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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("multtest-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('multtest')
Loading required package: survival
Loading required package: splines
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "MTP-class"
> 
> ### * MTP-class
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MTP-class
> ### Title: Class "MTP", classes and methods for multiple testing procedure
> ###   output
> ### Aliases: MTP-class
> ### Keywords: classes
> 
> ### ** Examples
> 
> ## See MTP function: ? MTP
> 
> 
> 
> cleanEx(); ..nameEx <- "MTP"
> 
> ### * MTP
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MTP
> ### Title: A function to perform resampling-based multiple hypothesis
> ###   testing
> ### Aliases: MTP
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> #data 
> set.seed(99)
> data<-matrix(rnorm(90),nr=9)
> group<-c(rep(1,5),rep(0,5))
> 
> #fwer control with bootstrap null distribution (B=100 for speed)
> m1<-MTP(X=data,Y=group,alternative="less",B=100,method="sd.minP")
running bootstrap...
iteration = 100

> print(m1)

	Multiple Testing Procedure

Object of class:  MTP
sample size = 10 
number of hypotheses = 9 

test statistics = t.twosamp.unequalvar 
type I error rate = fwer 
nominal level alpha = 0.05 
multiple testing procedure = sd.minP 

Call: MTP(X = data, Y = group, alternative = "less", B = 100, method = "sd.minP")

Slots: 
            Class    Mode Length Dimension
statistic numeric numeric      9          
estimate  numeric numeric      9          
sampsize  integer numeric      1          
rawp      numeric numeric      9          
adjp      numeric numeric      9          
conf.reg    array logical      0     0,0,0
cutoff     matrix logical      0       0,0
reject     matrix logical      9       9,1
nulldist   matrix numeric    900     9,100
call         call    call      6          
seed      integer numeric      0          
> summary(m1)
MTP:  sd.minP 
Type I error rate:  fwer 

  Level Rejections
1  0.05          1

             Min. 1st Qu.  Median    Mean 3rd Qu.  Max.
adjp       0.0400  0.3100  0.3200  0.5656  0.9900 0.990
rawp       0.0100  0.0700  0.0900  0.3889  0.7700 0.990
statistic -1.8180 -1.2140 -0.9126 -0.2619  0.5916 2.696
estimate  -0.9052 -0.7545 -0.4827 -0.1838  0.2746 1.252
> par(mfrow=c(2,2))
> plot(m1,top=9)
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "boot.resample"
> 
> ### * boot.resample
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boot.resample
> ### Title: Non-parametric bootstrap resampling function in package
> ###   `multtest'
> ### Aliases: boot.resample
> ### Keywords: manip internal
> 
> ### ** Examples
> 
> 
> #data example: ALL data set
> set.seed(99)
> data<-matrix(rnorm(90),nr=9)
> 
> #closure
> ttest<-meanX(psi0=0,na.rm=TRUE,standardize=TRUE,alternative="two.sided",robust=FALSE)
> 
> #test statistics
> obs<-get.Tn(X=data,stat.closure=ttest,W=NULL)
> 
> #bootstrap null distribution (B=100 for speed)
> nulldistn<-boot.resample(X=data,W=NULL,stat.closure=ttest,B=100,theta0=0,tau0=1)
running bootstrap...
iteration = 100

> 
> #unadjusted p-values
> rawp<-apply((obs[1,]/obs[2,])<=nulldistn,1,mean)
> sum(rawp<=0.01)
[1] 1
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "fwer2gfwer"
> 
> ### * fwer2gfwer
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: fwer2gfwer
> ### Title: Function to compute augmentation MTP adjusted p-values
> ### Aliases: fwer2gfwer fwer2tppfp fwer2fdr
> ### Keywords: htest internal
> 
> ### ** Examples
> 
> 
> data<-matrix(rnorm(200),nr=20)
> group<-c(rep(0,5),rep(1,5))
> fwer.mtp<-MTP(X=data,Y=group)
running bootstrap...
iteration = 100 200 300 400 500 600 700 800 900 1000

> fwer.adjp<-fwer.mtp@adjp
> gfwer.adjp<-fwer2gfwer(adjp=fwer.adjp,k=c(1,5,10))
> compare.gfwer<-cbind(fwer.adjp,gfwer.adjp)
> mt.plot(adjp=compare.gfwer,teststat=fwer.mtp@statistic,proc=c("gFWER(0)","gFWER(1)","gFWER(5)","gFWER(10)"),col=1:4,lty=1:4)
> title("Comparison of Single-step MaxT gFWER Controlling Methods")
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "get.index"
> 
> ### * get.index
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: get.index
> ### Title: Function to compute indices for ordering hypotheses in Package
> ###   'multtest'
> ### Aliases: get.index
> ### Keywords: htest internal
> 
> ### ** Examples
> 
> data<-matrix(rnorm(200),nr=20)
> mtp<-MTP(X=data,test="t.onesamp")
running bootstrap...
iteration = 100 200 300 400 500 600 700 800 900 1000

> index<-get.index(adjp=mtp@adjp,rawp=mtp@rawp,stat=mtp@statistic)
> mtp@statistic[index]
 [1] -0.62383091  0.61959029  0.54996909  0.45979913  0.36310805  0.32177655
 [7] -0.28896780 -0.28343768  0.26179136  0.21265993 -0.20977124 -0.17197359
[13] -0.15690916  0.13675479 -0.11772816 -0.11483575 -0.06494253  0.03120672
[19]  0.01349878 -0.01032543
> mtp@estimate[index]
 [1] -0.59769844  0.38449858  0.52070416  0.32496404  0.38942266  0.26209966
 [7] -0.16498298 -0.18576650  0.27141328  0.24321005 -0.23988579 -0.17167561
[13] -0.18859576  0.12377288 -0.14162717 -0.11010342 -0.03883344  0.02916605
[19]  0.01102713 -0.01031648
> apply(data[index,],1,mean)
 [1] -0.59769844  0.38449858  0.52070416  0.32496404  0.38942266  0.26209966
 [7] -0.16498298 -0.18576650  0.27141328  0.24321005 -0.23988579 -0.17167561
[13] -0.18859576  0.12377288 -0.14162717 -0.11010342 -0.03883344  0.02916605
[19]  0.01102713 -0.01031648
> 
> 
> 
> cleanEx(); ..nameEx <- "meanX"
> 
> ### * meanX
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: meanX
> ### Title: Functions to create test statistic closures and apply them to
> ###   data
> ### Aliases: meanX diffmeanX FX blockFX lmX lmY coxY get.Tn
> ### Keywords: htest internal
> 
> ### ** Examples
> 
> data<-matrix(rnorm(200),nr=20)
> ttest<-meanX(psi0=0,na.rm=TRUE,standardize=TRUE,alternative="two.sided",robust=FALSE)
> obs<-wapply(data,1,ttest,W=NULL)
> statistics<-obs[1,]*obs[3,]/obs[2,]
> statistics
 [1] -0.15690916  0.61959029  0.03120672 -0.20977124 -0.28343768  0.26179136
 [7]  0.36310805 -0.01032543 -0.28896780  0.21265993  0.54996909 -0.06494253
[13]  0.45979913 -0.62383091 -0.11772816 -0.17197359  0.01349878 -0.11483575
[19]  0.13675479  0.32177655
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.maxT"
> 
> ### * mt.maxT
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.maxT
> ### Title: Step-down maxT and minP multiple testing procedures
> ### Aliases: mt.maxT mt.minP
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Gene expression data from Golub et al. (1999)
> # To reduce computation time and for illustrative purposes, we condider only
> # the first 100 genes and use the default of B=10,000 permutations.
> # In general, one would need a much larger number of permutations
> # for microarray data.
> 
> data(golub)
> smallgd<-golub[1:100,] 
> classlabel<-golub.cl
> 
> # Permutation unadjusted p-values and adjusted p-values 
> # for maxT and minP procedures with Welch t-statistics
> resT<-mt.maxT(smallgd,classlabel)
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
> resP<-mt.minP(smallgd,classlabel)
B=10000
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
r=1	r=2	r=3	r=4	r=5	r=6	r=7	r=8	r=9	r=10	
r=11	r=12	r=13	r=14	r=15	r=16	r=17	r=18	r=19	r=20	
r=21	r=22	r=23	r=24	r=25	r=26	r=27	r=28	r=29	r=30	
r=31	r=32	r=33	r=34	r=35	r=36	r=37	r=38	r=39	r=40	
r=41	r=42	r=43	r=44	r=45	r=46	r=47	r=48	r=49	r=50	
r=51	r=52	r=53	r=54	r=55	r=56	r=57	r=58	r=59	r=60	
r=61	r=62	r=63	r=64	r=65	r=66	r=67	r=68	r=69	r=70	
r=71	r=72	r=73	r=74	r=75	r=76	r=77	r=78	r=79	r=80	
r=81	r=82	r=83	r=84	r=85	r=86	r=87	r=88	r=89	r=90	
r=91	r=92	r=93	r=94	r=95	r=96	r=97	r=98	r=99	r=100	
> rawp<-resT$rawp[order(resT$index)]
> teststat<-resT$teststat[order(resT$index)]
> 
> # Plot results and compare to Bonferroni procedure
> bonf<-mt.rawp2adjp(rawp, proc=c("Bonferroni"))
> allp<-cbind(rawp, bonf$adjp[order(bonf$index),2], resT$adjp[order(resT$index)],resP$adjp[order(resP$index)])
> 
> mt.plot(allp, teststat, plottype="rvsa", proc=c("rawp","Bonferroni","maxT","minP"),leg=c(0.7,50),lty=1,col=1:4,lwd=2)
> mt.plot(allp, teststat, plottype="pvsr", proc=c("rawp","Bonferroni","maxT","minP"),leg=c(60,0.2),lty=1,col=1:4,lwd=2)
> mt.plot(allp, teststat, plottype="pvst", proc=c("rawp","Bonferroni","maxT","minP"),leg=c(-6,0.6),pch=16,col=1:4)
> 
> # Permutation adjusted p-values for minP procedure with F-statistics (like equal variance t-statistics)
> mt.minP(smallgd,classlabel,test="f",fixed.seed.sampling="n")
B=10000

We're doing 10000 random permutations
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
r=1	r=2	r=3	r=4	r=5	r=6	r=7	r=8	r=9	r=10	
r=11	r=12	r=13	r=14	r=15	r=16	r=17	r=18	r=19	r=20	
r=21	r=22	r=23	r=24	r=25	r=26	r=27	r=28	r=29	r=30	
r=31	r=32	r=33	r=34	r=35	r=36	r=37	r=38	r=39	r=40	
r=41	r=42	r=43	r=44	r=45	r=46	r=47	r=48	r=49	r=50	
r=51	r=52	r=53	r=54	r=55	r=56	r=57	r=58	r=59	r=60	
r=61	r=62	r=63	r=64	r=65	r=66	r=67	r=68	r=69	r=70	
r=71	r=72	r=73	r=74	r=75	r=76	r=77	r=78	r=79	r=80	
r=81	r=82	r=83	r=84	r=85	r=86	r=87	r=88	r=89	r=90	
r=91	r=92	r=93	r=94	r=95	r=96	r=97	r=98	r=99	r=100	
    index     teststat   rawp   adjp plower
68     68 3.062227e+01 0.0001 0.0093 0.0001
96     96 2.766694e+01 0.0001 0.0093 0.0001
13     13 2.414014e+01 0.0001 0.0093 0.0001
66     66 1.861284e+01 0.0002 0.0177 0.0093
11     11 2.008664e+01 0.0003 0.0256 0.0176
56     56 2.001496e+01 0.0003 0.0256 0.0176
12     12 1.844169e+01 0.0005 0.0413 0.0338
62     62 1.444177e+01 0.0005 0.0413 0.0338
23     23 1.324712e+01 0.0008 0.0634 0.0562
74     74 1.250343e+01 0.0011 0.0838 0.0774
81     81 1.289048e+01 0.0012 0.0893 0.0828
78     78 1.228135e+01 0.0016 0.1143 0.1084
55     55 9.224597e+00 0.0044 0.2638 0.2589
32     32 8.844296e+00 0.0048 0.2804 0.2756
36     36 8.519580e+00 0.0053 0.3011 0.2967
67     67 8.196699e+00 0.0069 0.3630 0.3590
82     82 7.311714e+00 0.0103 0.4767 0.4735
50     50 7.099773e+00 0.0103 0.4767 0.4735
84     84 6.217233e+00 0.0172 0.6421 0.6400
1       1 6.260538e+00 0.0176 0.6498 0.6471
35     35 5.644936e+00 0.0239 0.7471 0.7457
18     18 5.532228e+00 0.0254 0.7613 0.7605
39     39 4.759517e+00 0.0301 0.8092 0.8079
60     60 4.793357e+00 0.0366 0.8623 0.8617
79     79 4.557838e+00 0.0372 0.8625 0.8621
25     25 4.545975e+00 0.0388 0.8713 0.8706
59     59 4.426178e+00 0.0390 0.8713 0.8706
26     26 4.851972e+00 0.0413 0.8844 0.8841
63     63 4.210565e+00 0.0472 0.9109 0.9107
51     51 4.109967e+00 0.0491 0.9171 0.9164
43     43 3.958032e+00 0.0580 0.9430 0.9427
20     20 3.449932e+00 0.0723 0.9710 0.9708
77     77 3.103298e+00 0.0849 0.9843 0.9843
72     72 2.852188e+00 0.0889 0.9854 0.9854
100   100 2.951363e+00 0.0964 0.9893 0.9892
47     47 2.773701e+00 0.1081 0.9935 0.9935
48     48 2.617835e+00 0.1115 0.9942 0.9941
73     73 2.619894e+00 0.1172 0.9951 0.9951
40     40 2.311915e+00 0.1387 0.9988 0.9988
29     29 2.241490e+00 0.1436 0.9988 0.9988
41     41 2.304974e+00 0.1440 0.9988 0.9988
53     53 2.167377e+00 0.1475 0.9988 0.9988
89     89 2.061347e+00 0.1552 0.9988 0.9988
17     17 2.109530e+00 0.1564 0.9988 0.9988
49     49 1.967708e+00 0.1878 0.9995 0.9995
83     83 1.822895e+00 0.1920 1.0000 1.0000
21     21 1.666260e+00 0.2043 1.0000 1.0000
37     37 1.654224e+00 0.2092 1.0000 1.0000
46     46 1.540901e+00 0.2270 1.0000 1.0000
64     64 1.467839e+00 0.2349 1.0000 1.0000
5       5 1.408085e+00 0.2434 1.0000 1.0000
90     90 1.383139e+00 0.2567 1.0000 1.0000
34     34 1.326855e+00 0.2605 1.0000 1.0000
2       2 1.336722e+00 0.2617 1.0000 1.0000
61     61 1.275653e+00 0.2726 1.0000 1.0000
6       6 1.192989e+00 0.2824 1.0000 1.0000
75     75 1.193419e+00 0.2888 1.0000 1.0000
69     69 8.536810e-01 0.3591 1.0000 1.0000
98     98 8.539816e-01 0.3598 1.0000 1.0000
44     44 8.372706e-01 0.3679 1.0000 1.0000
42     42 8.429247e-01 0.3693 1.0000 1.0000
94     94 7.328465e-01 0.4011 1.0000 1.0000
54     54 5.968788e-01 0.4550 1.0000 1.0000
65     65 5.502305e-01 0.4609 1.0000 1.0000
80     80 6.556086e-01 0.4856 1.0000 1.0000
92     92 4.707996e-01 0.4927 1.0000 1.0000
70     70 4.341107e-01 0.5066 1.0000 1.0000
45     45 4.607621e-01 0.5130 1.0000 1.0000
93     93 3.967730e-01 0.5378 1.0000 1.0000
24     24 3.947238e-01 0.5462 1.0000 1.0000
71     71 3.647944e-01 0.5533 1.0000 1.0000
97     97 3.576693e-01 0.5543 1.0000 1.0000
95     95 3.253561e-01 0.5847 1.0000 1.0000
57     57 3.125110e-01 0.5958 1.0000 1.0000
88     88 3.823742e-01 0.6123 1.0000 1.0000
30     30 2.893964e-01 0.6168 1.0000 1.0000
85     85 2.472995e-01 0.6260 1.0000 1.0000
52     52 2.110735e-01 0.6531 1.0000 1.0000
22     22 2.100324e-01 0.6611 1.0000 1.0000
38     38 1.990003e-01 0.6621 1.0000 1.0000
7       7 2.442059e-01 0.6774 1.0000 1.0000
16     16 1.457034e-01 0.6919 1.0000 1.0000
27     27 1.360907e-01 0.7165 1.0000 1.0000
19     19 1.192702e-01 0.7302 1.0000 1.0000
14     14 1.242731e-01 0.7312 1.0000 1.0000
8       8 1.341616e-01 0.7683 1.0000 1.0000
33     33 7.460349e-02 0.7838 1.0000 1.0000
4       4 7.434216e-02 0.7897 1.0000 1.0000
31     31 2.707073e-02 0.8675 1.0000 1.0000
76     76 4.698657e-02 0.8751 1.0000 1.0000
15     15 2.370482e-02 0.8796 1.0000 1.0000
91     91 1.835717e-02 0.8903 1.0000 1.0000
99     99 1.663129e-02 0.8979 1.0000 1.0000
3       3 1.209703e-02 0.9128 1.0000 1.0000
86     86 4.653993e-03 0.9479 1.0000 1.0000
10     10 8.231442e-03 0.9585 1.0000 1.0000
28     28 2.693274e-03 0.9626 1.0000 1.0000
58     58 7.484439e-04 0.9760 1.0000 1.0000
87     87 9.860035e-05 0.9915 1.0000 1.0000
9       9 1.257922e-04 0.9932 1.0000 1.0000
> 
> # Note that the test statistics used in the examples below are not appropriate 
> # for the Golub et al. data. The sole purpose of these examples is to 
> # demonstrate the use of the mt.maxT and mt.minP functions.
> 
> # Permutation adjusted p-values for maxT procedure with paired t-statistics
> classlabel<-rep(c(0,1),19)
> mt.maxT(smallgd,classlabel,test="pairt")

We're doing 10000 random permutations
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
    index    teststat   rawp   adjp
96     96 -2.60421977 0.0173 0.6974
59     59 -2.33755545 0.0289 0.8673
23     23 -2.20387309 0.0397 0.9302
27     27 -2.03969281 0.0558 0.9773
99     99 -2.00108376 0.0604 0.9820
63     63 -1.93588894 0.0681 0.9891
53     53 -1.86166565 0.0849 0.9940
95     95 -1.82033275 0.0855 0.9955
30     30  1.72770024 0.0983 0.9984
32     32  1.66180749 0.0912 0.9996
1       1 -1.60285470 0.1531 0.9998
41     41  1.56481848 0.1345 0.9998
100   100  1.56357062 0.1335 0.9998
52     52  1.46843667 0.1529 0.9999
83     83 -1.41707550 0.1751 1.0000
5       5 -1.37475720 0.1751 1.0000
4       4 -1.35478834 0.1862 1.0000
49     49 -1.31527878 0.2161 1.0000
73     73  1.28332034 0.2216 1.0000
34     34  1.28278125 0.2144 1.0000
37     37  1.27848786 0.2176 1.0000
80     80 -1.25044618 0.3160 1.0000
62     62 -1.23821967 0.2267 1.0000
65     65 -1.22051990 0.2393 1.0000
22     22 -1.20045311 0.2528 1.0000
2       2 -1.19476921 0.2537 1.0000
88     88 -1.15100480 0.2965 1.0000
40     40  1.11350723 0.2844 1.0000
76     76  1.09589109 0.3326 1.0000
55     55 -1.08856240 0.2886 1.0000
33     33 -1.08221870 0.2976 1.0000
79     79 -1.07886266 0.2907 1.0000
60     60  1.07041860 0.2918 1.0000
67     67  1.05377621 0.3205 1.0000
6       6 -1.04876563 0.3028 1.0000
90     90  1.04870681 0.3472 1.0000
10     10  1.04390457 0.3755 1.0000
81     81 -1.01552853 0.3271 1.0000
3       3 -1.01184475 0.3355 1.0000
78     78 -0.99690722 0.3521 1.0000
20     20  0.98207112 0.3354 1.0000
9       9  0.97052584 0.3904 1.0000
25     25 -0.92433717 0.3686 1.0000
66     66 -0.90195472 0.3829 1.0000
38     38  0.88272533 0.3847 1.0000
17     17 -0.87506967 0.3920 1.0000
72     72  0.86436963 0.4259 1.0000
12     12 -0.85800235 0.4065 1.0000
21     21  0.82915343 0.4088 1.0000
64     64 -0.82169371 0.4240 1.0000
68     68  0.78825137 0.4348 1.0000
7       7  0.73145793 0.5835 1.0000
58     58 -0.72586790 0.4780 1.0000
74     74 -0.71613131 0.4788 1.0000
94     94 -0.71402074 0.4883 1.0000
50     50  0.70002505 0.5020 1.0000
42     42  0.66777708 0.5113 1.0000
31     31  0.66426220 0.5172 1.0000
54     54  0.64826876 0.5189 1.0000
8       8  0.61535598 0.6570 1.0000
75     75 -0.61066078 0.5424 1.0000
82     82 -0.59674635 0.5528 1.0000
29     29  0.56224784 0.5700 1.0000
70     70 -0.55543071 0.5849 1.0000
14     14 -0.53873437 0.5901 1.0000
56     56  0.49496778 0.6381 1.0000
19     19  0.47968574 0.6318 1.0000
85     85 -0.47556264 0.6396 1.0000
13     13 -0.45748006 0.6441 1.0000
93     93 -0.44832518 0.6580 1.0000
86     86 -0.44748833 0.6615 1.0000
35     35  0.42487123 0.6674 1.0000
46     46  0.40376554 0.6929 1.0000
87     87 -0.39111111 0.7035 1.0000
48     48 -0.39058601 0.7004 1.0000
57     57  0.38658911 0.7077 1.0000
18     18  0.38121040 0.7093 1.0000
98     98  0.31129095 0.7850 1.0000
28     28 -0.30278045 0.7554 1.0000
71     71  0.29255719 0.7725 1.0000
26     26  0.26414778 0.7928 1.0000
45     45  0.25676230 0.7955 1.0000
97     97  0.23947865 0.8200 1.0000
77     77  0.22309752 0.8310 1.0000
44     44  0.20631066 0.8434 1.0000
47     47 -0.19189123 0.8552 1.0000
24     24  0.18303022 0.8548 1.0000
11     11 -0.18189038 0.8503 1.0000
92     92 -0.16398790 0.8693 1.0000
69     69  0.14962497 0.8815 1.0000
16     16  0.14437956 0.8852 1.0000
36     36 -0.11570229 0.9157 1.0000
43     43  0.11313815 0.9091 1.0000
84     84 -0.11041616 0.9147 1.0000
89     89 -0.08497806 0.9389 1.0000
61     61 -0.07314702 0.9498 1.0000
15     15 -0.05690673 0.9533 1.0000
39     39 -0.02810173 0.9777 1.0000
51     51  0.02746677 0.9785 1.0000
91     91  0.01297850 0.9897 1.0000
> 
> # Permutation adjusted p-values for maxT procedure with block F-statistics
> classlabel<-rep(0:18,2)
> mt.maxT(smallgd,classlabel,test="blockf",side="upper")
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
    index  teststat   rawp   adjp
18     18 3.6139699 0.0055 0.3898
21     21 2.7690759 0.0178 0.7818
64     64 2.7146480 0.0152 0.8021
29     29 2.3893531 0.0407 0.9283
98     98 2.3810108 0.0319 0.9283
83     83 2.3519292 0.0312 0.9330
44     44 2.2689744 0.0478 0.9530
19     19 2.1389482 0.0680 0.9750
62     62 2.1364820 0.0399 0.9752
27     27 2.0297403 0.0724 0.9871
13     13 1.9211264 0.0422 0.9945
93     93 1.8361003 0.1051 0.9973
38     38 1.7803992 0.0584 0.9986
77     77 1.7608017 0.1258 0.9989
53     53 1.7561125 0.1268 0.9989
86     86 1.7033746 0.1436 0.9996
63     63 1.5659623 0.1688 0.9999
51     51 1.5592634 0.1841 0.9999
35     35 1.5577416 0.1799 0.9999
15     15 1.5164586 0.1989 0.9999
28     28 1.5142189 0.1999 0.9999
99     99 1.5009710 0.1673 0.9999
70     70 1.4769979 0.2120 1.0000
3       3 1.4728588 0.2064 1.0000
17     17 1.4702197 0.2185 1.0000
94     94 1.4531532 0.2200 1.0000
39     39 1.3891276 0.2385 1.0000
79     79 1.3788664 0.2644 1.0000
8       8 1.3648830 0.1446 1.0000
24     24 1.3554613 0.2696 1.0000
5       5 1.3275549 0.2808 1.0000
42     42 1.2846011 0.2968 1.0000
6       6 1.2638410 0.3223 1.0000
26     26 1.2510375 0.1055 1.0000
14     14 1.2499650 0.3258 1.0000
95     95 1.2425320 0.3128 1.0000
10     10 1.2378640 0.1740 1.0000
73     73 1.2365865 0.3282 1.0000
9       9 1.2326967 0.2389 1.0000
55     55 1.2179523 0.3267 1.0000
7       7 1.1926289 0.2933 1.0000
37     37 1.1895677 0.3625 1.0000
4       4 1.1871576 0.3701 1.0000
65     65 1.1810075 0.3603 1.0000
90     90 1.1519504 0.2656 1.0000
54     54 1.1287818 0.3973 1.0000
20     20 1.1102820 0.4133 1.0000
43     43 1.1078936 0.4100 1.0000
33     33 1.0938577 0.4244 1.0000
41     41 1.0874277 0.4142 1.0000
12     12 1.0770652 0.3410 1.0000
85     85 1.0766656 0.4346 1.0000
57     57 1.0723152 0.4193 1.0000
2       2 1.0550416 0.3916 1.0000
58     58 1.0520557 0.4460 1.0000
49     49 1.0244823 0.4148 1.0000
67     67 1.0242288 0.4190 1.0000
100   100 1.0181612 0.4869 1.0000
50     50 1.0165300 0.4971 1.0000
76     76 1.0157120 0.3911 1.0000
61     61 1.0074150 0.4062 1.0000
74     74 0.9926239 0.5074 1.0000
92     92 0.9924591 0.5022 1.0000
59     59 0.9780074 0.4880 1.0000
97     97 0.9653883 0.5318 1.0000
34     34 0.9646748 0.5365 1.0000
71     71 0.9562545 0.5319 1.0000
87     87 0.9343956 0.5586 1.0000
45     45 0.9339340 0.5403 1.0000
47     47 0.9246263 0.5738 1.0000
68     68 0.9119733 0.5681 1.0000
78     78 0.9041381 0.5987 1.0000
1       1 0.8965092 0.5096 1.0000
81     81 0.8838244 0.6069 1.0000
46     46 0.8785220 0.6191 1.0000
80     80 0.8675999 0.6966 1.0000
60     60 0.8666959 0.6139 1.0000
88     88 0.8547663 0.6962 1.0000
48     48 0.8542449 0.6552 1.0000
72     72 0.8476884 0.6004 1.0000
52     52 0.8319301 0.6467 1.0000
32     32 0.8255257 0.6724 1.0000
75     75 0.7917817 0.6682 1.0000
69     69 0.7809722 0.6915 1.0000
36     36 0.7596911 0.7243 1.0000
22     22 0.7514488 0.7913 1.0000
66     66 0.7177759 0.7831 1.0000
30     30 0.6982564 0.8081 1.0000
84     84 0.6909243 0.7686 1.0000
25     25 0.6737118 0.7855 1.0000
31     31 0.6707505 0.8289 1.0000
82     82 0.6556963 0.8166 1.0000
96     96 0.6004815 0.8710 1.0000
91     91 0.5575354 0.8871 1.0000
89     89 0.5181634 0.9658 1.0000
16     16 0.5119585 0.9126 1.0000
56     56 0.4338079 0.9632 1.0000
11     11 0.4296084 0.9604 1.0000
40     40 0.3837019 0.9714 1.0000
23     23 0.2610202 0.9963 1.0000
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.plot"
> 
> ### * mt.plot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.plot
> ### Title: Plotting results from multiple testing procedures
> ### Aliases: mt.plot
> ### Keywords: hplot
> 
> ### ** Examples
> 
> # Gene expression data from Golub et al. (1999)
> # To reduce computation time and for illustrative purposes, we condider only
> # the first 100 genes and use the default of B=10,000 permutations.
> # In general, one would need a much larger number of permutations
> # for microarray data.
> 
> data(golub)
> smallgd<-golub[1:100,] 
> classlabel<-golub.cl
> 
> # Permutation unadjusted p-values and adjusted p-values for maxT procedure
> res1<-mt.maxT(smallgd,classlabel)
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
> rawp<-res1$rawp[order(res1$index)]
> teststat<-res1$teststat[order(res1$index)]
> 
> # Permutation adjusted p-values for simple multiple testing procedures
> procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY")
> res2<-mt.rawp2adjp(rawp,procs)
> 
> # Plot results from all multiple testing procedures
> allp<-cbind(res2$adjp[order(res2$index),],res1$adjp[order(res1$index)])
> dimnames(allp)[[2]][9]<-"maxT"
> procs<-dimnames(allp)[[2]]
> procs[7:9]<-c("maxT","BH","BY")
> allp<-allp[,procs]
> 
> cols<-c(1:4,"orange","brown","purple",5:6)
> ltypes<-c(3,rep(1,6),rep(2,2))
> 
> # Ordered adjusted p-values
> mt.plot(allp,teststat,plottype="pvsr",proc=procs,leg=c(80,0.4),lty=ltypes,col=cols,lwd=2)
> 
> # Adjusted p-values in original data order
> mt.plot(allp,teststat,plottype="pvsi",proc=procs,leg=c(80,0.4),lty=ltypes,col=cols,lwd=2)
> 
> # Number of rejected hypotheses vs. level of the test
> mt.plot(allp,teststat,plottype="rvsa",proc=procs,leg=c(0.05,100),lty=ltypes,col=cols,lwd=2)
> 
> # Adjusted p-values vs. test statistics
> mt.plot(allp,teststat,plottype="pvst",logscale=TRUE,proc=procs,leg=c(0,4),pch=ltypes,col=cols)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.rawp2adjp"
> 
> ### * mt.rawp2adjp
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.rawp2adjp
> ### Title: Adjusted p-values for simple multiple testing procedures
> ### Aliases: mt.rawp2adjp
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Gene expression data from Golub et al. (1999)
> # To reduce computation time and for illustrative purposes, we condider only
> # the first 100 genes and use the default of B=10,000 permutations.
> # In general, one would need a much larger number of permutations
> # for microarray data.
> 
> data(golub)
> smallgd<-golub[1:100,] 
> classlabel<-golub.cl
> 
> # Permutation unadjusted p-values and adjusted p-values for maxT procedure
> res1<-mt.maxT(smallgd,classlabel)
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
> rawp<-res1$rawp[order(res1$index)]
> 
> # Permutation adjusted p-values for simple multiple testing procedures
> procs<-c("Bonferroni","Holm","Hochberg","SidakSS","SidakSD","BH","BY")
> res2<-mt.rawp2adjp(rawp,procs)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.reject"
> 
> ### * mt.reject
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.reject
> ### Title: Identity and number of rejected hypotheses
> ### Aliases: mt.reject
> ### Keywords: htest
> 
> ### ** Examples
> 
> # Gene expression data from Golub et al. (1999)
> # To reduce computation time and for illustrative purposes, we condider only
> # the first 100 genes and use the default of B=10,000 permutations.
> # In general, one would need a much larger number of permutations
> # for microarray data.
> 
> data(golub)
> smallgd<-golub[1:100,] 
> classlabel<-golub.cl
> 
> # Permutation unadjusted p-values and adjusted p-values for maxT procedure
> res<-mt.maxT(smallgd,classlabel)
b=100	b=200	b=300	b=400	b=500	b=600	b=700	b=800	b=900	b=1000	
b=1100	b=1200	b=1300	b=1400	b=1500	b=1600	b=1700	b=1800	b=1900	b=2000	
b=2100	b=2200	b=2300	b=2400	b=2500	b=2600	b=2700	b=2800	b=2900	b=3000	
b=3100	b=3200	b=3300	b=3400	b=3500	b=3600	b=3700	b=3800	b=3900	b=4000	
b=4100	b=4200	b=4300	b=4400	b=4500	b=4600	b=4700	b=4800	b=4900	b=5000	
b=5100	b=5200	b=5300	b=5400	b=5500	b=5600	b=5700	b=5800	b=5900	b=6000	
b=6100	b=6200	b=6300	b=6400	b=6500	b=6600	b=6700	b=6800	b=6900	b=7000	
b=7100	b=7200	b=7300	b=7400	b=7500	b=7600	b=7700	b=7800	b=7900	b=8000	
b=8100	b=8200	b=8300	b=8400	b=8500	b=8600	b=8700	b=8800	b=8900	b=9000	
b=9100	b=9200	b=9300	b=9400	b=9500	b=9600	b=9700	b=9800	b=9900	b=10000	
> mt.reject(cbind(res$rawp,res$adjp),seq(0,1,0.1))$r
    [,1] [,2]
0      0    0
0.1   35    8
0.2   45   11
0.3   57   14
0.4   64   14
0.5   70   16
0.6   77   19
0.7   81   22
0.8   88   24
0.9   91   27
1    100  100
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.sample.teststat"
> 
> ### * mt.sample.teststat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.sample.teststat
> ### Title: Permutation distribution of test statistics and raw (unadjusted)
> ###   p-values
> ### Aliases: mt.sample.teststat mt.sample.rawp mt.sample.label
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> # Gene expression data from Golub et al. (1999)
> data(golub)
> 
> mt.sample.label(golub.cl,B=10)
      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
 [1,]    0    0    0    0    0    0    0    0    0     0     0     0     0
 [2,]    1    0    0    0    1    0    0    0    1     0     0     0     0
 [3,]    0    0    1    0    0    0    1    1    0     0     0     0     0
 [4,]    0    0    0    0    0    0    0    0    0     0     0     0     0
 [5,]    1    0    0    0    0    0    1    0    1     0     0     0     0
 [6,]    1    0    0    0    0    0    1    1    1     1     0     0     1
 [7,]    0    0    0    0    0    0    0    1    1     0     1     0     0
 [8,]    0    0    0    0    0    0    0    1    0     0     0     0     1
 [9,]    1    0    1    0    1    0    0    0    0     0     0     1     1
[10,]    1    1    0    0    0    1    0    0    1     1     0     0     1
      [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
 [1,]     0     0     0     0     0     0     0     0     0     0     0     0
 [2,]     0     0     0     1     0     1     0     0     0     1     0     1
 [3,]     1     0     0     0     0     1     0     1     0     0     0     0
 [4,]     1     1     1     1     0     0     1     1     0     0     0     1
 [5,]     1     0     0     0     0     0     0     0     1     0     1     0
 [6,]     0     0     0     1     1     0     0     0     0     0     0     0
 [7,]     1     1     0     0     1     1     0     0     0     0     1     0
 [8,]     1     1     0     1     1     1     1     0     0     1     0     0
 [9,]     1     0     0     0     0     1     0     0     0     0     0     1
[10,]     0     0     1     0     0     1     0     0     0     0     0     0
      [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
 [1,]     0     0     1     1     1     1     1     1     1     1     1     1
 [2,]     0     0     0     0     1     0     0     0     0     0     1     1
 [3,]     1     0     1     0     1     1     0     0     0     0     0     1
 [4,]     0     0     0     1     1     0     0     0     1     1     0     0
 [5,]     1     0     0     0     1     0     0     1     1     1     0     0
 [6,]     0     1     0     1     0     1     0     0     0     0     0     0
 [7,]     1     0     0     0     0     1     0     0     0     0     0     0
 [8,]     1     0     0     0     0     0     0     0     0     0     0     1
 [9,]     1     0     0     0     0     1     0     0     0     1     0     0
[10,]     0     1     0     0     0     1     0     0     0     0     1     0
      [,38]
 [1,]     1
 [2,]     1
 [3,]     0
 [4,]     0
 [5,]     0
 [6,]     0
 [7,]     1
 [8,]     0
 [9,]     0
[10,]     0
> 
> permt<-mt.sample.teststat(golub[1,],golub.cl,B=1000)
> qqnorm(permt)
> qqline(permt)
> 
> permt<-mt.sample.teststat(golub[50,],golub.cl,B=1000)
> qqnorm(permt)
> qqline(permt)
> 
> permp<-mt.sample.rawp(golub[1,],golub.cl,B=1000)
> hist(permp)
> 
> 
> 
> cleanEx(); ..nameEx <- "mt.teststat"
> 
> ### * mt.teststat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mt.teststat
> ### Title: Computing test statistics for each row of a data frame
> ### Aliases: mt.teststat mt.teststat.num.denum
> ### Keywords: univar
> 
> ### ** Examples
> 
> # Gene expression data from Golub et al. (1999)
> data(golub)
> 
> teststat<-mt.teststat(golub,golub.cl)
> qqnorm(teststat)
> qqline(teststat)
> 
> tmp<-mt.teststat.num.denum(golub,golub.cl,test="t")
> num<-tmp$teststat.num
> denum<-tmp$teststat.denum
> plot(sqrt(denum),num)
> 
> tmp<-mt.teststat.num.denum(golub,golub.cl,test="f")
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "ss.maxT"
> 
> ### * ss.maxT
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ss.maxT
> ### Title: Procedures to perform multiple testing
> ### Aliases: ss.maxT ss.minP sd.maxT sd.minP
> ### Keywords: htest internal
> 
> ### ** Examples
> 
> ## These functions are used internally by the MTP function
> ## See MTP function: ? MTP
> 
> 
> 
> cleanEx(); ..nameEx <- "wapply"
> 
> ### * wapply
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: wapply
> ### Title: Weighted version of the apply function
> ### Aliases: wapply
> ### Keywords: internal
> 
> ### ** Examples
> 
> data<-matrix(rnorm(200),nr=20)
> weights<-matrix(rexp(200,rate=0.1),nr=20)
> wapply(X=data,MARGIN=1,FUN=mean,W=weights)
 [1] -0.53731310  0.11287960  0.14724489 -0.07577719 -0.35971689  0.06633178
 [7]  0.42600551 -0.01011928 -0.12837291  0.23731857  0.42414649 -0.06067378
[13]  0.42233521 -0.79002925 -0.27069079 -0.21887077 -0.31048450 -0.40369660
[19]  0.45308152 -0.09597569
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  51.97 0.39 52.71 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')