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("SAGx-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('SAGx')
Loading required package: multtest
Loading required package: survival
Loading required package: splines
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Fstat"
> 
> ### * Fstat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Fstat
> ### Title: Calculation of F statistic by gene given a linear model
> ### Aliases: Fstat
> ### Keywords: models
> 
> ### ** Examples
> 
> ## Annette Dobson (1990) "An Introduction to Generalized Linear Models". 
> ## Page 9: Plant Weight Data. 
>  ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) 
>  trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) 
>  group <- gl(2,10,20, labels=c("Ctl","Trt")) 
>  weight <- c(ctl, trt) 
>  anova(lm.D9 <- lm(weight ~ group)) 
Analysis of Variance Table

Response: weight
          Df Sum Sq Mean Sq F value Pr(>F)
group      1 0.6882  0.6882  1.4191  0.249
Residuals 18 8.7293  0.4850               
> # Analysis of Variance Table
> 
> # Response: weight
> #          Df Sum Sq Mean Sq F value Pr(>F)
> #group      1 0.6882  0.6882  1.4191  0.249
> #Residuals 18 8.7292  0.4850               
> 
>  Fstat(indata = rbind(weight,weight),formula1=~group) # Fstat will need at least two genes to work with #
$Fstat
       weight   weight
[1,] 1.419101 1.419101

$fnum
[1] 18

$fdenom
[1] 1

$design1
   (Intercept) groupTrt
1            1        0
2            1        0
3            1        0
4            1        0
5            1        0
6            1        0
7            1        0
8            1        0
9            1        0
10           1        0
11           1        1
12           1        1
13           1        1
14           1        1
15           1        1
16           1        1
17           1        1
18           1        1
19           1        1
20           1        1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"


$design0
   (Intercept)
1            1
2            1
3            1
4            1
5            1
6            1
7            1
8            1
9            1
10           1
11           1
12           1
13           1
14           1
15           1
16           1
17           1
18           1
19           1
20           1
attr(,"assign")
[1] 0

$SS1
      weight  weight
[1,] 8.72925 8.72925

$SS0
       weight   weight
[1,] 0.688205 0.688205

$pvalue
NULL

> #$Fstat
> #  weight   weight 
> #1.419101 1.419101 
> 
> #$fnum
> #[1] 18
> 
> #$fdenom
> #[1] 1
> 
> #$design1
> #   (Intercept) groupTrt
> #1            1        0
> #2            1        0
> #3            1        0
> #4            1        0
> #5            1        0
> #6            1        0
> #7            1        0
> #8            1        0
> #9            1        0
> #10           1        0
> #11           1        1
> #12           1        1
> #13           1        1
> #14           1        1
> #15           1        1
> #16           1        1
> #17           1        1
> #18           1        1
> #19           1        1
> #20           1        1
> #attr(,"assign")
> #[1] 0 1
> 
> # $design0
> # NULL
> 
> # $SS1
> # weight  weight 
> #8.72925 8.72925 
> 
> #$SS0
> #  weight   weight 
> #0.688205 0.688205 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "fetchSignal"
> 
> ### * fetchSignal
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fetchSignal
> ### Title: Fetch data from the GATC database
> ### Aliases: fetchSignal
> ### Keywords: database
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # Do not run example 1. Fetch Probeset, Signal, ABS_CALL and CHIP for one sample.
> ##D library(RODBC)
> ##D (channel<-odbcConnect("DSN",uid="USERID",pwd="PASSWORD"))
> ##D ali.data <-fetchSignal(experiment="AZ33 ALI", channel, chip="hg_u95a")
> ##D  colnames(ali.data)
> ##D #[1] "FILENAME" "PROBESET" "SIGNAL" "ABS_CALL" "CHIP"
> ##D 
> ##D # Do not run example 2
> ##D t1 <- paste("select q1.name as name from experiment q1, physical_chip q2, chip_design q3")
> ##D t2 <- paste("where q1.physical_chip_id=q2.id and q3.id=q2.design_id and ")
> ##D t3 <- paste("upper(q1.name) like '
> ##D Ids <- sqlQuery(channel,paste(t1,t2,t3) )   
> ##D # fetch Signal from GATC corresponding to the U95A chip for all samples in experiment. #
> ##D tmp <- apply(Ids,1,toupper)
> ##D probes <- data.frame(fetchSignal(experiment=tmp[1],channel, chip="hg_u95a")[,"PROBESET"])
> ##D test <- matrix(nrow=nrow(as.data.frame(probes)),ncol=nrow(Ids))
> ##D for(i in 1:nrow(as.data.frame(tmp))){
> ##D    test[,i] <- fetchSignal(experiment=tmp[i],channel, chip="hg_u95a")[,"SIGNAL"]
> ##D }
> ##D codes <- data.frame(apply(Ids,1,code<-function(x) substr(x,1,5)))
> ##D colnames(test) <- as.character(t(codes))
> ##D test <- test[,order(colnames(test))]
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "firstpass"
> 
> ### * firstpass
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: firstpass
> ### Title: First pass description of GeneChip data
> ### Aliases: firstpass
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # not run
> ##D  g <- c(rep(1,4),rep(2,4)); labs <- c("Mean Diet","Mean Control"); probes <- paste("Probe",1:1000)
> ##D  firstpass(data = utmat[1:2,], probes = probes[1:2], g, log = FALSE, labels = labs)
> ##D #  Probesets         Mean Diet      Mean Control             LCL.1             LCL.2             UCL.1             UCL.2               pval
> ##D #1   Probe 1 -12.3444460036497 -11.7495704973055 -12.9047961446666 -12.2832657957485 -11.7840958626327 -11.2158751988625 0.0433081428107922
> ##D #2   Probe 2 -7.99773926405627 -8.02799133391929 -8.47704512876227 -8.19487551919835 -7.51843339935028 -7.86110714864023  0.772829992684449
> ##D #          Difference  Subject 1  Subject 2  Subject 3  Subject 4  Subject 5  Subject 6  Subject 7  Subject 8
> ##D #1 -0.594875506344176 -12.345150 -11.805071 -12.776232 -12.451332 -11.595748 -12.320430 -11.482349 -11.599755
> ##D #2 0.0302520698630131  -7.660097  -8.157944  -8.404433  -7.768484  -7.979951  -8.017327  -8.197361  -7.917326
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "gap"
> 
> ### * gap
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gap
> ### Title: GAP statistic clustering figure of merit
> ### Aliases: gap
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> library(MASS)
> data(swiss)
> cl <- myclus(data = swiss, k = 3)
> gap(swiss,cl$cluster)
[1] 0.8540805
> 
> 
> 
> cleanEx(); ..nameEx <- "list.experiments"
> 
> ### * list.experiments
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: list.experiments
> ### Title: Display all experiment names and id's
> ### Aliases: list.experiments
> ### Keywords: database
> 
> ### ** Examples
> 
> # Not run
> ## Not run: 
> ##D library(Rodbc)
> ##D channel <- odbcConnect(DBN, USRID, PWD) 
> ##D ut <- list.experiments(channel, chip = "hu6800")
> ##D colnames(ut)
> ##D #[1] "EXPERIMENT"  
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "myclus"
> 
> ### * myclus
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: myclus
> ### Title: A clustering function
> ### Aliases: myclus
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> library(MASS)
> data(swiss)
> cl <- myclus(data = swiss, k = 3)
> gap(swiss,cl$cluster)
[1] 0.8540805
> 
> 
> 
> cleanEx(); ..nameEx <- "outlier"
> 
> ### * outlier
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: outlier
> ### Title: Identify outliers in the multivariate distribution
> ### Aliases: outlier
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> ## Not run: 
> ##D # not run
> ##D ut<-outlier(M)
> ##D #[1] "The number of significant dimensions is 19"
> ##D colnames(ut)
> ##D #[1] "Hotelling" "DMODX"  
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "pava"
> 
> ### * pava
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pava
> ### Title: Pooling of Adjacent Violators
> ### Aliases: pava
> ### Keywords: regression
> 
> ### ** Examples
> 
> pava(c(1,2,4,3,5))
[1] 1.0 2.0 3.5 3.5 5.0
> # [1] 1.0 2.0 3.5 3.5 5.0
> 
> 
> 
> cleanEx(); ..nameEx <- "permutation.t"
> 
> ### * permutation.t
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: permutation.t
> ### Title: bootstrap p-value for matrix with variables in rows and with
> ###   arbitrary missing structure.
> ### Aliases: permutation.t
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> ## Not run: 
> ##D p.vals <- permutation.t(data = clinical.data,grp = grp, B = 10000, na.rate = 0.1) 
> ## End(Not run)
> 
> 
> 
> cleanEx(); ..nameEx <- "rank.trend"
> 
> ### * rank.trend
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rank.trend
> ### Title: Trens analysis based on ranks
> ### Aliases: rank.trend
> ### Keywords: robust
> 
> ### ** Examples
> 
> # not run
> D <- c(123, 334, 578, 762, 755, 890)
> rank.trend(data = t(as.matrix(D)), har = TRUE)
     Trend score Hardy score p-value for no trend
[1,]           2          90           0.01750284
> #     Trend score Hardy score p-value for no trend
> # [1,]           2          90           0.01750284
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "samrocnboot"
> 
> ### * samrocnboot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: samrocNboot
> ### Title: Calculate ROC curve based SAM statistic
> ### Aliases: samrocNboot
> ### Keywords: htest
> 
> ### ** Examples
> 
> library(multtest)
> #Loading required package: genefilter 
> #Loading required package: survival 
> #Loading required package: splines 
> #Loading required package: reposTools 
> data(golub)
>  # This makes the expression data from Golub et al available
>  # in the matrix golub, and the sample labels in the vector golub.cl
>  
>  samroc.res <- samrocNboot(data = golub, formula = ~as.factor(golub.cl))
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
Warning: no finite arguments to max; returning -Inf
> #Warning message: 
> #package 'modreg' has been merged into 'stats' 
>  samroc.res$p0
[1] 0.4288874
> #[1] 0.4776763
>  samroc.res$s0
      10% 
0.1147915 
> #  0 
>  hist(samroc.res$pvalue)
>  # many genes appear changed
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  53.86 3.82 58.02 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')