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
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
|
#### Utilities for Sparse Model Matrices
## The "first" version {no longer used}:
fac2sparse <- function(from, to = c("d","i","l","n","z"), drop.unused.levels = TRUE)
{
## factor(-like) --> sparseMatrix {also works for integer, character}
fact <- if (drop.unused.levels) factor(from) else as.factor(from)
levs <- levels(fact)
n <- length(fact)
to <- match.arg(to)
## MM: using new() and then assigning slots has efficiency "advantage"
## of *not* validity checking
res <- new(paste0(to, "gCMatrix"))
res@i <- as.integer(fact) - 1L # 0-based
res@p <- 0:n
res@Dim <- c(length(levs), n)
res@Dimnames <- list(levs, NULL)
if(to != "n")
res@x <- rep.int(switch(to,
"d" = 1., "i" = 1L, "l" = TRUE, "z" = 1+0i),
n)
res
}
## This version can deal with NA's [maybe slightly less efficient (how much?)] :
fac2sparse <- function(from, to = c("d","i","l","n","z"),
drop.unused.levels = TRUE)
{
## factor(-like) --> sparseMatrix {also works for integer, character}
fact <- if (drop.unused.levels) factor(from) else as.factor(from)
levs <- levels(fact)
n <- length(fact)
to <- match.arg(to)
i <- as.integer(fact) - 1L ## 0-based indices
df <- data.frame(i = i, j = seq_len(n) - 1L)[!is.na(i),]
if(to != "n")
df$x <- rep.int(switch(to,
"d" = 1., "i" = 1L, "l" = TRUE, "z" = 1+0i),
nrow(df))
## a version of the following which adapts to a future change of the 1st arg.name of new():
## do.call("new", c(list(Class = paste0(to, "gTMatrix"),
## Dim = c(length(levs), n),
## Dimnames = list(levs, names(fact)))))
argNew <- c(list(Class = paste0(to, "gTMatrix"),
Dim = c(length(levs), n),
Dimnames = list(levs, names(fact))),
df)
names(argNew)[1] <- names(formals(methods::new))[[1]]
as(do.call("new", argNew), "CsparseMatrix")
}
setAs("factor", "sparseMatrix", function(from) fac2sparse(from, to = "d"))
##' fac2Sparse() := fac2sparse w/ contrasts
##'
##' @param from factor of which we want the "contrasted" (indicator)
##' design matrix
##' @param to character string specifying the response type
##' @param drop.unused.level logical indicating if non-present factor
##' levels should be dropped, via factor(from)
##' @param factorPatt12 logical vector fp[] of length 2
##' fp[1] : give contrasted t(X); fp[2] : give "dummy" t(X)
##' @param contrasts.arg character string or NULL or (coercable to)
##' sparseMatrix, specifying the contrast
##'
##' @return a list of length two, each with the corresponding t(model matrix),
##' when the corresponding factorPatt12 is true.
fac2Sparse <- function(from, to = c("d","i","l","n","z"),
drop.unused.levels = TRUE,
factorPatt12, contrasts.arg = NULL)
{
stopifnot(is.logical(factorPatt12), length(factorPatt12) == 2)
if(any(factorPatt12))
m <- fac2sparse(from, to=to,
drop.unused.levels=drop.unused.levels)
##
## code '2' : keep dummy, i.e. no contrasts :
ans <- list(NULL, if(factorPatt12[2]) m)
##
if(factorPatt12[1]) {
## *do* use contrasts.arg
if(is.null(contrasts.arg))
contrasts.arg <- getOption("contrasts")[if(is.ordered(from))
"ordered" else "unordered"]
ans[[1]] <-
crossprod(if(is.character(contrasts.arg)) {
stopifnot(is.function(FUN <- get(contrasts.arg)))
## calling contr.*() with correct level names directly :
FUN(rownames(m), sparse = TRUE)
} else as(contrasts.arg, "sparseMatrix"), m)
}
ans
}
## Goal: a "sparse model.matrix()"
## model.matrix(object, data = environment(object),
## contrasts.arg = NULL, xlev = NULL, ...)
##
## Cut'n'paste from model.matrix() ... just replacing small part at end:
sparse.model.matrix <- function(object, data = environment(object),
contrasts.arg = NULL, xlev = NULL,
transpose = FALSE, ...)
{
t <- if(missing(data)) terms(object) else terms(object, data=data)
if (is.null(attr(data, "terms")))
data <- model.frame(object, data, xlev=xlev)
else {
reorder <- match(sapply(attr(t,"variables"),deparse,
width.cutoff=500)[-1L],
names(data))
if (any(is.na(reorder)))
stop("model frame and formula mismatch in model.matrix()")
if(!identical(reorder, seq_len(ncol(data))))
data <- data[,reorder, drop=FALSE]
}
int <- attr(t, "response")
if(length(data)) { # otherwise no rhs terms, so skip all this
contr.funs <- as.character(getOption("contrasts"))
namD <- names(data)
## turn any character columns into factors
for(i in namD)
if(is.character(data[[i]])) {
data[[i]] <- factor(data[[i]])
warning(gettextf("variable '%s' converted to a factor", i),
domain = NA)
}
isF <- sapply(data, function(x) is.factor(x) || is.logical(x) )
isF[int] <- FALSE
isOF <- sapply(data, is.ordered)
for(nn in namD[isF]) # drop response
if(is.null(attr(data[[nn]], "contrasts")))
contrasts(data[[nn]]) <- contr.funs[1 + isOF[nn]]
## it might be safer to have numerical contrasts:
## get(contr.funs[1 + isOF[nn]])(nlevels(data[[nn]]))
if (!is.null(contrasts.arg) && is.list(contrasts.arg)) {
if (is.null(namC <- names(contrasts.arg)))
stop("invalid 'contrasts.arg' argument")
for (nn in namC) {
if (is.na(ni <- match(nn, namD)))
warning(gettextf("variable '%s' is absent, its contrast will be ignored", nn),
domain = NA)
else {
ca <- contrasts.arg[[nn]]
## FIXME: work for *sparse* ca
if(is.matrix(ca)) contrasts(data[[ni]], ncol(ca)) <- ca
else contrasts(data[[ni]]) <- contrasts.arg[[nn]]
}
}
}
} else { # internal model.matrix needs some variable
isF <- FALSE
data <- cbind(data, x = 0)
}
## <Sparse> src/library/stats/R/models.R has
## ans <- .Internal(model.matrix(t, data))
ans <- model.spmatrix(t, data, transpose=transpose)
## ==============
## </Sparse>
attr(ans, "contrasts") <-
lapply(data[isF], function(x) attr(x, "contrasts"))
ans
} ## {sparse.model.matrix}
##' <description>
##' Produce the t(Z); Z = "design matrix" of (X : Y), where
##' --- t(Z) : aka rowwise -version : "r"
##' <details>
##'
##' @title sparse model matrix for 2-way interaction
##' @param X and Y either are numeric matrices {maybe 1-column}
##' @param Y or "as(<factor>, sparseM)"
##' @param do.names logical
##' @param forceSparse logical
##' @return
##' @author Martin Maechler
sparse2int <- function(X, Y, do.names = TRUE, forceSparse = FALSE)
{
if(do.names) {
dnx <- dimnames(X)
dny <- dimnames(Y)
}
dimnames(Y) <- dimnames(X) <- list(NULL,NULL)
nx <- nrow(X)
ny <- nrow(Y)
r <-
if((nX <- is.numeric(X)) | (nY <- is.numeric(Y))) {
if(nX) {
if (nY || nx > 1) { # both numeric, or X >=2 "columns"
F <- if(forceSparse) function(m) .Call(dense_to_Csparse, m) else identity
F((if(ny == 1) X else X[rep.int(seq_len(nx), ny) , ]) *
(if(nx == 1) Y else Y[rep (seq_len(ny),each=nx), ]))
}
else { ## numeric X (1 "column"), sparseMatrix Y
r <- Y
dp <- Y@p[-1] - Y@p[-(Y@Dim[2]+1L)]
## stopifnot(all(dp %in% 0:1)) # just for now
## if(nx == 1)
## FIXME: similar trick would be applicable for nx > 2
r@x <- X[dp == 1L] * Y@x
r
}
}
else { ## sparseMatrix X, numeric Y
if(ny == 1) {
## FIXME: similar trick would be applicable for ny > 2
r <- X
dp <- X@p[-1] - X@p[-(X@Dim[2]+1L)]
stopifnot(all(dp %in% 0:1)) # just for now - drop! - FIXME
r@x <- Y[dp == 1L] * X@x
r
}
else { ## ny > 1 -- *larger* matrix
X[rep.int(seq_len(nx), ny) , ] *
(if(nx == 1) Y else Y[rep(seq_len(ny),each=nx), ])
}
}
}
else { ## X & Y are both sparseMatrix
(if(ny == 1) X else X[rep.int(seq_len(nx), ny) , ]) *
(if(nx == 1) Y else Y[rep (seq_len(ny),each=nx) , ])
}
if(do.names) {
## FIXME: This names business needs a good solution..
## but maybe "up in the caller"
if(!is.null(dim(r)) &&
!is.null(nX <- dnx[[1]]) &&
!is.null(nY <- dny[[1]]))
rownames(r) <- outer(nX, nY, paste, sep = ":")
}
r
}
##' @param rList: list(.) of (transposed) single-factor model matrices,
##' belonging to, say, factors a, b, c,...
##' @param do.names
##' @return the model matrix corresponding to a:b:...
sparseInt.r <- function(rList, do.names = TRUE, forceSparse = FALSE) {
m <- length(rList)
F <- if(forceSparse) {
function(m) if(is.matrix(m)) .Call(dense_to_Csparse, m) else m
} else identity
if(m == 1) {
F(rList[[1]])
} else {
## recurse
F(sparse2int(sparseInt.r(rList[-m], do.names=do.names),
rList[[m]], do.names=do.names))
}
}
## not used currently
is.model.frame <- function(x)
{
## Purpose: check if x is a "valid" model.frame
## ------------------------------------------------------------
## Author: Martin Maechler, Date: 3 Jul 2009
is.data.frame(x) &&
!is.null(tms <- attr(x, "terms")) &&
inherits(tms, "terms") && ## is.terms() would be better
inherits(tms, "formula") &&
is.matrix(attr(tms, "factors")) &&
is.language(vv <- attr(tms, "variables")) &&
vv[[1]] == as.symbol("list") &&
all((vars <- sapply(as.list(vv[-1]), as.character)) %in% colnames(x))
## and we could go on testing vars
}
##' <description>
##' Create a sparse model matrix from a model frame.
##' -- This version uses 'rBind' and returns X' i.e. t(X) :
##' <details>
##'
##' @title Sparse Model Matrix from Model Frame
##' @param trms a "terms" object
##' @param mf a data frame, typically resulting from model.frame()
##' @param transpose logical indicating if X' = t(X) {is faster!}
##' or X should be returned
##' @param drop.unused.levels logical indicating if unused factor
##' levels should be dropped
##' @param row.names
##' @return sparse matrix (class "dgCMatrix")
##' @author Martin Maechler
model.spmatrix <- function(trms, mf, transpose=FALSE,
drop.unused.levels = TRUE, row.names=TRUE)
{
## Author: Martin Maechler, Date: 7 Jul 2009
## mf is a model frame or a "simple" data.frame [after reorder !]
stopifnot(is.data.frame(mf))
n <- nrow(mf)
if(row.names)
rnames <- row.names(mf)
## mf: make into list, dropping all attributes (but the names)
fnames <- names(mf <- unclass(mf))
attributes(mf) <- list(names = fnames)
if(length(factorPattern <- attr(trms, "factors"))) {
d <- dim(factorPattern)
nVar <- d[1]
nTrm <- d[2]
n.fP <- dimnames(factorPattern)
fnames <- n.fP[[1]] # == names of variables {incl. "F(var)"} in the model
Names <- n.fP[[2]] # == colnames == names of terms: "a", "b:c", ...
} else { ## degenerate, e.g. 'Y ~ 1'
nVar <- nTrm <- 0L
fnames <- Names <- character(0)
}
## all the "variables in the model" are also in "mf", including "sin(x)";
## actually, ..../src/main/model.c even assumes
stopifnot((m <- length(mf)) >= nVar)
if(m > nVar) mf <- mf[seq_len(nVar)]
stopifnot(fnames == names(mf))
noVar <- nVar == 0
##>> this seems wrong; we use 1:nVar for indexing mf[] below ..
##>> if(noVar) nVar <- 1L # (as in ~/R/D/r-devel/R/src/main/model.c)
## Note: "character" variables have been changed to factor in the caller;
## hence: both factor and *logical* should be dealt as factor :
is.f <- if(noVar) logical(0) else sapply(mf, function(.) is.factor(.) | is.logical(.))
indF <- which(is.f)
hasInt <- attr(trms, "intercept") == 1
## the degree of interaction:
intOrder <- attr(trms, "order")
##
if(!hasInt && length(indF)) {
## change the '1' of the first factor into a '2' :
if(any(i1 <- factorPattern[indF, ] == 1))
## replace at the first '1' location:
factorPattern[indF,][which.max(i1)] <- 2L
else {}
## nothing to do
}
## Convert "factors" to "Rowwise- sparseMatrix ("dummy"-matrix) -----------
## Result: a list of sparse model matrices for the "factor"s :
f.matr <- structure(vector("list", length = length(indF)),
names = fnames[indF])
i.f <- 0
## ---- For each variable in the model -------------------
for(i in seq_len(nVar)) {
nam <- fnames[i]
f <- mf[[i]]
if(is.f[i]) {
fp <- factorPattern[i,] ## == factorPattern[nam,]
contr <- attr(f, "contrasts")
f.matr[[(i.f <- i.f + 1)]] <- # a list of 2
lapply(fac2Sparse(f, to = "d",
drop.unused.levels=drop.unused.levels,
factorPatt12 = 1:2 %in% fp,
contrasts.arg = contr),
function(s) {
if(is.null(s)) return(s)
## else
rownames(s) <-
paste0(nam, if(is.null(rownames(s)))
## for some contr.*(), have lost rownames; hmm..
seq_len(nrow(s)) else rownames(s))
s
})
} else { ## continuous variable --> "matrix" - for all of them
if(any(iA <- (cl <- class(f)) == "AsIs")) # drop "AsIs" class
class(f) <- if(length(cl) > 1L) cl[!iA]
nr <- if(is.matrix(f)) nrow(f <- t(f)) else (dim(f) <- c(1L, length(f)))[1]
if(is.null(rownames(f)))
rownames(f) <- if(nr == 1) nam else paste0(nam, seq_len(nr))
mf[[i]] <- f
}
}
## FIXME: do all this in C --
getR <- function(N) # using 'nm'
if(!is.null(r <- f.matr[[N]])) r[[factorPattern[N, nm]]] else mf[[N]]
vNms <- "(Intercept)"[hasInt]
counts <- integer(nTrm)
r <-
if(hasInt) ## column of 1's - as sparse
new("dgCMatrix", i = 0:(n-1L), p = c(0L, n),
Dim = c(n, 1L), x = rep.int(1, n))
else new("dgCMatrix", Dim = c(n, 0L))
if(transpose) r <- t(r)
iTrm <- seq_len(nTrm)
for(j in iTrm) { ## j-th term
nm <- Names[j]
nmSplits <- strsplit(nm, ":", fixed=TRUE)[[1]]
rj <- sparseInt.r(lapply(nmSplits, getR), do.names=TRUE, forceSparse = TRUE)
## fast version of cbind2() / rbind2(), w/o checks, dimnames, etc
r <- if(transpose) .Call(Csparse_vertcat, r, rj)
else .Call(Csparse_horzcat, r, t(rj))
vNms <- c(vNms, dimnames(rj)[[1]])
counts[j] <- nrow(rj)
}
rns <- if(row.names) rnames
dimnames(r) <- if(transpose) list(vNms, rns) else list(rns, vNms)
attr(r, "assign") <- c(if(hasInt) 0L, rep(iTrm, counts))
r
} ## model.spmatrix()
## For now --- to help people who used to use this as part of 'Matrix':
model.Matrix <- function(object, ...) {
.Deprecated(msg = paste(
"model.Matrix() has been moved from package 'Matrix' to the new package",
"'MatrixModels' which is now loaded (if installed).",
" Its use from package 'Matrix' is deprecated.",
"Do use it from 'MatrixModels' instead.", sep='\n'))
MatrixModels::model.Matrix(object, ...)
}
|
