Last data update: 2014.03.03

 R: Step-down maxT and minP multiple testing procedures
mt.maxTR Documentation

Step-down maxT and minP multiple testing procedures

Description

These functions compute permutation adjusted p-values for step-down multiple testing procedures described in Westfall & Young (1993).

Usage

mt.maxT(X,classlabel,test="t",side="abs",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")
mt.minP(X,classlabel,test="t",side="abs",fixed.seed.sampling="y",B=10000,na=.mt.naNUM,nonpara="n")

Arguments

X

A data frame or matrix, with m rows corresponding to variables (hypotheses) and n columns to observations. In the case of gene expression data, rows correspond to genes and columns to mRNA samples. The data can be read using read.table.

classlabel

A vector of integers corresponding to observation (column) class labels. For k classes, the labels must be integers between 0 and k-1. For the blockf test option, observations may be divided into n/k blocks of k observations each. The observations are ordered by block, and within each block, they are labeled using the integers 0 to k-1.

test

A character string specifying the statistic to be used to test the null hypothesis of no association between the variables and the class labels.
If test="t", the tests are based on two-sample Welch t-statistics (unequal variances).
If test="t.equalvar", the tests are based on two-sample t-statistics with equal variance for the two samples. The square of the t-statistic is equal to an F-statistic for k=2.
If test="wilcoxon", the tests are based on standardized rank sum Wilcoxon statistics.
If test="f", the tests are based on F-statistics.
If test="pairt", the tests are based on paired t-statistics. The square of the paired t-statistic is equal to a block F-statistic for k=2.
If test="blockf", the tests are based on F-statistics which adjust for block differences (cf. two-way analysis of variance).

side

A character string specifying the type of rejection region.
If side="abs", two-tailed tests, the null hypothesis is rejected for large absolute values of the test statistic.
If side="upper", one-tailed tests, the null hypothesis is rejected for large values of the test statistic.
If side="lower", one-tailed tests, the null hypothesis is rejected for small values of the test statistic.

fixed.seed.sampling

If fixed.seed.sampling="y", a fixed seed sampling procedure is used, which may double the computing time, but will not use extra memory to store the permutations. If fixed.seed.sampling="n", permutations will be stored in memory. For the blockf test, the option n was not implemented as it requires too much memory.

B

The number of permutations. For a complete enumeration, B should be 0 (zero) or any number not less than the total number of permutations.

na

Code for missing values (the default is .mt.naNUM=--93074815.62). Entries with missing values will be ignored in the computation, i.e., test statistics will be based on a smaller sample size. This feature has not yet fully implemented.

nonpara

If nonpara="y", nonparametric test statistics are computed based on ranked data.
If nonpara="n", the original data are used.

Details

These functions compute permutation adjusted p-values for the step-down maxT and minP multiple testing procedures, which provide strong control of the family-wise Type I error rate (FWER). The adjusted p-values for the minP procedure are defined in equation (2.10) p. 66 of Westfall & Young (1993), and the maxT procedure is discussed p. 50 and 114. The permutation algorithms for estimating the adjusted p-values are given in Ge et al. (In preparation). The procedures are for the simultaneous test of m null hypotheses, namely, the null hypotheses of no association between the m variables corresponding to the rows of the data frame X and the class labels classlabel. For gene expression data, the null hypotheses correspond to no differential gene expression across mRNA samples.

Value

A data frame with components

index

Vector of row indices, between 1 and nrow(X), where rows are sorted first according to their adjusted p-values, next their unadjusted p-values, and finally their test statistics.

teststat

Vector of test statistics, ordered according to index. To get the test statistics in the original data order, use teststat[order(index)].

rawp

Vector of raw (unadjusted) p-values, ordered according to index.

adjp

Vector of adjusted p-values, ordered according to index.

plower

For mt.minP function only, vector of "adjusted p-values", where ties in the permutation distribution of the successive minima of raw p-values with the observed p-values are counted only once. Note that procedures based on plower do not control the FWER. Comparison of plower and adjp gives an idea of the discreteness of the permutation distribution. Values in plower are ordered according to index.

Author(s)

Yongchao Ge, yongchao.ge@mssm.edu,
Sandrine Dudoit, http://www.stat.berkeley.edu/~sandrine.

References

S. Dudoit, J. P. Shaffer, and J. C. Boldrick (Submitted). Multiple hypothesis testing in microarray experiments.

Y. Ge, S. Dudoit, and T. P. Speed. Resampling-based multiple testing for microarray data hypothesis, Technical Report #633 of UCB Stat. http://www.stat.berkeley.edu/~gyc

P. H. Westfall and S. S. Young (1993). Resampling-based multiple testing: Examples and methods for p-value adjustment. John Wiley & Sons.

See Also

mt.plot, mt.rawp2adjp, mt.reject, mt.sample.teststat, mt.teststat, golub.

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)
resP<-mt.minP(smallgd,classlabel)
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")

# 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")

# Permutation adjusted p-values for maxT procedure with block F-statistics
classlabel<-rep(0:18,2)
mt.maxT(smallgd,classlabel,test="blockf",side="upper")

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(multtest)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/multtest/mt.maxT.Rd_%03d_medium.png", width=480, height=480)
> ### 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")
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 0.0071 0.3904
21     21 2.7690759 0.0160 0.7827
64     64 2.7146480 0.0165 0.8068
29     29 2.3893531 0.0377 0.9260
98     98 2.3810108 0.0339 0.9260
83     83 2.3519292 0.0311 0.9313
44     44 2.2689744 0.0465 0.9517
19     19 2.1389482 0.0657 0.9756
62     62 2.1364820 0.0422 0.9757
27     27 2.0297403 0.0720 0.9870
13     13 1.9211264 0.0387 0.9937
93     93 1.8361003 0.1029 0.9977
38     38 1.7803992 0.0629 0.9981
77     77 1.7608017 0.1240 0.9981
53     53 1.7561125 0.1264 0.9981
86     86 1.7033746 0.1471 0.9988
63     63 1.5659623 0.1743 0.9999
51     51 1.5592634 0.1880 0.9999
35     35 1.5577416 0.1820 0.9999
15     15 1.5164586 0.1957 0.9999
28     28 1.5142189 0.1975 0.9999
99     99 1.5009710 0.1682 0.9999
70     70 1.4769979 0.2173 0.9999
3       3 1.4728588 0.2044 0.9999
17     17 1.4702197 0.2114 0.9999
94     94 1.4531532 0.2229 0.9999
39     39 1.3891276 0.2376 1.0000
79     79 1.3788664 0.2559 1.0000
8       8 1.3648830 0.1417 1.0000
24     24 1.3554613 0.2595 1.0000
5       5 1.3275549 0.2698 1.0000
42     42 1.2846011 0.2937 1.0000
6       6 1.2638410 0.3171 1.0000
26     26 1.2510375 0.1112 1.0000
14     14 1.2499650 0.3215 1.0000
95     95 1.2425320 0.3179 1.0000
10     10 1.2378640 0.1659 1.0000
73     73 1.2365865 0.3233 1.0000
9       9 1.2326967 0.2330 1.0000
55     55 1.2179523 0.3270 1.0000
7       7 1.1926289 0.2832 1.0000
37     37 1.1895677 0.3611 1.0000
4       4 1.1871576 0.3570 1.0000
65     65 1.1810075 0.3682 1.0000
90     90 1.1519504 0.2664 1.0000
54     54 1.1287818 0.3943 1.0000
20     20 1.1102820 0.4055 1.0000
43     43 1.1078936 0.4153 1.0000
33     33 1.0938577 0.4180 1.0000
41     41 1.0874277 0.4195 1.0000
12     12 1.0770652 0.3378 1.0000
85     85 1.0766656 0.4438 1.0000
57     57 1.0723152 0.4090 1.0000
2       2 1.0550416 0.3899 1.0000
58     58 1.0520557 0.4467 1.0000
49     49 1.0244823 0.4237 1.0000
67     67 1.0242288 0.4086 1.0000
100   100 1.0181612 0.4781 1.0000
50     50 1.0165300 0.5160 1.0000
76     76 1.0157120 0.4021 1.0000
61     61 1.0074150 0.4131 1.0000
74     74 0.9926239 0.5076 1.0000
92     92 0.9924591 0.5074 1.0000
59     59 0.9780074 0.4984 1.0000
97     97 0.9653883 0.5295 1.0000
34     34 0.9646748 0.5263 1.0000
71     71 0.9562545 0.5273 1.0000
87     87 0.9343956 0.5594 1.0000
45     45 0.9339340 0.5414 1.0000
47     47 0.9246263 0.5821 1.0000
68     68 0.9119733 0.5679 1.0000
78     78 0.9041381 0.6004 1.0000
1       1 0.8965092 0.5029 1.0000
81     81 0.8838244 0.6066 1.0000
46     46 0.8785220 0.6166 1.0000
80     80 0.8675999 0.7044 1.0000
60     60 0.8666959 0.6186 1.0000
88     88 0.8547663 0.6895 1.0000
48     48 0.8542449 0.6690 1.0000
72     72 0.8476884 0.5963 1.0000
52     52 0.8319301 0.6420 1.0000
32     32 0.8255257 0.6785 1.0000
75     75 0.7917817 0.6623 1.0000
69     69 0.7809722 0.6918 1.0000
36     36 0.7596911 0.7262 1.0000
22     22 0.7514488 0.7980 1.0000
66     66 0.7177759 0.7803 1.0000
30     30 0.6982564 0.8062 1.0000
84     84 0.6909243 0.7685 1.0000
25     25 0.6737118 0.7902 1.0000
31     31 0.6707505 0.8310 1.0000
82     82 0.6556963 0.8148 1.0000
96     96 0.6004815 0.8745 1.0000
91     91 0.5575354 0.8914 1.0000
89     89 0.5181634 0.9623 1.0000
16     16 0.5119585 0.9177 1.0000
56     56 0.4338079 0.9600 1.0000
11     11 0.4296084 0.9612 1.0000
40     40 0.3837019 0.9746 1.0000
23     23 0.2610202 0.9949 1.0000
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO