1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
|
#######################################################################
########### here are the multtest methods - marginal siblings can be found in marginal.R ######################
### one sample model with multtest ###
onesamp.multtest <- function(data, alternative, robust, psi0, alpha, nulldist, B=1000, method, seed=12345) {
result <- MTP(X=data, W = NULL, Y = NULL, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "t.onesamp", robust = robust,
standardize = TRUE, alternative = alternative, psi0 = psi0,
typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted",
alpha = alpha, smooth.null = FALSE, nulldist = nulldist,
B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE,
seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
return(list(adjPValues=result@adjp, rejected=as.vector(result@reject)))
}
mutoss.onesamp.multtest.model <- function(model) {
return("typ" %in% names(model) && model$typ == "onesamp")
}
mutoss.onesamp.multtest <- function() { return(new(Class="MutossMethod",
label="Resampling-based one sample test",
errorControl="FWER",
callFunction="onesamp.multtest",
output=c("adjPValues", "rejected"),
info="<h2>Resampling-based one sample test</h2>
<p>There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\
<ul>
<li>\"boot.cs\": non-parametric bootstrap with centering and scaling</li>\n\
<li>\"boot.ctr\": centered-only bootstrap distribution</li>\n\
<li>\"boot.qt\": quantile transformed bootstrap distribution. the default marginal t-distribution with n-1 degree of freedom is used.</li>\n\
<li>\"perm\": permutation distribution (refering to the Westfall and Young procedures)</li>\n\
<li>\"ic\": under GUI construction (available at (library(multtest)) </li>\n
</ul>
</p>
<p>There are four adjustment methods to control the FWER:
<ul>
<li>\"sd.minP\": step-down common-quantile procedure based on the minima of unadjusted p-values</li>
<li>\"sd.maxT\": step-down common-cut-off procedure based on the maxima of test statistics</li>
<li>\"ss.minP\": single-step common-quantile procedure</li>
<li>\"ss.maxT\": single-step common-cut-off procedure</li>
</ul>
</p>
<p> The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed
of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000. </p>
<p> The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.</p>
<h3>Reference:</h3>
<ul>
<li>Dudoit, S. and van der Laan, M.J. (2007). <i>Mulitple Testing Procedures and Applications to Genomics.</i> Springer Series in Statistics.</li>\n\
<li>Westfall, P.H. and Young, S.S. (1993). <i>Resampling-Based Multiple Testing. Examples and Methods for p-value adjustment. </i>Wiley Series in Probability and Mathematical Statistics. </li>\n
</ul>",
parameters=list(
data=list(type="ANY"),
model=list(type="ANY"),
robust=list(type="logical", label="Robust statistic"),
alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")),
psi0=list(type="numeric", label="Hypothesized null value", default=0),
alpha=list(type="numeric"),
nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")),
B=list(type="numeric", label="Number of Resampling Iterations", default=1000),
method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"),
seed=list(type="ANY", default=12345)
)
))) }
### paired sample model with multtest ###
paired.multtest <- function(data, model, alternative, robust, psi0, alpha, nulldist, B=1000, method, seed=12345) {
result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "t.pair", robust = robust,
standardize = TRUE, alternative = alternative, psi0 = psi0,
typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted",
alpha = alpha, smooth.null = FALSE, nulldist = nulldist,
B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE,
seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
return(list(adjPValues=result@adjp, rejected=as.vector(result@reject)))
}
mutoss.paired.multtest.model <- function(model) {
return("typ" %in% names(model) && model$typ == "pairedsamp")
}
mutoss.paired.multtest <- function() { return(new(Class="MutossMethod",
label="Resampling-based paired sample test",
errorControl="FWER",
callFunction="paired.multtest",
output=c("adjPValues", "rejected"),
info="<h2>Resampling-based paired test</h2>
<p>There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\
<ul>
<li>\"boot.cs\": non-parametric bootstrap with centering and scaling</li>\n\
<li>\"boot.ctr\": centered-only bootstrap distribution</li>\n\
<li>\"boot.qt\": quantile transformed bootstrap distribution. the default marginal t-distribution with n-1 degree
of freedom is used, where n is the number of pairs.</li>\n\
<li>\"perm\": permutation distribution (refering to the Westfall and Young procedures)</li>\n\
<li>\"ic\": under GUI construction (available at (library(multtest)) </li>\n
</ul>
</p>
<p>There are four adjustment methods to control the FWER:
<ul>
<li>\"sd.minP\": step-down common-quantile procedure based on the minima of unadjusted p-values</li>
<li>\"sd.maxT\": step-down common-cut-off procedure based on the maxima of test statistics</li>
<li>\"ss.minP\": single-step common-quantile procedure</li>
<li>\"ss.maxT\": single-step common-cut-off procedure</li>
</ul>
</p>
<p> The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed
of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000. </p>
<p> The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.</p>
<h3>Reference:</h3>
<ul>
<li>Dudoit, S. and van der Laan, M.J. (2007). <i>Mulitple Testing Procedures and Applications to Genomics.</i> Springer Series in Statistics.</li>\n\
<li>Westfall, P.H. and Young, S.S. (1993). <i>Resampling-Based Multiple Testing. Examples and Methods for p-value adjustment. </i>Wiley Series in Probability and Mathematical Statistics. </li>\n
</ul>",
parameters=list(
data=list(type="ANY"),
model=list(type="ANY"),
robust=list(type="logical", label="Robust statistic"),
alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")),
psi0=list(type="numeric", label="Hypothesized null value", default=0),
alpha=list(type="numeric"),
nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")),
B=list(type="numeric", label="Number of Resampling Iterations", default=1000),
method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"),
seed=list(type="ANY", default=12345))
)
)) }
### two sample model with multtest ####
twosamp.multtest <- function(data, model, alternative, robust, psi0, equalvar, alpha, nulldist, B=1000, method, seed=12345) {
if (equalvar) {
result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "t.twosamp.equalvar", robust = robust,
standardize = TRUE, alternative = alternative, psi0 = psi0,
typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted",
alpha = alpha, smooth.null = FALSE, nulldist = nulldist,
B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE,
seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
}
else {
result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "t.twosamp.unequalvar", robust = robust,
standardize = TRUE, alternative = alternative, psi0 = psi0,
typeone = "fwer", k = 0, q = 0.1, fdr.method = "restricted",
alpha = alpha, smooth.null = FALSE, nulldist = nulldist,
B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE,
seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
}
return(list(adjPValues=result@adjp, rejected=as.vector(result@reject)))
}
mutoss.twosamp.multtest.model <- function(model) {
return("typ" %in% names(model) && model$typ == "twosamp")
}
mutoss.twosamp.multtest <- function() { return(new(Class="MutossMethod",
label="Resampling-based two sample test",
errorControl="FWER",
callFunction="twosamp.multtest",
output=c("adjPValues", "rejected"),
info="<h2>Resampling-based two sample test</h2>
<p>There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\
<ul>
<li>\"boot.cs\": non-parametric bootstrap with centering and scaling</li>\n\
<li>\"boot.ctr\": centered-only bootstrap distribution</li>\n\
<li>\"boot.qt\": quantile transformed bootstrap distribution. the default marginal t-distribution with n-1 degree of freedom is used.</li>\n\
<li>\"perm\": permutation distribution (refering to the Westfall and Young procedures)</li>\n\
<li>\"ic\": under GUI construction (available at (library(multtest)) </li>\n
</ul>
</p>
<p>There are four adjustment methods to control the FWER:
<ul>
<li>\"sd.minP\": step-down common-quantile procedure based on the minima of unadjusted p-values</li>
<li>\"sd.maxT\": step-down common-cut-off procedure based on the maxima of test statistics</li>
<li>\"ss.minP\": single-step common-quantile procedure</li>
<li>\"ss.maxT\": single-step common-cut-off procedure</li>
</ul>
</p>
<p> The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed
of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000. </p>
<p> The robust version uses the Wilcoxon-Mann-Whitney test, otherwise a t-test will be performed.</p>
<h3>Reference:</h3>
<ul>
<li>Dudoit, S. and van der Laan, M.J. (2007). <i>Mulitple Testing Procedures and Applications to Genomics.</i> Springer Series in Statistics.</li>\n\
<li>Westfall, P.H. and Young, S.S. (1993). <i>Resampling-Based Multiple Testing. Examples and Methods for p-value adjustment. </i>Wiley Series in Probability and Mathematical Statistics. </li>\n
</ul>",
parameters=list(
data=list(type="ANY"),
model=list(type="ANY"),
robust=list(type="logical", label="Robust statistic"),
alternative=list(type="character", label="Alternative", choices=c("two.sided", "less", "greater")),
psi0=list(type="numeric", label="Hypothesized null value", default=0),
equalvar=list(type="logical", label="Equal variance"),
alpha=list(type="numeric"),
nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")),
B=list(type="numeric", label="Number of Resampling Iterations", default=1000),
method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"),
seed=list(type="ANY", default=12345))
)
)) }
##################### F test #############################################
###########################################################################
ftest.multtest <- function(data, model, robust, alpha, nulldist, B=1000, method, seed=12345) {
result <- MTP(X=data, W = NULL, Y = model$classlabel, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "f", robust = robust,
standardize = TRUE, typeone = "fwer",
alpha = alpha, smooth.null = FALSE, nulldist = nulldist,
B = B, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = method, get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = FALSE, keep.rawdist = FALSE,
seed = seed, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
return(list(adjPValues=result@adjp, rejected=as.vector(result@reject)))
}
mutoss.ftest.multtest <- function() { return(new(Class="MutossMethod",
label="Resampling-based F test",
errorControl="FWER",
callFunction="ftest.multtest",
output=c("adjPValues", "rejected"),
info="<h2>Resampling-based F test</h2>
<p>There are different choices of resampling methods available for estimating the joint test statistics null distribution:\n\
<ul>
<li>\"boot.cs\": non-parametric bootstrap with centering and scaling</li>\n\
<li>\"boot.ctr\": centered-only bootstrap distribution</li>\n\
<li>\"boot.qt\": quantile transformed bootstrap distribution. the default marginal F-distribution with df1=k-1, df2=n-k for k gruops is used.</li>\n\
<li>\"perm\": permutation distribution (refering to the Westfall and Young procedures)</li>\n\
<li>\"ic\": under GUI construction (available at (library(multtest)) </li>\n
</ul>
</p>
<p>There are four adjustment methods to control the FWER:
<ul>
<li>\"sd.minP\": step-down common-quantile procedure based on the minima of unadjusted p-values</li>
<li>\"sd.maxT\": step-down common-cut-off procedure based on the maxima of test statistics</li>
<li>\"ss.minP\": single-step common-quantile procedure</li>
<li>\"ss.maxT\": single-step common-cut-off procedure</li>
</ul>
</p>
<p> The default number of bootstrap iterations (or number of permutations if resampling method is \"perm\") is 1000. This can be reduced to increase the speed
of computations, at a cost to precision. However, it is recommended to use a large number of resampling iterations, e.g. 10,000. </p>
<p> The robust version uses the Kruskal-Wallis test, otherwise a F test will be performed.</p>
<h3>Reference:</h3>
<ul>
<li>Dudoit, S. and van der Laan, M.J. (2007). <i>Mulitple Testing Procedures and Applications to Genomics.</i> Springer Series in Statistics.</li>\n\
<li>Westfall, P.H. and Young, S.S. (1993). <i>Resampling-Based Multiple Testing. Examples and Methods for p-value adjustment. </i>Wiley Series in Probability and Mathematical Statistics. </li>\n
</ul>",
parameters=list(
data=list(type="ANY"),
model=list(type="ANY"),
robust=list(type="logical", label="Robust statistic"),
alpha=list(type="numeric"),
nulldist=list(type="character", label="Resampling Method", choices=c("boot.cs", "boot.ctr", "boot.qt", "perm")),
B=list(type="numeric", label="Number of Resampling Iterations", default=1000),
method=list(type="character", label="Adjustment Method", choices=c("sd.minP","sd.maxT","ss.minP","ss.maxT"),
seed=list(type="ANY", default=12345)
)))
)}
mutoss.ftest.multtest.model <- function(model) {
return("typ" %in% names(model) && model$typ == "ftest")
}
|
