R Under development (unstable) (2023-12-04 r85659 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > require(copula) Loading required package: copula > doX <- FALSE # no "doExtras" -- be fast > > demo(gof_graph) demo(gof_graph) ---- ~~~~~~~~~ > ## Copyright (C) 2012 Marius Hofert and Martin Maechler > ## > ## This program is free software; you can redistribute it and/or modify it under > ## the terms of the GNU General Public License as published by the Free Software > ## Foundation; either version 3 of the License, or (at your option) any later > ## version. > ## > ## This program is distributed in the hope that it will be useful, but WITHOUT > ## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS > ## FOR A PARTICULAR PURPOSE. See the GNU General Public License for more > ## details. > ## > ## You should have received a copy of the GNU General Public License along with > ## this program; if not, see . > > > ### setup ###################################################################### > > require(copula) > doPDF <- !dev.interactive(orNone=TRUE) > if(!(exists("setSeeds") && is.logical(as.logical(setSeeds)))) + setSeeds <- TRUE # for reproducibility > ## maybe set to FALSE *before* running this demo > > if(interactive()) readline( + "NOTE: Set doX <- TRUE before running this demo 'realistically' ok? ") > if(!(exists("doX") && is.logical(as.logical(doX)))) + print(doX <- copula:::doExtras()) > if(!doX) + cat("** doX is FALSE ==> unrealistically small N, but speed for testing.\n") ** doX is FALSE ==> unrealistically small N, but speed for testing. > (N <- if(doX) 256 else 32)# be fast when run as "check" [1] 32 > ### Example 1: 5d Gumbel copula ################################################ > > ## setup > n <- if(doX) 1000 else 250 # sample size > d <- 5 # dimension > family <- "Gumbel" # copula family > tau <- 0.5 > if(setSeeds) set.seed(1) > ## define and sample the copula (= H0 copula), build pseudo-observations > cop <- getAcop(family) > th <- cop@iTau(tau) # correct parameter value > copH0 <- onacopulaL(family, list(th, 1:d)) # define H0 copula > U. <- pobs(rCopula(n, cop=copH0)) > ## create array of pairwise copH0-transformed data columns > cu.u <- pairwiseCcop(U., copH0) > stopifnot(is.array(cu.u), dim(cu.u) == c(n,d,d)) # check > ## compute pairwise matrix of p-values and corresponding colors > pwIT <- pairwiseIndepTest(cu.u, N=N, verbose=interactive()) # (d,d)-matrix of test results > round(pmat <- pviTest(pwIT), 3) # pick out p-values [,1] [,2] [,3] [,4] [,5] [1,] NA 0.924 0.530 0.379 0.985 [2,] 0.682 NA 0.439 0.439 0.682 [3,] 0.924 0.652 NA 0.652 0.924 [4,] 0.682 0.742 0.439 NA 0.712 [5,] 0.924 0.682 0.924 0.864 NA > ## Here (with seed=1): no significant ones, smallest = 0.0603 > str(cc <- pairsColList(pmat)) # compute corresponding colors Loading required namespace: colorspace List of 4 $ fgColMat : chr [1:5, 1:5] "#000000" "#000000" "#000000" "#000000" ... $ bgColMat : chr [1:5, 1:5] "transparent" "#F4EFC8" "#F4EFC8" "#F4EFC8" ... $ bucketCols : chr [1:6] "#0066CC" "#995CD8" "#D35DD3" "#FFAD8B" ... $ pvalueBuckets: num [1:7] 1e-04 1e-03 1e-02 5e-02 1e-01 ... > ## which pairs violate H0? [none, here] > which(pmat < 0.05, arr.ind=TRUE) row col > ## check whether p-values are uniform -- only if we have "many" (~ d^2) > if(d > 10){ + n. <- d*(d-1) + qqplot(qunif(ppoints(n.)), sort(pmat), main = paste("n = ",n.)) + abline(0,1) + } > ## Artificially more extreme P-values: {to see more} > pm.0 <- pmat > pm.0[1,2] <- 5.0e-3 > pm.0[3,2] <- 0.03 > pm.0[5,2] <- 0.9e-3 > ### Example 1 b): Plots ######################################################## > > ## 1a) plain (too large plot symbols here) > pairsRosenblatt(cu.u, pvalueMat=pmat) > ## 1b) More reasonably plotting char {and more extreme P-values} > pairsRosenblatt(cu.u, pvalueMat=pm.0, pch=".") > ## 2) with title, no subtitle > pwRoto <- "Pairwise Rosenblatt transformed observations" > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=pwRoto, sub=NULL) > ## 3) with title and manual subtitle > (gp <- format(gpviTest(pmat), digits=1, nsmall=1)) holm hochberg hommel bonferroni BH BY none "1.0" "1.0" "1.0" "1.0" "1.0" "1.0" "0.4" > sub <- paste(names(gp), gp, sep=": ") > sub. <- paste(paste(sub[1:3], collapse=", "), "\n", + paste(sub[4:7], collapse=", "), sep="") > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=pwRoto, sub=sub., line.sub=5.4) > ## 4) two-line title including expressions, and centered --- JCGS, Fig.3 (left) --- > title <- list(paste(pwRoto, "to test"), + substitute(italic(H[0]^s:C~~bold("is Gumbel with"~~tau==tau.)), + list(tau.=tau))) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", + main=title, line.main=c(4, 1.4), main.centered=TRUE) > ## 5) omit panel borders > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", panel.border=FALSE) > pairsRosenblatt(cu.u, pvalueMat=pm.0, pch=".", panel.border=FALSE) > ## 6) without axes > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", axes=FALSE) > ## 7) without axes and borders > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", axes=FALSE, panel.border=FALSE) > pairsRosenblatt(cu.u, pvalueMat=pm.0, pch=".", panel.border=FALSE) > ## 8) adjust colors: make black colors of the dots less dominant > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", col=adjustcolor("black", 0.5)) > pairsRosenblatt(cu.u, pvalueMat=pm.0, pch=".", col=adjustcolor("black", 0.5)) > ## 9) use your own colors > > ##' Adjust color list as returned by pairsColList() > ##' @param colList color list as returned by pairsColList() > ##' @param diag foreground color on the diagonal > ##' @param bgDiag background color on the diagonal > ##' @param adj.f # alpha-factor for off-diagonal colors > ##' @return adjusted colList object > ##' @author Martin Maechler > colAdj <- function(colList, diag = c("firebrick", "chocolate3", "darkorange2", + "royalblue3", "deepskyblue3"), + bgDiag = "gray94", adj.f = 0.5) { + ## diag should recycle (?) stopifnot(length(diag) == ncol(colList$bgColMat)) + diag(colList$bgColMat) <- bgDiag # adjust background color on the diagonal + diag(colList$fgColMat) <- diag # adjust foreground color on the diagonal + colList$fgColMat[colList$fgColMat == "#000000"] <- + adjustcolor("black", adj.f) # adjust off-diagonal foreground colors + colList + } > ## compute colList and adjust (more complicated examples are possible by > ## providing bgColMat etc. to pairsColList) > colList <- colAdj(pairsColList(pmat, bg.col="greenish")) # use different color scheme > ## define your own colors > require(colorspace) Loading required package: colorspace > bg.col.bottom <- as(hex2RGB("#16493C"), "polarLUV")@coords[3:1] > bg.col.top <- as(hex2RGB("#69BADA"), "polarLUV")@coords[3:1] > colLi.0 <- colAdj(pairsColList(pm.0, bg.col.bottom=bg.col.bottom, + bg.col.top=bg.col.top)) > ## call pairsRosenblatt with the new colors > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", colList=colList) > pairsRosenblatt(cu.u, pvalueMat=pm.0, pch=".", colList=colLi.0) > ## 10) plot just colors (axis labels are automagically removed) > pairsRosenblatt(cu.u, pvalueMat=pmat, method="none") > ## 11) also remove labels on the diagonal > pairsRosenblatt(cu.u, pvalueMat=pmat, method="none", labels="n") > ## 12) Q-Q plots -- can, in general, better detect outliers > pairsRosenblatt(cu.u, pvalueMat=pmat, method="QQchisq", cex=0.2) > ## pairsRosenblatt(cu.u, pvalueMat=pmat, method="QQchisq", cex=0.2, > ## panel=function(x, y, ...){ > ## points(x, y, ...) > ## qqline(y) ## only makes sense for the normal distribution > ## }) > ## => maybe in the future with a more general function for qqline (supporting > ## other distributions) > > ## 13) P-P plots -- actually, MM sees *more* (though outliers are invisible) > pairsRosenblatt(cu.u, pvalueMat=pmat, method="PPchisq") > pairsRosenblatt(cu.u, pvalueMat=pmat, method="PPchisq", + panel=function(x, y, ...){ + points(x, y, ...) + abline(0, 1, col="cyan") # add straight line + }) > ### Example 1 c): Boundary cases ############################################### > > ## Note: this is only for checking "boundary input cases", it does not make > ## sense given the data. > > ## 1) one pdiv, within range(pmat) > pmat <- matrix(c(NA, 0.1, 0.2, 0.3, 0.4, + 0.2, NA, 0.3, 0.4, 0.5, + 0.3, 0.4, NA, 0.5, 0.6, + 0.4, 0.5, 0.6, NA, 0.7, + 0.5, 0.6, 0.7, 0.8, NA), + nrow=5, ncol=5) > colList <- pairsColList(pmat, pdiv=0.2, signif.P=0.2) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", colList=colList) > ## 2) one pdiv, pdiv < min(pmat) > colList <- pairsColList(pmat, pdiv=0.05, signif.P=0.05) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", colList=colList) > ## 3) one pdiv, pdiv > max(pmat) > colList <- pairsColList(pmat, pdiv=0.9, signif.P=0.9) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", colList=colList) > ## 4) one pdiv, equal to all values of pmat > pmat <- matrix(0.05, nrow=5, ncol=5) > diag(pmat) <- NA > colList <- pairsColList(pmat, pdiv=0.05, signif.P=0.05) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", colList=colList) > ## => plot color key up to 1 (see pairsColList()) > > > ### Example 2: (2,3)-nested Gumbel copula ###################################### > > ## setup > n <- if(doX) 1000 else 250 # sample size > d <- 5 # dimension > family <- "Gumbel" # copula family > if(setSeeds) set.seed(2) > ## define and sample the copula, build pseudo-observations > cop <- getAcop(family) > th <- cop@iTau(tau <- c(0.2, 0.4, 0.6)) > nacList <- list(th[1], NULL, list(list(th[2], 1:2), list(th[3], 3:d))) > copG <- onacopulaL(family, nacList=nacList) > U <- rCopula(n, cop=copG) > U. <- pobs(U) > ## define the H0 copula > th0 <- cop@iTau(tau0 <- c(0.2, 0.4, 0.4)) # wrong 2nd-sector-parameter > nacList <- list(th0[1], NULL, list(list(th0[2], 1:2), list(th0[3], 3:d))) > copH0 <- onacopulaL(family, nacList) > ## create array of pairwise copH0-transformed data columns > cu.u <- pairwiseCcop(U., copH0) > ## compute pairwise matrix of p-values and corresponding colors > pwIT <- pairwiseIndepTest(cu.u, N=N, verbose=interactive()) # (d,d)-matrix of test results > round(pmat <- pviTest(pwIT), 3) # pick out p-values [,1] [,2] [,3] [,4] [,5] [1,] NA 0.924 0.591 0.591 0.591 [2,] 0.894 NA 0.197 0.955 0.682 [3,] 0.591 0.258 NA 0.015 0.015 [4,] 0.591 0.955 0.015 NA 0.015 [5,] 0.500 0.712 0.015 0.015 NA > cc <- pairsColList(pmat) # compute corresponding colors > ## which pairs violate H0? > which(pmat < 0.05, arr.ind=TRUE) row col [1,] 4 3 [2,] 5 3 [3,] 3 4 [4,] 5 4 [5,] 3 5 [6,] 4 5 > ## now we got! > > ## pairwise Rosenblatt plot > title <- list(paste(pwRoto, "to test"), + substitute(italic(H[0]^s:C~~bold("is nested Gumbel with"~~ + tau[0]==tau0*","~~ + tau[1]==tau1*","~~ + tau[2]==tau2)), + list(tau0=tau0[1], tau1=tau0[2], tau2=tau0[3]))) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=title) > ## --- JCGS, Fig.4 (left) --- > if(doPDF) pdf(file=(file <- "gof_graph_fig-nG-scatter.pdf")) > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=title, + line.main=c(4, 1.4), main.centered=TRUE) > if(doPDF) dev.off() pdf 2 > ## --- JCGS, Fig.4 (right) --- > if(doPDF) pdf(file=(file <- "gof_graph_fig-nG-QQ.pdf")) > pairsRosenblatt(cu.u, pvalueMat=pmat, method="QQchisq", cex=0.2, main=title, + line.main=c(4, 1.4), main.centered=TRUE) > if(doPDF) dev.off() pdf 2 > ### Example 3: 5d t_4 copula (fixed/known d.o.f., estimated P) ################# > > ## setup > n <- if(doX) 1000 else 250 # sample size > d <- 5 # dimension > family <- "t" # copula family > df <- 4 # degrees of freedom > if(setSeeds) set.seed(4) > ## define and sample the copula, build pseudo-observations > tau <- c(0.2, 0.4, 0.6) > r <- iTau(tCopula(), tau) > P <- c(r[2], r[1], r[1], r[1], # upper triangle (without diagonal) of correlation "matrix" + r[1], r[1], r[1], + r[3], r[3], + r[3]) > copt4 <- ellipCopula(family, param=P, dim=d, dispstr="un", df=df, df.fixed=TRUE) > U <- rCopula(n, cop=copt4) > U. <- pobs(U) > ## define the H0 copula > ## Note: that's the same result when using pseudo-observations since estimation via > ## tau is invariant strictly increasing transformations > stopifnot(require(Matrix)) Loading required package: Matrix > P. <- nearPD(iTau(tCopula(), cor(U., method="kendall")), corr=TRUE)$mat # estimate P > P.. <- P2p(P.) > plot(P, P.., asp=1); abline(0,1, col=adjustcolor("gray", 0.9)) # P. should be close to P > copH0 <- ellipCopula(family, param=P.., dim=d, dispstr="un", df=df, df.fixed=TRUE) > ## create array of pairwise copH0-transformed data columns > cu.u <- pairwiseCcop(U., copH0, df=df) > ## compute pairwise matrix of p-values and corresponding colors > pwIT <- pairwiseIndepTest(cu.u, N=N, verbose=interactive()) # (d,d)-matrix of test results > round(pmat <- pviTest(pwIT), 3) # pick out p-values [,1] [,2] [,3] [,4] [,5] [1,] NA 0.833 0.924 0.833 0.773 [2,] 0.742 NA 0.985 0.985 0.833 [3,] 0.833 0.985 NA 0.985 0.712 [4,] 0.833 0.985 0.773 NA 0.742 [5,] 0.833 0.924 0.985 0.742 NA > cc <- pairsColList(pmat) # compute corresponding colors > ## which pairs violate H0? > which(pmat < 0.05, arr.ind=TRUE) # [none] row col > ## pairwise Rosenblatt plot > title <- list("Pairwise Rosenblatt transformed pseudo-observations", + expression(bold("to test")~~italic(H[0]^s:C~~bold("is t")[4]))) > ## --- JCGS, Fig.5 (left) --- > pairsRosenblatt(cu.u, pvalueMat=pmat, pch=".", main=title, + line.main=c(4, 1.4), main.centered=TRUE) > ## --- JCGS, Fig.5 (right) --- > pairsRosenblatt(cu.u, pvalueMat=pmat, method = "QQchisq", pch=".", main=title, + line.main=c(4, 1.4), main.centered=TRUE) > ### Example 4: SMI constituents ################################################ > > data(SMI.12) > n <- nrow(SMI.12) > d <- ncol(SMI.12) > x <- diff(log(SMI.12)) # build log-returns > u <- pobs(x) # build pseudo-observations > ## --- JCGS, Fig.6 --- > pairs(u, gap=0, pch=".", xaxt="n", yaxt="n", main="Pseudo-observations of the log-returns of the SMI", + labels=as.expression( sapply(1:d, function(j) bquote(italic(hat(U)[.(j)]))) )) > tau <- cor(u, method="kendall") # estimate pairwise tau > P <- iTau(normalCopula(), tau) # compute corresponding matrix of pairwise correlations (equal to family="t") > ### Estimate (a) t-copula(s) with the approach of Demarta, McNeil (2005) > > ## compute a positive-definite estimator of P > P. <- nearPD(P, corr=TRUE)$mat > image(P.) # nice (because 'P.' is a Matrix-pkg Matrix) > mP <- as.matrix(P) > p <- P2p(mP) > ##' @title -log-likelihood for t copulas > ##' @param nu d.o.f. parameter > ##' @param P standardized dispersion matrix > ##' @param u data matrix (in [0,1]^d) > ##' @return -log-likelihood for a t copula > ##' @author Marius Hofert > nLLt <- function(nu, P, u){ + stopifnot(require(mvtnorm)) + stopifnot((d <- ncol(u))==ncol(P), ncol(P)==nrow(P)) + qtu <- qt(u, df=nu) + ldtnu <- function(u, P, nu) dmvt(qtu, sigma=P, df=nu, log=TRUE) - + rowSums(dt(qtu, df=nu, log=TRUE)) # t copula log-density + -sum(ldtnu(u, P=P, nu=nu)) + } > ## Note: nLLt() is ~ 30% faster than these {where p := P2p(P) } : > t.20 <- tCopula(dim=d, dispstr="un") > nLLt2 <- function(nu, P, u) -loglikCopula(c(P, df=nu), u=u, copula=t.20) > nLLt3 <- function(nu, P, u) -sum(dCopula(u, setTheta(t.20, c(P, nu)), log=TRUE)) > ## confirm the "equivalence" of nLLt(), nLLt2() and nLLt3() > nu. <- if(doX) seq(.5, 128, by=.5) else 1:15 > system.time(nL1 <- vapply(nu., nLLt , .0, P=mP, u=u)) Loading required package: mvtnorm user system elapsed 0.09 0.00 0.10 > system.time(nL2 <- vapply(nu., nLLt2, .0, P= p, u=u)) user system elapsed 0.09 0.00 0.10 > system.time(nL3 <- vapply(nu., nLLt3, .0, P= p, u=u)) user system elapsed 0.08 0.02 0.10 > stopifnot(all.equal(nL1, nL2, tolerance = 1e-14)) > stopifnot(all.equal(nL2, nL3, tolerance = 1e-14)) > ## estimate nu via MLE for given P > nus <- if(doX) seq(.5, 128, by=.5) else 2^seq(-1,7, by=.5) > mP <- as.matrix(P.) > nLLt.nu <- sapply(nus, nLLt, P=mP, u=u) > plot(nus, nLLt.nu, type="l", xlab=bquote(nu), + ylab=expression(-logL(nu))) > plot(nus, nLLt.nu + 1200, type="l", xlab=bquote(nu), + ylab=expression(1200-logL(nu)), log = "xy") > ## now we got the picture, find the minimum: > (nuOpt <- optimize(nLLt, interval=c(.5, 128), P=mP, u=u, tol = 1e-7)$minimum) [1] 11.96027 > ## define the H0 copula > ## Note: that's the same result when using pseudo-observations since estimation via > ## tau is invariant under strictly increasing transformations > P.. <- P2p(P.) > cop.N <- ellipCopula("normal", param=P.., dim=ncol(P.), dispstr="un") > ## The estimated H0 copula: > cop.t <- ellipCopula("t", df=nuOpt, param=P.., dim=ncol(P.), dispstr="un") > ## create array of pairwise copH0-transformed data columns > cu.uN <- pairwiseCcop(u, cop.N) > cu.ut <- pairwiseCcop(u, cop.t) > if(setSeeds) set.seed(8) > ## compute pairwise matrix (d x d) of p-values and corresponding colors > pwITN <- pairwiseIndepTest(cu.uN, N=N, verbose=interactive()) > pwITt <- pairwiseIndepTest(cu.ut, N=N, verbose=interactive()) > pmatN <- pviTest(pwITN) # pick out p-values > pmatt <- pviTest(pwITt) > ## which pairs violate H0? > which(pmatN < 0.05, arr.ind=TRUE) # => none (so the margins cause non-normality (see below)) row col > which(pmatt < 0.05, arr.ind=TRUE) # => none (fine) row col > ## testing *multivariate normality* > if(require("mvnormtest")) withAutoprint({ + mshapiro.test(t(x)) ## => *not* a multivariate normal distribution + mshapiro.test(t(qnorm(u))) + ## => also not a Gauss copula after removing marginal non-Gaussianity + }) Loading required package: mvnormtest > mshapiro.test(t(x)) ## => *not* a multivariate normal distribution Shapiro-Wilk normality test data: Z W = 0.62837, p-value < 2.2e-16 > mshapiro.test(t(qnorm(u))) Shapiro-Wilk normality test data: Z W = 0.77336, p-value = 1.961e-13 > ## Well, look at the 1D margins : > print(Pm <- apply(x, 2, function(u) shapiro.test(u)$p.value)) ABBN ATLN ADEN CSGN GIVN HOLN 1.511746e-01 1.907419e-06 6.300039e-02 1.824002e-01 4.642566e-02 5.360728e-01 BAER NESN NOVN CFR ROG SGSN 9.678117e-01 2.355932e-01 4.559167e-06 1.344229e-04 4.113431e-03 1.183519e-01 UHR SREN SCMN SYNN SYST RIGN 4.823192e-05 5.348414e-01 2.095678e-01 1.890206e-02 1.544034e-04 1.029705e-05 UBSN ZURN 2.366650e-03 8.638469e-03 > ## not at all uniform: > qqplot(Pm, ppoints(length(Pm)), + main = "QQ plot of p-values of Shapiro( X[,j] ), j=1..20") > abline(0,1, lty=2, col="gray") > ## test for Gauss copula > title <- list("Pairwise Rosenblatt transformed pseudo-observations", + expression(bold("to test")~~italic(H[0]^c:C~~bold("is Gauss")))) > pairsRosenblatt(cu.uN, pvalueMat=pmatN, method="none", cex.labels=0.7, + key.space=1.5, main.centered=TRUE, main=title, line.main=c(3, 0.4)) > ## pairwise Rosenblatt plot (test for t_nuOpt) --- JCGS, Fig.7 -- > nuOpt. <- round(nuOpt, 2) > title <- list("Pairwise Rosenblatt transformed pseudo-observations", + bquote(bold("to test")~~italic(H[0]^c:C)~~"is"~~italic(t))) > if(doPDF) pdf(file=(file <- "gof_graph_fig-SMI-ex.pdf")) > pairsRosenblatt(cu.ut, pvalueMat=pmatt, method="none", cex.labels=0.7, + key.space=1.5, main.centered=TRUE, main=title, line.main=c(3, 0.4), + keyOpt=list(space=1.5, width=1.5, axis=TRUE, title=NULL, line=5)) > if(doPDF) dev.off() pdf 2 Warning messages: 1: In indepTestSim(n, p = 2, m = 2, N = N, verbose = idT.verbose, ...) : N should be at least 100 2: In indepTestSim(n, p = 2, m = 2, N = N, verbose = idT.verbose, ...) : N should be at least 100 3: In indepTestSim(n, p = 2, m = 2, N = N, verbose = idT.verbose, ...) : N should be at least 100 4: In indepTestSim(n, p = 2, m = 2, N = N, verbose = idT.verbose, ...) : N should be at least 100 5: In indepTestSim(n, p = 2, m = 2, N = N, verbose = idT.verbose, ...) : N should be at least 100 > > proc.time() user system elapsed 6.09 0.31 6.39