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Type 'q()' to quit R. > ## for R_DEFAULT_PACKAGES=NULL : > library(stats) > library(utils) > > library(Matrix) > > ## This is example(sp....) -- much extended > > mEQ <- function(x, y, check.attributes = NA, ...) { + ## first drop columns from y which are all 0 : + if(any(i0 <- colSums(abs(x)) == 0)) { + message(gettextf("x had %d zero-columns", sum(i0))) + x <- x[, !i0, drop = FALSE] + } + if(any(i0 <- colSums(abs(y)) == 0)) { + message(gettextf("y had %d zero-columns", sum(i0))) + y <- y[, !i0, drop = FALSE] + } + isTRUE(all.equal(x, y, tolerance = 0, check.attributes = check.attributes, ...)) + } > > ##' Is sparse.model.matrix() giving the "same" as dense model.matrix() ? > ##' > ##' @return logical > ##' @param frml formula > ##' @param dat data frame > ##' @param showFactors > ##' @param ... further arguments passed to {sparse.}model.matrix() > isEQsparseDense <- function(frml, dat, + showFactors = isTRUE(getOption("verboseSparse")), ...) + { + ## Author: Martin Maechler, Date: 21 Jul 2009 + stopifnot(inherits(frml, "formula"), is.data.frame(dat)) + if(showFactors) + print(attr(terms(frml, data=dat), "factors")) + smm <- sparse.model.matrix(frml, dat, ...) + mm <- model.matrix(frml, dat, ...) + sc <- smm@contrasts + mEQ(as(smm, "generalMatrix"), Matrix(mm, sparse=TRUE)) & + identical(smm@assign, attr(mm, "assign")) & + (if(is.null(mc <- attr(mm, "contrasts"))) length(sc) == 0 else identical(sc, mc)) + } > > ### ------------ all the "datasets" we construct for use ------------- > dd <- data.frame(a = gl(3,4), b = gl(4,1,12))# balanced 2-way > (dd3 <- cbind(dd, c = gl(2,6), d = gl(3,8))) a b c d 1 1 1 1 1 2 1 2 1 1 3 1 3 1 1 4 1 4 1 1 5 2 1 1 1 6 2 2 1 1 7 2 3 2 1 8 2 4 2 1 9 3 1 2 2 10 3 2 2 2 11 3 3 2 2 12 3 4 2 2 13 1 1 1 2 14 1 2 1 2 15 1 3 1 2 16 1 4 1 2 17 2 1 1 3 18 2 2 1 3 19 2 3 2 3 20 2 4 2 3 21 3 1 2 3 22 3 2 2 3 23 3 3 2 3 24 3 4 2 3 > dd. <- dd3[- c(1, 13:15, 17), ] > set.seed(17) > dd4 <- cbind(dd, c = gl(2,6), d = gl(8,3)) > dd4 <- cbind(dd4, x = round(rnorm(nrow(dd4)), 1)) > dd4 <- dd4[- c(1, 13:15, 17), ] > ##-> 'd' has unused levels > dM <- dd4 > dM$X <- outer(10*rpois(nrow(dM), 2), 1:3) > dM$Y <- cbind(pmax(0, dM$x - .3), floor(4*rnorm(nrow(dM)))) > str(dM)# contains *matrices* 'data.frame': 19 obs. of 7 variables: $ a: Factor w/ 3 levels "1","2","3": 1 1 1 2 2 2 2 3 3 3 ... $ b: Factor w/ 4 levels "1","2","3","4": 2 3 4 1 2 3 4 1 2 3 ... $ c: Factor w/ 2 levels "1","2": 1 1 1 1 1 2 2 2 2 2 ... $ d: Factor w/ 8 levels "1","2","3","4",..: 1 1 2 2 2 3 3 3 4 4 ... $ x: num -0.1 -0.2 -0.8 0.8 -0.2 1 1.7 0.3 0.4 1.2 ... $ X: num [1:19, 1:3] 10 20 40 0 20 10 20 30 20 10 ... $ Y: num [1:19, 1:2] 0 0 0 0.5 0 0.7 1.4 0 0.1 0.9 ... > > options("contrasts") # the default: "contr.treatment" $contrasts unordered ordered "contr.treatment" "contr.poly" > op <- options(sparse.colnames = TRUE) # for convenience > > stopifnot(identical(## non-sensical, but "should work" (with a warning each): + sparse.model.matrix(a~ 1, dd), + sparse.model.matrix( ~ 1, dd))) > sparse.model.matrix(~ a + b, dd, contrasts.arg = list(a="contr.sum")) 12 x 6 sparse Matrix of class "dgCMatrix" (Intercept) a1 a2 b2 b3 b4 1 1 1 . . . . 2 1 1 . 1 . . 3 1 1 . . 1 . 4 1 1 . . . 1 5 1 . 1 . . . 6 1 . 1 1 . . 7 1 . 1 . 1 . 8 1 . 1 . . 1 9 1 -1 -1 . . . 10 1 -1 -1 1 . . 11 1 -1 -1 . 1 . 12 1 -1 -1 . . 1 > sparse.model.matrix(~ a + b, dd, contrasts.arg = list(b="contr.SAS")) 12 x 6 sparse Matrix of class "dgCMatrix" (Intercept) a2 a3 b1 b2 b3 1 1 . . 1 . . 2 1 . . . 1 . 3 1 . . . . 1 4 1 . . . . . 5 1 1 . 1 . . 6 1 1 . . 1 . 7 1 1 . . . 1 8 1 1 . . . . 9 1 . 1 1 . . 10 1 . 1 . 1 . 11 1 . 1 . . 1 12 1 . 1 . . . > xm <- sparse.model.matrix(~ x, dM) # {no warning anymore ...} > dxm <- Matrix(model.matrix(~ x, dM), sparse=TRUE) > stopifnot(is(xm, "sparseMatrix"), mEQ(as(xm,"generalMatrix"), dxm)) > > ## Sparse method is equivalent to the traditional one : > stopifnot(isEQsparseDense(~ a + b, dd), + suppressWarnings(isEQsparseDense(~ x, dM)), + isEQsparseDense(~ 0 + a + b, dd), + identical(sparse.model.matrix(~ 0 + a + b, dd), + sparse.model.matrix(~ -1 + a + b, dd)), + isEQsparseDense(~ a + b, dd, contrasts.arg = list(a="contr.sum")), + isEQsparseDense(~ a + b, dd, contrasts.arg = list(a="contr.SAS")), + ## contrasts as *functions* or contrast *matrices* : + isEQsparseDense(~ a + b, dd, + contrasts.arg = list( + a=contr.sum, + b=contr.treatment(4))), + isEQsparseDense(~ a + b, dd, + contrasts.arg = list( + a=contr.SAS(3), + b = function(n, contr=TRUE, sparse=FALSE) + contr.sum(n=n, contrasts=contr, sparse=sparse)))) > > sm <- sparse.model.matrix(~a * b, dd, + contrasts.arg = list(a=contr.SAS(3, sparse=TRUE))) > sm 12 x 12 sparse Matrix of class "dgCMatrix" (Intercept) a1 a2 b2 b3 b4 a1:b2 a2:b2 a1:b3 a2:b3 a1:b4 a2:b4 1 1 1 . . . . . . . . . . 2 1 1 . 1 . . 1 . . . . . 3 1 1 . . 1 . . . 1 . . . 4 1 1 . . . 1 . . . . 1 . 5 1 . 1 . . . . . . . . . 6 1 . 1 1 . . . 1 . . . . 7 1 . 1 . 1 . . . . 1 . . 8 1 . 1 . . 1 . . . . . 1 9 1 . . . . . . . . . . . 10 1 . . 1 . . . . . . . . 11 1 . . . 1 . . . . . . . 12 1 . . . . 1 . . . . . . > ## FIXME: Move part of this to ../../MatrixModels/tests/ > ##stopifnot(all(sm == model.Matrix( ~a * b, dd, contrasts= list(a= contr.SAS(3))))) > > ## > stopifnot(isEQsparseDense(~ a + b + c + d, dd.)) > stopifnot(isEQsparseDense(~ a + b:c + c + d, dd.)) > ## no intercept -- works too > stopifnot(isEQsparseDense(~ -1+ a + b + c + d, dd.)) > stopifnot(isEQsparseDense(~ 0 + a + b:c + c + d, dd.)) > > > Sparse.model.matrix <- function(...) { + s <- sparse.model.matrix(...) + as(s, "generalMatrix")# dropping 'assign',.. slots + } > ## > dim(mm <- Matrix(model.matrix(~ a + b + c + d, dd4), sparse=TRUE)) [1] 19 14 > dim(sm <- Sparse.model.matrix(~ a + b + c + d, dd4)) [1] 19 14 > ## was (19 13), when 'drop.unused.levels' was implicitly TRUE > dim(sm. <- Sparse.model.matrix(~ a + b + c + d, dd4, drop.unused.levels=TRUE)) [1] 19 13 > stopifnot(mEQ(sm , mm), ## (both have a zero column) + mEQ(sm., mm)) ## << that's ok, since mm has all-0 column ! x had 1 zero-columns y had 1 zero-columns y had 1 zero-columns > ## look at this : > all(mm[,"d5"] == 0) ## !!!! --- correct: a column of all 0 <--> dropped level! [1] TRUE > stopifnot(all.equal(sm., mm[, - which("d5" == colnames(mm))], ## indeed ! + check.attributes = NA)) > ## i.e., sm has just dropped an all zero column --- which it should! > > stopifnot(isEQsparseDense(~ 1 + sin(x) + b*c + a:x, dd4, showFactors=TRUE)) sin(x) b c b:c a:x sin(x) 1 0 0 0 0 b 0 1 0 1 0 c 0 0 1 1 0 a 0 0 0 0 2 x 0 0 0 0 2 > > stopifnot(isEQsparseDense(~ I(a) + b*c + a:x, dd4, showFactors=TRUE)) I(a) b c b:c a:x I(a) 1 0 0 0 0 b 0 1 0 1 0 c 0 0 1 1 0 a 0 0 0 0 2 x 0 0 0 0 2 > ## no intercept -- works too > stopifnot(isEQsparseDense(~ 0+ I(a) + b*c + a:x, dd4, showFactors=TRUE)) I(a) b c b:c a:x I(a) 1 0 0 0 0 b 0 1 0 1 0 c 0 0 1 1 0 a 0 0 0 0 2 x 0 0 0 0 2 > > f <- ~ 1 + a + b*c + a*x > attr(terms(f, data=dd4), "factors") a b c x b:c a:x a 1 0 0 0 0 1 b 0 1 0 0 1 0 c 0 0 1 0 1 0 x 0 0 0 1 0 1 > dim(mm <- Matrix(model.matrix(f, data=dd4), sparse=TRUE)) [1] 19 13 > dim(sm <- Sparse.model.matrix(f, data=dd4)) # == [1] 19 13 > stopifnot(mEQ(sm, mm)) > > f <- ~ a*X + X*Y + a*c > attr(terms(f, data=dM), "factors") a X Y c a:X X:Y a:c a 1 0 0 0 1 0 1 X 0 1 0 0 1 1 0 Y 0 0 1 0 0 1 0 c 0 0 0 1 0 0 1 > dim(mm <- Matrix(model.matrix(f, data=dM), sparse=TRUE)) [1] 19 23 > dim(sm <- Sparse.model.matrix(f, data=dM, verbose=TRUE)) model.spmatrix(t, data, ...) with t = Classes 'terms', 'formula' language ~a * X + X * Y + a * c model.spmatrix(): (n=19, nVar=4 (m=4), nTrm=7) --> indF = a c 1 4 ---> f.matr list : List of 2 $ a:List of 2 $ c:List of 2 term[ 1] "a" .. .sparse.interaction.N([1], fS=TRUE): is.mat=(.) -- concatenating (r, rj): dim = (19, 1) | ( 2,19) term[ 2] "X" .. .sparse.interaction.N([1], fS=TRUE): is.mat=(|) -- concatenating (r, rj): dim = (19, 3) | ( 3,19) term[ 3] "Y" .. .sparse.interaction.N([1], fS=TRUE): is.mat=(|) -- concatenating (r, rj): dim = (19, 6) | ( 2,19) term[ 4] "c" .. .sparse.interaction.N([1], fS=TRUE): is.mat=(.) -- concatenating (r, rj): dim = (19, 8) | ( 1,19) term[ 5] "a:X" .. .sparse.interaction.N([2], fS=TRUE): is.mat=(.|) .sparse.interaction.2([2], [3]) -- concatenating (r, rj): dim = (19, 9) | ( 6,19) term[ 6] "X:Y" .. .sparse.interaction.N([2], fS=TRUE): is.mat=(||) .sparse.interaction.2([3], [2]) -- concatenating (r, rj): dim = (19,15) | ( 6,19) term[ 7] "a:c" .. .sparse.interaction.N([2], fS=TRUE): is.mat=(..) .sparse.interaction.2([2], [1]) -- concatenating (r, rj): dim = (19,21) | ( 2,19) [1] 19 23 > stopifnot(mEQ(sm, mm)) > > ## high order > f <- ~ a:b:X:c:Y > mm <- Matrix(model.matrix(f, data=dM), sparse=TRUE) > sm <- Sparse.model.matrix(f, data=dM, verbose=2) model.spmatrix(t, data, ...) with t = Classes 'terms', 'formula' language ~a:b:X:c:Y model.spmatrix(): (n=19, nVar=5 (m=5), nTrm=1) --> indF = a b c 1 2 4 ---> f.matr list : List of 3 $ a:List of 2 ..$ : NULL ..$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots $ b:List of 2 ..$ : NULL ..$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots $ c:List of 2 ..$ : NULL ..$ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots term[ 1] "a:b:X:c:Y" .. .sparse.interaction.N([5], fS=TRUE): is.mat=(..|.|) .sparse.interaction.2([3], [4]) .sparse.interaction.2([12], [3]) .sparse.interaction.2([36], [2]) .sparse.interaction.2([72], [2]) -- concatenating (r, rj): dim = (19, 1) | (144,19) > stopifnot(mEQ(sm, mm)) x had 102 zero-columns y had 102 zero-columns > > > f <- ~ 1 + a + b*c + a*x + b*d*x + b:c:d > attr(terms(f, data=dd4), "factors") a b c x d b:c a:x b:d b:x x:d b:x:d b:c:d a 1 0 0 0 0 0 1 0 0 0 0 0 b 0 1 0 0 0 1 0 1 1 0 1 2 c 0 0 1 0 0 1 0 0 0 0 0 1 x 0 0 0 1 0 0 1 0 1 1 1 0 d 0 0 0 0 1 0 0 1 0 1 1 1 > dim(mm <- Matrix(model.matrix(f, data=dd4), sparse=TRUE)) ## 19 100 [1] 19 100 > dim(sm <- Sparse.model.matrix(f, data=dd4)) ## (ditto) [1] 19 100 > dim(sm. <- Sparse.model.matrix(f, data=dd4, drop.unused.levels=TRUE)) # 19 88 [1] 19 88 > stopifnot(mEQ(sm, mm), mEQ(sm., mm))# {32, 32; 20 and 32 zero-columns ..} x had 32 zero-columns y had 32 zero-columns x had 20 zero-columns y had 32 zero-columns > > ## now get a bit courageous: > ## > > ## stopifnot(isEQsparseDense(~ 1 + c + a:b:d, dat=dd4)) > dim(mm <- Matrix(model.matrix(~ 1 + a + b*c + a:b:c:d, data=dd4), + sparse=TRUE)) ## 19 202 [1] 19 202 > dim(sm <- Sparse.model.matrix(~ 1 + a + b*c + a:b:c:d, data=dd4)) [1] 19 202 > dim(sm. <- Sparse.model.matrix(~ 1 + a + b*c + a:b:c:d, data=dd4, + drop.unused.levels=TRUE)) [1] 19 178 > stopifnot(mEQ(sm, mm), mEQ(sm., mm))# {173, 173, 149 and 173 zero-columns !} x had 173 zero-columns y had 173 zero-columns x had 149 zero-columns y had 173 zero-columns > > ## stopifnot(isEQsparseDense(~ 1 + a + b*c + a:b:c:d, dat=dd4)) > dim(mm <- Matrix(model.matrix(~ 1 + a + b:c + a:b:d, data=dd4), + sparse=TRUE)) ## 19 107 [1] 19 107 > dim(sm <- Sparse.model.matrix(~ 1 + a + b:c + a:b:d, data=dd4)) [1] 19 107 > dim(sm. <- Sparse.model.matrix(~ 1 + a + b:c + a:b:d, data=dd4, + drop.unused.levels=TRUE)) [1] 19 95 > stopifnot(mEQ(sm, mm), mEQ(sm., mm)) x had 77 zero-columns y had 77 zero-columns x had 65 zero-columns y had 77 zero-columns > > dim(mm <- Matrix(model.matrix(~ a*b*c +c*d, dd4), sparse=TRUE)) ## 19 38 [1] 19 38 > dim(sm <- Sparse.model.matrix(~ a*b*c +c*d, dd4))# (ditto) [1] 19 38 > dim(sm. <- Sparse.model.matrix(~ a*b*c +c*d, dd4, drop.unused.levels=TRUE)) [1] 19 36 > stopifnot(mEQ(sm, mm), mEQ(sm., mm)) x had 5 zero-columns y had 5 zero-columns x had 3 zero-columns y had 5 zero-columns > > > f1 <- ~ (a+b+c+d)^2 + (a+b):c:d + a:b:c:d > f2 <- ~ (a+b+c+d)^4 - a:b:c - a:b:d > mm1 <- Matrix(model.matrix(f1, dd4), sparse=TRUE) > dim(mm2 <- Matrix(model.matrix(f2, dd4), sparse=TRUE)) [1] 19 198 > sm1 <- sparse.model.matrix(f1, dd4) > dim(sm2 <- sparse.model.matrix(f2, dd4)) [1] 19 198 > s.1 <- sparse.model.matrix(f1, dd4, drop.unused.levels=TRUE) > dim(s.2 <- sparse.model.matrix(f2, dd4, drop.unused.levels=TRUE)) [1] 19 174 > stopifnot(identical(mm1,mm2), + identical(sm1,sm2), identical(s.1,s.2), + mEQ(sm1,mm1), mEQ(s.1,mm1)) x had 120 zero-columns y had 120 zero-columns x had 96 zero-columns y had 120 zero-columns > > str(dd <- data.frame(d = gl(10,6), a = ordered(gl(3,20)))) 'data.frame': 60 obs. of 2 variables: $ d: Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 2 2 2 2 ... $ a: Ord.factor w/ 3 levels "1"<"2"<"3": 1 1 1 1 1 1 1 1 1 1 ... > X. <- sparse.model.matrix(~ a + d, data = dd) > ## failed because of contr.poly default in Matrix 0.999375-33 > stopifnot(dim(X.) == c(60, 12), nnzero(X.) == 234, + isEQsparseDense(~ 0 + d + I(as.numeric(d)^2), dd)) > ## I(.) failed (upto 2010-05-07) > > ## When the *contrasts* are sparse : > spC <- as(contrasts(dd$d), "sparseMatrix") > ddS <- dd > contrasts(ddS$d) <- spC > Xs <- sparse.model.matrix(~ a + d, data=ddS) > stopifnot(exprs = { + inherits(spC, "sparseMatrix") + identical(spC, contrasts(ddS[,"d"])) + mEQ(X., Xs) + }) > > ## Fixing matrix-Bugs [#6673] by Davor Josipovic > df <- data.frame('a' = factor(1:3), 'b' = factor(4:6)) > Cid <- lapply(df, contrasts, contrasts=FALSE) > CidS <- lapply(df, contrasts, contrasts=FALSE, sparse=TRUE) > X2 <- sparse.model.matrix(~ . -1, data = df, contrasts.arg = Cid) > X2S <- sparse.model.matrix(~ . -1, data = df, contrasts.arg = CidS) > X2 3 x 6 sparse Matrix of class "dgCMatrix" a1 a2 a3 b4 b5 b6 1 1 . . 1 . . 2 . 1 . . 1 . 3 . . 1 . . 1 > stopifnot(all.equal(X2, X2S, tolerance = 0, check.attributes = NA)) > ## X2S was missing the last column ('b6') in Matrix <= 1.x-y > > > ## Fixing (my repr.ex.) of Matrix bug [#6657] by Nick Hanewinckel > mkD <- function(n, p2 = 2^ceiling(log2(n)), sd = 10, rf = 4) { + stopifnot(p2 >= n, n >= 0, p2 %% 2 == 0) + G <- gl(2, p2/2, labels=c("M","F"))[sample.int(p2, n)] + data.frame(sex = G, + age = round(rf*rnorm(n, mean=32 + 2*as.numeric(G), sd=sd)) / rf) + } > set.seed(101) > D1 <- mkD(47) > Xs <- sparse.model.matrix(~ sex* poly(age, 2), data = D1) > ## Error in model.spmatrix(..): no slot of name "i" for .. class "dgeMatrix" > validObject(Xs) [1] TRUE > stopifnot(exprs = { + identical(c(47L, 6L), dim(Xs)) + identical(colnames(Xs)[3:6], + c(1:2, outer("sexF", 1:2, paste, sep=":"))) + all(Xs == model.matrix(~ sex* poly(age, 2), data = D1)) + }) > > > > cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' Time elapsed: 1.59 0.12 1.7 NA NA > > if(!interactive()) warnings() > > proc.time() user system elapsed 1.59 0.12 1.70