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
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
|
fourthcorner.rlq <- function(xtest, nrepet = 999, modeltype = 6, typetest = c("axes","Q.axes","R.axes"), p.adjust.method.G = p.adjust.methods, p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global","levels"), ...)
{
## test RLQ axes
if (!inherits(xtest, "dudi"))
stop("Object of class dudi expected")
if (!inherits(xtest, "rlq"))
stop("Object of class 'rlq' expected")
if (!(modeltype %in% c(2, 4, 5, 6)))
stop("modeltype should be 2, 4, 5 or 6")
if(modeltype == 6){
test1 <- fourthcorner.rlq(xtest, modeltype = 2,nrepet = nrepet, typetest = typetest, p.adjust.method.G = p.adjust.method.G, p.adjust.method.D = p.adjust.method.D, p.adjust.D = p.adjust.D)
test2 <- fourthcorner.rlq(xtest, modeltype = 4,nrepet = nrepet, typetest = typetest, p.adjust.method.G = p.adjust.method.G, p.adjust.method.D = p.adjust.method.D, p.adjust.D = p.adjust.D)
res <- combine.4thcorner(test1, test2)
res$call <- res$tabD2$call <- res$tabD$call <- res$tabG$call <- match.call()
return(res)
}
p.adjust.D <- match.arg(p.adjust.D)
p.adjust.method.D <- match.arg(p.adjust.method.D)
p.adjust.method.G <- match.arg(p.adjust.method.G)
typetest <- match.arg(typetest)
appel <- as.list(xtest$call)
dudiR <- eval.parent(appel$dudiR)
dudiQ <- eval.parent(appel$dudiQ)
dudiL <- eval.parent(appel$dudiL)
tabR.cw <- dudiR$cw
appelR <- as.list(dudiR$call)
tabR <- Rinit <- eval.parent(appelR$df)
## Test the different cases
## typ=1 no modification (PCA on original variable)
## typ=2 ACM
## typ=3 normed and centred PCA
## typ=4 centred PCA
## typ=5 normed and non-centred PCA
## typ=6 COA
## typ=7 FCA
## typ=8 Hill-smith
typR <- dudi.type(dudiR$call)
##------- index can takes 2 values (1 for quantitative / 2 for factor) --------#
if (typR %in% c(1, 3, 4, 5, 6, 7)) {
indexR <- rep(1, ncol(Rinit))
assignR <- 1:ncol(Rinit)
} else if (typR == 2) {
indexR <- rep(2, ncol(Rinit))
assignR <- rep(1:ncol(Rinit), apply(Rinit, 2, function(x) nlevels(as.factor(x))))
Rinit <- acm.disjonctif(Rinit)
} else if (typR == 8) {
provinames <- "tmp"
indexR <- ifelse(dudiR$index == "q", 1, 2)
assignR <- as.numeric(dudiR$assign)
res <- matrix(0, nrow(Rinit), 1)
for (j in 1:(ncol(Rinit))) {
if (indexR[j] == 1) {
res <- cbind(res, Rinit[, j])
provinames <- c(provinames,names(Rinit)[j])
}
else if (indexR[j] == 2) {
w <- fac2disj(Rinit[, j], drop = TRUE)
res <- cbind(res, w)
provinames <- c(provinames, paste(substr(names(Rinit)[j], 1, 5), ".", names(w), sep = ""))
}
}
Rinit <- res[,-1]
colnames(Rinit) <- provinames[-1]
} else stop ("Not yet available")
tabQ.cw <- dudiQ$cw
appelQ <- as.list(dudiQ$call)
tabQ <- Qinit <- eval.parent(appelQ$df)
typQ <- dudi.type(dudiQ$call)
if (typQ %in% c(1, 3, 4, 5, 6, 7)) {
indexQ <- rep(1,ncol(Qinit))
assignQ <- 1:ncol(Qinit)
} else if (typQ == 2) {
indexQ <- rep(2, ncol(Qinit))
assignQ <- rep(1:ncol(Qinit),apply(Qinit, 2, function(x) nlevels(as.factor(x))))
Qinit <- acm.disjonctif(Qinit)
} else if (typQ == 8) {
provinames <- "tmp"
indexQ <- ifelse(dudiQ$index=="q",1,2)
assignQ <- as.numeric(dudiQ$assign)
res <- matrix(0, nrow(Qinit), 1)
for (j in 1:(ncol(Qinit))) {
if (indexQ[j] == 1) {
res <- cbind(res, Qinit[, j])
provinames <- c(provinames,names(Qinit)[j])
}
else if (indexQ[j] == 2) {
w <- fac2disj(Qinit[, j])
res <- cbind(res, w)
provinames <- c(provinames, paste(substr(names(Qinit)[j], 1, 5), ".", names(w), sep = ""))
}
}
Qinit <- res[,-1]
colnames(Qinit) <- provinames[-1]
} else stop ("Not yet available")
appelL <- as.list(dudiL$call)
tabL <- eval.parent(appelL$df)
tabL.cw <- dudiL$cw
tabL.lw <- dudiL$lw
ncolQ <- ncol(Qinit)
ncolR <- ncol(Rinit)
nvarR <- ncol(tabR)
nvarQ <- ncol(tabQ)
## Dimensions for D ang G matrices
naxes <- xtest$nf
if(typetest=="axes"){
ncolD <- ncolG <- naxes
nrowD <- nrowG <- naxes
typeTestN <- 1
} else if (typetest=="Q.axes"){
ncolD <- ncolG <- naxes
nrowD <- ncolQ
nrowG <- nvarQ
typeTestN <- 3
} else if(typetest=="R.axes"){
ncolD <- ncolR
ncolG <- nvarR
nrowD <- nrowG <- naxes
typeTestN <- 2
}
##----- create objects to store results -------#
tabD <- matrix(0, nrepet + 1, nrowD * ncolD)
tabD2 <- matrix(0, nrepet + 1, nrowD * ncolD)
tabG <- matrix(0, nrepet + 1, nrowG * ncolG)
res <- list()
##------------------
## Call the C code
##------------------
res <- .C("quatriemecoinRLQ",
as.double(t(Rinit)),
as.double(t(tabL)),
as.double(t(Qinit)),
as.integer(ncolR),
as.integer(nvarR),
as.integer(nrow(tabL)),
as.integer(ncol(tabL)),
as.integer(ncolQ),
as.integer(nvarQ),
as.integer(nrepet),
modeltype = as.integer(modeltype),
tabD = as.double(tabD),
tabD2 = as.double(tabD2),
tabG = as.double(tabG),
as.integer(nrowD),
as.integer(ncolD),
as.integer(nrowG),
as.integer(ncolG),
as.integer(indexR),
as.integer(indexQ),
as.integer(assignR),
as.integer(assignQ),
as.double(t(xtest$c1)),
as.double(t(xtest$l1)),
as.integer(typeTestN),
as.integer(naxes),
as.integer(typR),
as.integer(typQ),
as.double(tabR.cw),
as.double(tabQ.cw),
PACKAGE="ade4")[c("tabD","tabD2","tabG")]
##-------------------------------------------------------------------#
## Outputs #
##-------------------------------------------------------------------#
if(typetest == "axes"){
res$varnames.Q <- res$colnames.Q <- names(xtest$lQ)
res$varnames.R <- res$colnames.R <- names(xtest$lR)
res$assignR <- res$assignQ <- 1:naxes
res$indexR <- res$indexQ <- rep(1,naxes)
} else if (typetest == "Q.axes"){
res$varnames.Q <- names(tabQ)
res$colnames.Q <- colnames(Qinit)
res$varnames.R <- res$colnames.R <- names(xtest$lR)
res$indexQ <- indexQ
res$assignQ <- assignQ
res$assignR <- 1:naxes
res$indexR <- rep(1,naxes)
} else if(typetest == "R.axes"){
res$varnames.Q <- res$colnames.Q <- names(xtest$lQ)
res$varnames.R <- names(tabR)
res$colnames.R <- colnames(Rinit)
res$indexR <- indexR
res$assignR <- assignR
res$assignQ <- 1:naxes
res$indexQ <- rep(1,naxes)
}
## set invalid permutation to NA (in the case of levels of a factor with no observation)
res$tabD <- ifelse(res$tabD < (-998), NA, res$tabD)
res$tabG <- ifelse(res$tabG < (-998), NA, res$tabG)
## Reshape the tables
res$tabD <- matrix(res$tabD, nrepet + 1, nrowD * ncolD, byrow = TRUE)
res$tabD2 <- matrix(res$tabD2, nrepet + 1, nrowD * ncolD, byrow = TRUE)
res$tabG <- matrix(res$tabG, nrepet + 1, nrowG * ncolG, byrow = TRUE)
## Create vectors to store type of statistics and alternative hypotheses
names.stat.D <- vector(mode="character")
names.stat.D2 <- vector(mode="character")
names.stat.G <- vector(mode="character")
alter.G <- vector(mode="character")
alter.D <- vector(mode="character")
alter.D2 <- vector(mode="character")
for (i in 1:nrowG){
for (j in 1:ncolG){
## Type of statistics for G and alternative hypotheses
if ((res$indexR[j]==1)&(res$indexQ[i]==1)){
names.stat.G <- c(names.stat.G, "r")
alter.G <- c(alter.G, "two-sided")
}
if ((res$indexR[j]==1)&(res$indexQ[i]==2)){
names.stat.G <- c(names.stat.G, "F")
alter.G <- c(alter.G, "greater")
}
if ((res$indexR[j]==2)&(res$indexQ[i]==1)){
names.stat.G <- c(names.stat.G, "F")
alter.G <- c(alter.G, "greater")
}
}
}
for (i in 1:nrowD){
for (j in 1:ncolD){
## Type of statistics for D and alternative hypotheses
if ((res$indexR[res$assignR[j]]==1)&(res$indexQ[res$assignQ[i]]==1)){
names.stat.D <- c(names.stat.D, "r")
names.stat.D2 <- c(names.stat.D2, "r")
alter.D <- c(alter.D, "two-sided")
alter.D2 <- c(alter.D2, "two-sided")
}
if ((res$indexR[res$assignR[j]]==1)&(res$indexQ[res$assignQ[i]]==2)){
names.stat.D <- c(names.stat.D, "Homog.")
names.stat.D2 <- c(names.stat.D2, "r")
alter.D <- c(alter.D, "less")
alter.D2 <- c(alter.D2, "two-sided")
}
if ((res$indexR[res$assignR[j]]==2)&(res$indexQ[res$assignQ[i]]==1)){
names.stat.D <- c(names.stat.D, "Homog.")
names.stat.D2 <- c(names.stat.D2, "r")
alter.D <- c(alter.D, "less")
alter.D2 <- c(alter.D2, "two-sided")
}
}
}
provinames <- apply(expand.grid(res$colnames.R, res$colnames.Q), 1, paste, collapse=" / ")
res$tabD <- as.krandtest(obs = res$tabD[1, ], sim = res$tabD[-1, , drop = FALSE], names = provinames, alter = alter.D, call = match.call(), p.adjust.method = p.adjust.method.D)
res$tabD2 <- as.krandtest(obs = res$tabD2[1, ], sim = res$tabD2[-1, , drop = FALSE], names = provinames, alter = alter.D2, call = match.call(), p.adjust.method = p.adjust.method.D)
if(p.adjust.D == "levels"){
## adjustment only between levels of a factor (corresponds to the original paper of Legendre et al. 1997)
for (i in 1:nrowG){
for (j in 1:ncolG){
idx.varR <- which(res$assignR == j)
idx.varQ <- which(res$assignQ == i)
idx.vars <- ncolG * (idx.varQ - 1) + idx.varR
res$tabD$adj.pvalue[idx.vars] <- p.adjust(res$tabD$pvalue[idx.vars], method = p.adjust.method.D)
res$tabD2$adj.pvalue[idx.vars] <- p.adjust(res$tabD2$pvalue[idx.vars], method = p.adjust.method.D)
}
}
res$tabD$adj.method <- res$tabD2$adj.method <- paste(p.adjust.method.D, "by levels")
}
provinames <- apply(expand.grid(res$varnames.R, res$varnames.Q), 1, paste, collapse=" / ")
res$tabG <- as.krandtest(obs = res$tabG[1, ], sim = res$tabG[-1, , drop = FALSE], names = provinames, alter = alter.G, call = match.call(), p.adjust.method = p.adjust.method.G)
res$tabD$statnames <- names.stat.D
res$tabD2$statnames <- names.stat.D2
res$tabG$statnames <- names.stat.G
res$call <- match.call()
res$model <- modeltype
res$npermut <- nrepet
class(res) <- "4thcorner"
return(res)
}
|
