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Type 'q()' to quit R. > ### Testing positive definite matrices > > ## for R_DEFAULT_PACKAGES=NULL : > library(stats) > library(utils) > > library(Matrix) > source(system.file("test-tools.R", package = "Matrix"))# identical3() etc Loading required package: tools > cat("doExtras:",doExtras,"\n") doExtras: FALSE > > h9 <- Hilbert(9) > stopifnot(c(0,0) == dim(Hilbert(0)), + c(9,9) == dim(h9), + identical(h9@factors, list())) > str(h9)# no 'factors' 32b: -96.73694669 2.08e-8 Formal class 'dpoMatrix' [package "Matrix"] with 5 slots ..@ Dim : int [1:2] 9 9 ..@ Dimnames:List of 2 .. ..$ : NULL .. ..$ : NULL ..@ x : num [1:81] 1 0.5 0.333 0.25 0.2 ... ..@ uplo : chr "U" ..@ factors : list() > assert.EQ.(c(determinant(h9)$modulus), -96.7369487, tol = 8e-8) Mean relative difference: 6.46945e-08 > ## 64b: -96.73695078 2.15e-8 then 6.469e-8 > > ## determinant() now working via chol(): ==> h9 now has factorization > stopifnot(names(h9@factors) == "Cholesky", + identical(ch9 <- Cholesky(h9, perm = FALSE), h9@factors$Cholesky)) > str(f9 <- as(ch9, "dtrMatrix")) Formal class 'dtrMatrix' [package "Matrix"] with 5 slots ..@ Dim : int [1:2] 9 9 ..@ Dimnames:List of 2 .. ..$ : NULL .. ..$ : NULL ..@ x : num [1:81] 1 0 0 0 0 0 0 0 0 0.5 ... ..@ uplo : chr "U" ..@ diag : chr "N" > round(f9, 3) ## round() preserves 'triangular' ! 9 x 9 Matrix of class "dtrMatrix" [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 1.000 0.500 0.333 0.250 0.200 0.167 0.143 0.125 0.111 [2,] . 0.289 0.289 0.260 0.231 0.206 0.186 0.168 0.154 [3,] . . 0.075 0.112 0.128 0.133 0.133 0.130 0.126 [4,] . . . 0.019 0.038 0.052 0.063 0.070 0.075 [5,] . . . . 0.005 0.012 0.019 0.027 0.033 [6,] . . . . . 0.001 0.004 0.007 0.010 [7,] . . . . . . 0.000 0.001 0.002 [8,] . . . . . . . 0.000 0.000 [9,] . . . . . . . . 0.000 > stopifnot(all.equal(rcond(h9), 9.0938e-13), + all.equal(rcond(f9), 9.1272e-7, tolerance = 1e-6))# more precision fails > options(digits=4) > (cf9 <- crossprod(f9))# looks the same as h9 : 9 x 9 Matrix of class "dpoMatrix" [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 1.0000 0.5000 0.33333 0.25000 0.20000 0.16667 0.14286 0.12500 0.11111 [2,] 0.5000 0.3333 0.25000 0.20000 0.16667 0.14286 0.12500 0.11111 0.10000 [3,] 0.3333 0.2500 0.20000 0.16667 0.14286 0.12500 0.11111 0.10000 0.09091 [4,] 0.2500 0.2000 0.16667 0.14286 0.12500 0.11111 0.10000 0.09091 0.08333 [5,] 0.2000 0.1667 0.14286 0.12500 0.11111 0.10000 0.09091 0.08333 0.07692 [6,] 0.1667 0.1429 0.12500 0.11111 0.10000 0.09091 0.08333 0.07692 0.07143 [7,] 0.1429 0.1250 0.11111 0.10000 0.09091 0.08333 0.07692 0.07143 0.06667 [8,] 0.1250 0.1111 0.10000 0.09091 0.08333 0.07692 0.07143 0.06667 0.06250 [9,] 0.1111 0.1000 0.09091 0.08333 0.07692 0.07143 0.06667 0.06250 0.05882 > assert.EQ.mat(h9, as(cf9,"matrix"), tol=1e-15) > > h9. <- round(h9, 2) # dpo->dsy > h9p <- pack(h9) > ch9p <- Cholesky(h9p, perm = FALSE) > stopifnot(identical(ch9p, h9p@factors$pCholesky), + identical(names(h9p@factors), c("Cholesky", "pCholesky"))) > h4 <- h9.[1:4, 1:4] # this and the next > h9.[1,1] <- 10 # had failed in 0.995-14 > h9p[1,1] <- 10 > stopifnotValid(h9., "symmetricMatrix") > stopifnotValid(h9p, "symmetricMatrix") > stopifnotValid(h4, "symmetricMatrix") > > h9p[1,2] <- 99 > stopifnot(class(h9p) == "dgeMatrix", h9p[1,1:2] == c(10,99)) > > str(h9p <- as(h9, "dppMatrix"))# {again} Formal class 'dppMatrix' [package "Matrix"] with 5 slots ..@ uplo : chr "U" ..@ Dim : int [1:2] 9 9 ..@ Dimnames:List of 2 .. ..$ : NULL .. ..$ : NULL ..@ x : num [1:45] 1 0.5 0.333 0.333 0.25 ... ..@ factors :List of 1 .. ..$ Cholesky:Formal class 'Cholesky' [package "Matrix"] with 5 slots .. .. .. ..@ uplo : chr "U" .. .. .. ..@ x : num [1:81] 1 0 0 0 0 0 0 0 0 0.5 ... .. .. .. ..@ perm : int(0) .. .. .. ..@ Dim : int [1:2] 9 9 .. .. .. ..@ Dimnames:List of 2 .. .. .. .. ..$ : NULL .. .. .. .. ..$ : NULL > h6 <- h9[1:6,1:6] > stopifnot(all(h6 == Hilbert(6)), length(h6@factors) == 0) > stopifnotValid(th9p <- t(h9p), "dppMatrix") > stopifnotValid(h9p@factors$Cholesky,"Cholesky") > H6 <- as(h6, "packedMatrix") > pp6 <- as(H6, "dppMatrix") > po6 <- as(pp6, "dpoMatrix") > hs <- as(h9p, "dspMatrix") > stopifnot(names(H6@factors) == "pCholesky", + names(pp6@factors) == "pCholesky", + names(hs@factors) == "Cholesky") # for now > chol(hs) # and that is cached in 'hs' too : 9 x 9 Matrix of class "dtpMatrix" [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 1.000e+00 5.000e-01 3.333e-01 2.500e-01 2.000e-01 1.667e-01 1.429e-01 [2,] . 2.887e-01 2.887e-01 2.598e-01 2.309e-01 2.062e-01 1.856e-01 [3,] . . 7.454e-02 1.118e-01 1.278e-01 1.331e-01 1.331e-01 [4,] . . . 1.890e-02 3.780e-02 5.250e-02 6.299e-02 [5,] . . . . 4.762e-03 1.190e-02 1.948e-02 [6,] . . . . . 1.196e-03 3.589e-03 [7,] . . . . . . 3.002e-04 [8,] . . . . . . . [9,] . . . . . . . [,8] [,9] [1,] 1.250e-01 1.111e-01 [2,] 1.684e-01 1.540e-01 [3,] 1.304e-01 1.265e-01 [4,] 7.015e-02 7.483e-02 [5,] 2.652e-02 3.263e-02 [6,] 6.765e-03 1.031e-02 [7,] 1.051e-03 2.241e-03 [8,] 7.523e-05 3.009e-04 [9,] . 1.885e-05 > stopifnot(names(hs@factors) %in% c("Cholesky","pCholesky"), + all.equal(h9, crossprod(as(hs@factors$pCholesky, "dtpMatrix")), + tolerance = 1e-13), + all.equal(h9, crossprod(as(hs@factors$ Cholesky, "dtrMatrix")), + tolerance = 1e-13)) > > hs@x <- 1/h9p@x # is not pos.def. anymore > validObject(hs) # "but" this does not check [1] TRUE > stopifnot(diag(hs) == seq(1, by = 2, length.out = 9)) > > s9 <- solve(h9p, seq(nrow(h9p))) > signif(t(s9)/10000, 4)# only rounded numbers are platform-independent [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.0729 -5.76 109.5 -864.9 3468 -7668 9459 -6095 1597 > (I9 <- h9p %*% s9) 9 x 1 Matrix of class "dgeMatrix" [,1] [1,] 1 [2,] 2 [3,] 3 [4,] 4 [5,] 5 [6,] 6 [7,] 7 [8,] 8 [9,] 9 > m9 <- as.matrix(1:9) > stopifnot(all.equal(m9, as(I9, "matrix"), tolerance = 2e-9)) > > ### Testing nearPD() --- this is partly in ../man/nearPD.Rd : > pr <- Matrix(c(1, 0.477, 0.644, 0.478, 0.651, 0.826, + 0.477, 1, 0.516, 0.233, 0.682, 0.75, + 0.644, 0.516, 1, 0.599, 0.581, 0.742, + 0.478, 0.233, 0.599, 1, 0.741, 0.8, + 0.651, 0.682, 0.581, 0.741, 1, 0.798, + 0.826, 0.75, 0.742, 0.8, 0.798, 1), + nrow = 6, ncol = 6) > > nL <- + list(r = nearPD(pr, conv.tol = 1e-7), # default + r.1 = nearPD(pr, conv.tol = 1e-7, corr = TRUE), + rs = nearPD(pr, conv.tol = 1e-7, doDykstra=FALSE), + rs1 = nearPD(pr, conv.tol = 1e-7, doDykstra=FALSE, corr = TRUE), + rH = nearPD(pr, conv.tol = 1e-15), + rH.1= nearPD(pr, conv.tol = 1e-15, corr = TRUE)) > > sapply(nL, `[`, c("iterations", "normF")) r r.1 rs rs1 rH rH.1 iterations 2 12 2 11 2 31 normF 0.06262 0.07429 0.06262 0.07438 0.06262 0.07429 > > allnorms <- function(d) vapply(c("1","I","F","M","2"), + norm, x = d, double(1)) > > ## "F" and "M" distances are larger for the (corr=TRUE) constrained: > 100 * sapply(nL, function(rr) allnorms((pr - rr $ mat))) r r.1 rs rs1 rH rH.1 1 8.889 8.805 8.889 8.746 8.889 8.805 I 8.889 8.805 8.889 8.746 8.889 8.805 F 6.262 7.429 6.262 7.438 6.262 7.429 M 2.655 2.860 2.655 2.785 2.655 2.860 2 6.262 6.418 6.262 6.461 6.262 6.418 > > ## But indeed, the 'corr = TRUE' constraint yield a better solution, > ## if you need the constraint : cov2cor() does not just fix it up : > 100 * (nn <- sapply(nL, function(rr) allnorms((pr - cov2cor(rr $ mat))))) r r.1 rs rs1 rH rH.1 1 9.994 8.805 9.994 8.746 9.994 8.805 I 9.994 8.805 9.994 8.746 9.994 8.805 F 8.391 7.429 8.391 7.438 8.391 7.429 M 3.544 2.860 3.544 2.785 3.544 2.860 2 6.322 6.418 6.322 6.461 6.322 6.418 > > stopifnot( + all.equal(nn["1",], + c(r =0.0999444286984696, r.1= 0.0880468666522317, + rs=0.0999444286984702, rs1= 0.0874614179943388, + rH=0.0999444286984696, rH.1=0.0880468927726625), + tolerance=1e-9)) > > nr <- nL $rH.1 $mat > stopifnot( + all.equal(nr[lower.tri(nr)], + c(0.4877861230299, 0.6429309061748, 0.4904554299278, 0.6447150779852, + 0.8082100656035, 0.514511537243, 0.2503412693503, 0.673249718642, + 0.7252316891977, 0.5972811755863, 0.5818673040157, 0.7444549621769, + 0.7308954865819, 0.7713984381710, 0.8124321235679), + tolerance = 1e-9)) > showProc.time() Time (user system elapsed): 0.37 0.08 0.45 > > > suppressWarnings(RNGversion("3.5.0")); set.seed(27) > m9 <- h9 + rnorm(9^2)/1000 ; m9 <- (m9 + t(m9))/2 > nm9 <- nearPD(m9) > showProc.time() Time (user system elapsed): 0.03 0 0.03 > > nRep <- if(doExtras) 50 else 4 > CPU <- 0 > for(M in c(5, 12)) + for(i in 1:nRep) { + m <- matrix(round(rnorm(M^2),2), M, M) + m <- m + t(m) + diag(m) <- pmax(0, diag(m)) + 1 + m <- cov2cor(m) + CPU <- CPU + system.time(n.m <- nearPD(m, base.matrix=TRUE))[1] + X <- n.m$mat + stopifnot(all.equal(X, (X + t(X))/2, tolerance = 8*.Machine$double.eps), + all.equal(eigen(n.m$mat, only.values=TRUE)$values, + n.m$eigenvalues, tolerance = 4e-8)) + } > cat('Time elapsed for ',nRep, 'nearPD(): ', CPU,'\n') Time elapsed for 4 nearPD(): 0 > showProc.time() Time (user system elapsed): 0.99 0 0.99 > > ## cov2cor() > m <- diag(6:1) %*% as(pr,"matrix") %*% diag(6:1) # so we can "vector-index" > m[upper.tri(m)] <- 0 > ltm <- which(lower.tri(m)) > ne <- length(ltm) > set.seed(17) > m[ltm[sample(ne, 3/4*ne)]] <- 0 > m <- (m + t(m))/2 # now is a covariance matrix with many 0 entries > (spr <- Matrix(m)) 6 x 6 sparse Matrix of class "dsCMatrix" [1,] 36.000 7.155 . . 3.906 . [2,] 7.155 25.000 . . . . [3,] . . 16.000 . 2.324 . [4,] . . . 9.000 2.223 . [5,] 3.906 . 2.324 2.223 4.000 . [6,] . . . . . 1 > cspr <- cov2cor(spr) > ev <- eigen(cspr, only.values = TRUE)$values > stopifnot(is(spr, "dsCMatrix"), + is(cspr,"dsCMatrix"), + all.equal(ev, c(1.5901626099, 1.1902658504, 1, 1, + 0.80973414959, 0.40983739006), tolerance=1e-10)) > > x <- c(2,1,1,2) > mM <- Matrix(x, 2,2, dimnames=rep( list(c("A","B")), 2))# dsy > mM 2 x 2 Matrix of class "dsyMatrix" A B A 2 1 B 1 2 > stopifnot(length(mM@factors)== 0) > (po <- as(mM, "dpoMatrix")) # still has dimnames 2 x 2 Matrix of class "dpoMatrix" A B A 2 1 B 1 2 > mm <- as(mM, "matrix") > msy <- as(mm, "symmetricMatrix") > stopifnot(Qidentical(mM, msy), + length(mM @factors)== 1, + length(msy@factors)== 0) > > c1 <- as(mm, "corMatrix") > c2 <- as(mM, "corMatrix") > c3 <- as(po, "corMatrix") > (co.x <- matrix(x/2, 2,2)) [,1] [,2] [1,] 1.0 0.5 [2,] 0.5 1.0 > checkMatrix(c1) norm(m [2 x 2]) : 1 I F M ok Summary: ok 2*m =?= m+m: ok m >= m for all: ok m < m for none: ok symmpart(m) + skewpart(m) == m: ok; determinant(): ok > assert.EQ.mat(c1, co.x) > assert.EQ.mat(c2, co.x) # failed in Matrix 0.999375-9, because of > ## the wrong automatic "dsyMatrix" -> "corMatrix" coerce method > stopifnot(identical(dimnames(c1), dimnames(mM)), + all.equal(c1, c3, tolerance =1e-15)) > > showProc.time() Time (user system elapsed): 0.68 0 0.68 > > > proc.time() user system elapsed 3.35 0.23 3.57