian(mouse.bayesian,type='c',spec=0,col='blue')
> plot.bayesian(mouse.bayesian,type='i',spec=c(1000,2000),col='green')
> plot.bayesian(mouse.bayesian,type='i',spec=1:length(mouse.bayesian$lods))
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.confband.lines"
> 
> ### * plot.confband.lines
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.confband.lines
> ### Title: Adding Lines Satisfying a Confidence Criterion to the Current M
> ###   vs A Plot
> ### Aliases: plot.confband.lines
> ### Keywords: aplot
> 
> ### ** Examples
> data(MouseArray)
> ## mouse.setup <- init.grid
> ## mouse.data <- init.data
> 
> ## To display an M vs A plot of the data 
> plot.mva(mouse.data, mouse.setup) 
> 
> ## Calculate M and A values 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## To add default upper and lower confidence curves line to the M vs A plot
> plot.confband.lines(mouse.lratio$A, mouse.lratio$M) 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.confband.points"
> 
> ### * plot.confband.points
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.confband.points
> ### Title: Highlights a Set of Points on the Current M vs A Plot
> ### Aliases: plot.confband.points
> ### Keywords: aplot
> 
> ### ** Examples
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data()
> 
> plot.mva(mouse.data, mouse.setup) ## an M vs A plot 
> 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> plot.confband.points(mouse.lratio$A, mouse.lratio$M)
> 
> ## 2.5% of the spots with the smallest and largest M values are
> ## highlighted on the M vs A plot. 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.confband.text"
> 
> ### * plot.confband.text
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.confband.text
> ### Title: Add Selected Text to an M vs A Plot
> ### Aliases: plot.confband.text
> ### Keywords: aplot
> 
> ### ** Examples
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data()
> 
> plot.mva(mouse.data, mouse.setup) ## an M vs A plot
> 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> plot.confband.text(mouse.lratio$A, mouse.lratio$M)
NULL
> ## 2.5% of the spots with the largest and smallest M values are
> ## highlighted on the M vs A plot, and each spot is assigned the
> ## default label of its corresponding index value. 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.mat"
> 
> ### * plot.mat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.mat
> ### Title: Red and Green Color Image of Data Matrix
> ### Aliases: plot.mat
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> ##mouse.setup <- init.grid()
> ##mouse.data <- init.data() ## see \emph{init.data}
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## Looking at log ratios of mouse1
> plot.mat(spatial.func(mouse.lratio$M[,1], mouse.setup))
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.mva"
> 
> ### * plot.mva
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.mva
> ### Title: M vs. A Plot
> ### Aliases: plot.mva
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## Look at the normalized second data sets in the list using points to
> ## highlight large positive or large negative ratios.
> plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="pci",
+ plot.type="n") 
> 
> ## Look at the both unnormalized and normalized first data sets in the
> ## list using text to highlight large positive or negative ratios.
> ## plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="tci", plot.type="b") 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.print.tip.lowess"
> 
> ### * plot.print.tip.lowess
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.print.tip.lowess
> ### Title: M vs. A Plot with print tip lowess lines
> ### Aliases: plot.print.tip.lowess
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> plot.print.tip.lowess(mouse.data,mouse.setup)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.qq"
> 
> ### * plot.qq
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.qq
> ### Title: Histogram and Normal Quantile-Quantile plot
> ### Aliases: plot.qq
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data() ## see \emph{init.data}
> ## mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## Calculation of t-statistics
> ## cl <- c(rep(1,3), rep(2,3))
> ## mouse.t2 <- stat.t2(mouse.lratio, cl)
> 
> ## Diagnostic plots
> plot.qq(mouse.t2$t, "Mouse")
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.qqline"
> 
> ### * plot.qqline
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.qqline
> ### Title: Add Line Going Through the Quantiles of a Q-Q Plot
> ### Aliases: plot.qqline
> ### Keywords: aplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> # mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## Calculation of t-statistics
> ## cl <- c(rep(1,3), rep(2,3))
> ## mouse.t2 <- stat.t2(mouse.lratio, cl)
> 
> ## Diagnostic plots
> plot.qq(mouse.t2$t, "Mouse")
> 
> ## Using the QQline function
> q <- quantile(rnorm(1000))
> plot.qqline(q, mouse.t2$t)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.scale.box"
> 
> ### * plot.scale.box
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.scale.box
> ### Title: Box plots for microarray
> ### Aliases: plot.scale.box
> ### Keywords: hplot
> 
> ### ** Examples
> 
>      data(MouseArray)
>      # mouse.setup <- init.grid() 
>      # mouse.data <- init.data() ## see \emph{init.data} 
>      mouse.lratio <- stat.ma(mouse.data, mouse.setup)
>      ## Producing boxplots for different print-tips groups.
>      plot.scale.box(mouse.lratio$M[,1], mouse.setup)
>      ## Producing boxplots for different slides.
>      plot.scale.box(mouse.lratio$M)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.single.slide"
> 
> ### * plot.single.slide
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.single.slide
> ### Title: M vs. A Plot
> ### Aliases: plot.single.slide
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> plot.single.slide(mouse.data, mouse.setup, norm="p", image.id=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.smooth.line"
> 
> ### * plot.smooth.line
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.smooth.line
> ### Title: Adding Lowess Lines to Current Plot
> ### Aliases: plot.smooth.line
> ### Keywords: aplot
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data()
> 
> plot.mva(mouse.data, mouse.setup)
> plot.smooth.line(mouse.lratio$A, mouse.lratio$M)
Warning in approx(lowess(x[ind], M[ind], f = f)) : 
	 collapsing to unique 'x' values
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.spatial"
> 
> ### * plot.spatial
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.spatial
> ### Title: Spatial Representation of Microarray Spot Statistics
> ### Aliases: plot.spatial draw.image.func spatial.func
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> plot.spatial(mouse.lratio$M[,1], mouse.setup) ## default 85
      Min.    1st Qu.     Median       Mean    3rd Qu.       Max.       NA's 
  -1.43300   -0.61770    0.07446    0.20780    0.90280    3.16700 5746.00000 
> 
> # Looking for areas where the spots are not quite circular
> plot.spatial(mouse1[,"shape"], mouse.setup, crit1=0.1)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
   0.1312    0.5729    1.5710       Inf    1.7930       Inf 5093.0000 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.svb"
> 
> ### * plot.svb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.svb
> ### Title: Plot of Signal vs. Background
> ### Aliases: plot.svb svb.func
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> 
> plot.svb(mouse.data, "green", 3) 
> ## thiscreates a plot of the signal versus background intensities 
> ## for the green channel, using data collected from the third slide. 
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.t2"
> 
> ### * plot.t2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.t2
> ### Title: Diagnostic Plots for Two-Sample t-statistics
> ### Aliases: plot.t2
> ### Keywords: hplot
> 
> ### ** Examples
> 
> data(MouseArray)
> # mouse.setup <- init.grid()
> # mouse.data <- init.data() ## see \emph{init.data}
> # mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> ## Calculation of t-statistics
> ## cl <- c(rep(1,3), rep(2,3))
> ## mouse.t2 <- stat.t2(mouse.lratio, cl)
> 
> ## Diagnostic plots
> plot.t2(mouse.t2, "Mouse")
> 
> 
> 
> cleanEx(); ..nameEx <- "read.spot"
> 
> ### * read.spot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: read.spot
> ### Title: Reading in Data Generated by the Image Analysis Package Spot
> ### Aliases: read.spot
> ### Keywords: file
> 
> ### ** Examples
> 
> 
> ## write.spot(mouse.array, 1, "mouse")
> ## mouse1 <- read.spot("mouse.1.spot")
> 
> 
> 
> cleanEx(); ..nameEx <- "rgcolors.func"
> 
> ### * rgcolors.func
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rgcolors.func
> ### Title: Red and Green Color Specification
> ### Aliases: rgcolors.func
> ### Keywords: color
> 
> ### ** Examples
> 
> rgcolors.func(n=5)
[1] "#00FF00" "#000000" "#000000" "#FF0000"
> ## The following vector is returned:
> ## "#00FF00" "#40BF00" "#808000" "#BF4000" "#FF0000"
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.Chen"
> 
> ### * stat.Chen
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.Chen
> ### Title: Apply Chen's single slide method
> ### Aliases: stat.Chen
> ### Keywords: models
> 
> ### ** Examples
> 
> data(MouseArray)
> ##mouse.setup <- init.grid() 
> ##mouse.data <- init.data() ## see \emph{init.data}
> stat.Chen(mouse.data,mouse.setup,image.id=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.ChurSap"
> 
> ### * stat.ChurSap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.ChurSap
> ### Title: Apply Sapir and Churchills single slide method
> ### Aliases: stat.ChurSap
> ### Keywords: models
> 
> ### ** Examples
> 
> data(MouseArray)
> ##mouse.setup <- init.grid() 
> ##mouse.data <- init.data() ## see \emph{init.data}
> stat.ChurSap(mouse.data,mouse.setup,pp=0.95,image.id=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.Newton"
> 
> ### * stat.Newton
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.Newton
> ### Title: Apply Newtons single slide method
> ### Aliases: stat.Newton
> ### Keywords: models
> 
> ### ** Examples
> 
> data(MouseArray)
> ##mouse.setup <- init.grid() 
> ##mouse.data <- init.data() ## see \emph{init.data}
> stat.Newton(mouse.data,mouse.setup,image.id=3)
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.bay.est"
> 
> ### * stat.bay.est
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.bay.est
> ### Title: Calculates an Odds Ratio for Each Gene in a Multi-slide
> ###   Microarray Experiment.
> ### Aliases: stat.bay.est
> ### Keywords: models
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid() 
> ## mouse.data <- init.data() ## see \emph{init.data} 
> ## mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> mouse.bayesian<-stat.bay.est(M=mouse.lratio$M)
> plot(mouse.bayesian$Xprep$Mbar, mouse.bayesian$lods)
> 
> #alternatively
> 
> mouse.est<-apply(mouse.lratio$M,1,mean.na)
> mouse.Vest<-apply(mouse.lratio$M,1,var.na)
> n<-ncol(mouse.lratio$M)
> k<-n
> f<-n-1
> mouse.Xprep<-list(Mbar=mouse.est,Vest=mouse.Vest,k=k,f=f)
> 
> mouse.bayest<-stat.bay.est(Xprep=mouse.Xprep)
> plot(mouse.bayest$Xprep$Mbar, mouse.bayest$lods)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.bayesian"
> 
> ### * stat.bayesian
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.bayesian
> ### Title: Calculates an Odds Ratio of Each Gene in a Multi-slide
> ###   microarray Experiment
> ### Aliases: stat.bayesian
> ### Keywords: models
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid() 
> ## mouse.data <- init.data() ## see \emph{init.data} 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> mouse.bayesian<-stat.bayesian(M=mouse.lratio$M)
> plot(mouse.bayesian$Xprep$Mbar, mouse.bayesian$lods)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.gnames"
> 
> ### * stat.gnames
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.gnames
> ### Title: Sort Genes According to the Value of a Statistic
> ### Aliases: stat.gnames
> ### Keywords: misc
> 
> ### ** Examples
> 
> ## Calculating log ratio and performing a t test.
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data() ## see \emph{init.data}
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> cl <- c(rep(1,3), rep(2,3))
> mouse.t2 <- stat.t2(mouse.lratio, cl)
> 
> ## Looking at gene names
> ## Finding the top 10 t-statistics
> stat.gnames(abs(mouse.t2$t), mouse.gnames, crit=10)
$gnames
 [1] "2537" "720"  "2149" "3136" "2270" "5762" "3607" "5356" "5954" "1372"

$t
 [1] 22.086256 18.773782 18.024979 17.873581 14.038102 10.859668 10.668207
 [8]  8.978152  8.272516  8.029156

> 
> ## Finding the top 1
> stat.gnames(abs(mouse.t2$t), mouse.gnames, crit=0.01)
$gnames
 [1] "2537" "720"  "2149" "3136" "2270" "5762" "3607" "5356" "5954" "1372"
[11] "4530" "1496" "438"  "2507" "2962" "751"  "1739" "1457" "4133" "1812"
[21] "3937" "4857" "3984" "2847" "1774" "6319" "6354" "4353" "634"  "6234"
[31] "1577" "2727" "2552" "1005" "4962" "6121" "1337" "1667" "5375" "3892"
[41] "834"  "2422" "1686" "2146" "3334" "3073" "4840" "3546" "3524" "3853"
[51] "55"   "5685" "4139" "2160" "815"  "95"   "1045" "885"  "6261" "4188"
[61] "5344" "1770" "3307" "2306"

$t
 [1] 22.086256 18.773782 18.024979 17.873581 14.038102 10.859668 10.668207
 [8]  8.978152  8.272516  8.029156  7.788586  7.729940  7.565154  7.269420
[15]  7.242933  7.172458  6.936318  6.850161  6.848099  6.741371  6.460019
[22]  6.281574  6.172339  6.171186  6.170007  6.035888  5.951187  5.938929
[29]  5.902411  5.717835  5.701596  5.643471  5.586883  5.583210  5.548203
[36]  5.528417  5.512230  5.488486  5.446824  5.413551  5.405699  5.402522
[43]  5.399490  5.348837  5.341991  5.234975  5.213604  5.203122  5.159127
[50]  5.145850  5.056717  5.050813  5.032236  4.998076  4.992609  4.979529
[57]  4.863267  4.858311  4.856797  4.803160  4.761582  4.736982  4.660011
[64]  4.643792

> 
> ## Finding the 10 extreme M values in the first slide
> stat.gnames(abs(mouse.lratio$M[, 1]), mouse.gnames, crit=10)
$gnames
 [1] "6067" "6173" "4638" "1062" "4676" "4659" "2001" "3544" "3577" "5470"

$t
 [1] 3.167494 3.090640 3.019157 2.969282 2.959414 2.763548 2.701883 2.679197
 [9] 2.545123 2.505968

> 
> 
> 
> cleanEx(); ..nameEx <- "stat.ma"
> 
> ### * stat.ma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.ma
> ### Title: Calculation of log Intensity Ratios and Average log Intensities
> ### Aliases: stat.ma ma.func norm.l.func norm.pin.func norm.scale.func
> ### Keywords: datagen
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid() 
> ## mouse.data <- init.data() ## see \emph{init.data} 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.norm.exp"
> 
> ### * stat.norm.exp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.norm.exp
> ### Title: Normalization of log Intensity Ratios across slides /
> ###   experiments.
> ### Aliases: stat.norm.exp
> ### Keywords: manip
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid() 
> ## mouse.data <- init.data() ## see \emph{init.data} 
> mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> mouse.norm.lratio <- stat.norm.exp(mouse.lratio$M)
> 
> 
> 
> cleanEx(); ..nameEx <- "stat.t2"
> 
> ### * stat.t2
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: stat.t2
> ### Title: Two-sample t-statistics
> ### Aliases: stat.t2 t2stat.func
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(MouseArray)
> ## mouse.setup <- init.grid()
> ## mouse.data <- init.data() ## see \emph{init.data}
> ## mouse.lratio <- stat.ma(mouse.data, mouse.setup)
> cl <- c(rep(1,3), rep(2,3))
> mouse.t2 <- stat.t2(mouse.lratio, cl)
> 
> 
> 
> cleanEx(); ..nameEx <- "write.spot"
> 
> ### * write.spot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: write.spot
> ### Title: Writing in Data Generated by the Image Analysis Package Spot
> ### Aliases: write.spot
> ### Keywords: file
> 
> ### ** Examples
> ## Setting up the data
> ## library(Spot)
> ## SetParameters("mouse")
> ## Here is what you should see:
> ## Enter number of rows of grids per image (ngrid.r): 4
> ## Enter number of columns of grids per image (ngrid.c): 4
> ## Enter number of rows of spots per grid (nspot.r): 19
> ## Enter number of columns of spots per grid (nspot.c): 21
> ## Enter top/bottom translation tolerance, default is 50 (tolerance.r): 20
> ## Enter left/right translation, default is 50 (tolerance.c): 30
> ## Initialization complete
> 
> ##Inputting Image Data 
> ## SetImages("mouse")
> ## Combining the red and green channels for the first slide
> ## mouse.array <- Spots("mouse", 1)
> 
> ## Calling the function to write the data in
> ## write.spot(mouse.array, 1, "mouse")
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  65.07 11.29 76.89 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')