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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("GeneTS-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('GeneTS') Loading required package: locfdr > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "avpg" > > ### * avpg > > flush(stderr()); flush(stdout()) > > ### Name: avpg > ### Title: Average Periodogram for Multiple (Genetic) Time Series > ### Aliases: avgp > ### Keywords: ts > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > > # how many samples and how many genes? > dim(caulobacter) [1] 11 1444 > > # average periodogram > avgp.caulobacter <- avgp(caulobacter, "Caulobacter") > avgp.caulobacter $freq [1] 0.0909091 0.1818182 0.2727273 0.3636364 0.4545455 $avg.spec [1] 1.0194294 0.4531051 0.3946281 0.3072837 0.5645783 $title [1] "Caulobacter" > > # just compute and don't plot > avgp(caulobacter, "Caulobacter", plot=FALSE) $freq [1] 0.0909091 0.1818182 0.2727273 0.3636364 0.4545455 $avg.spec [1] 1.0194294 0.4531051 0.3946281 0.3072837 0.5645783 $title [1] "Caulobacter" > > > > cleanEx(); ..nameEx <- "bagging" > > ### * bagging > > flush(stderr()); flush(stdout()) > > ### Name: bagging > ### Title: Bagged Versions of Covariance and (Partial) Correlation Matrix > ### Aliases: bagged.cov bagged.cor bagged.pcor > ### Keywords: multivariate > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # small example data set > data(caulobacter) > dat <- caulobacter[,1:15] > dim(dat) [1] 11 15 > > # bagged estimates > b.cov <- bagged.cov(dat) > b.cor <- bagged.cor(dat) > b.pcor <- bagged.pcor(dat) > > # total squared difference > sum( (b.cov - cov(dat))^2 ) [1] 0.0554727 > sum( (b.cor - cor(dat))^2 ) [1] 0.3179933 > sum( (b.pcor - partial.cor(dat))^2 ) [1] 50.99283 > > # positive definiteness of bagged correlation > is.positive.definite(cor(dat)) [1] FALSE > is.positive.definite(b.cor) [1] TRUE > > > > cleanEx(); ..nameEx <- "caulobacter" > > ### * caulobacter > > flush(stderr()); flush(stdout()) > > ### Name: caulobacter > ### Title: Microarray Time Series Data for 1444 Caulobacter Crescentus > ### Genes > ### Aliases: caulobacter > ### Keywords: datasets > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > is.longitudinal(caulobacter) [1] TRUE > > # how many samples and how many genes? > dim(caulobacter) [1] 11 1444 > summary(caulobacter) Longitudinal data: 1444 variables measured at 11 different time points Total number of measurements per variable: 11 Repeated measurements: none To obtain the measurement design call 'get.time.repeats()'. > get.time.repeats(caulobacter) $time [1] 0 15 30 45 60 75 90 105 120 135 150 $repeats [1] 1 1 1 1 1 1 1 1 1 1 1 > > # plot first nine time series > plot(caulobacter, 1:9) > > > > cleanEx(); ..nameEx <- "cor.fit.mixture" > > ### * cor.fit.mixture > > flush(stderr()); flush(stdout()) > > ### Name: cor.fit.mixture > ### Title: Graphical Gaussian Models: Fit Mixture Distribution to Sample > ### Correlation Coefficients > ### Aliases: cor.fit.mixture > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # simulate mixture distribution > r <- rcor0(700, kappa=10) > u <- runif(200, min=-1, max=1) > rc <- c(r,u) > > # estimate kappa and eta0 (=7/9) > c1 <- cor.fit.mixture(r, fA.type="uniform") > c1$eta0 [1] 1 > c1$kappa [1] 9.919193 > c2 <- cor.fit.mixture(rc, fA.type="uniform") > c2$eta0 [1] 0.8137924 > c2$kappa [1] 9.062248 > > # for comparison > cor0.estimate.kappa(r) [1] 9.985368 > cor0.estimate.kappa(rc) [1] 5.924479 > > > > cleanEx(); ..nameEx <- "cor0.estimate.kappa" > > ### * cor0.estimate.kappa > > flush(stderr()); flush(stdout()) > > ### Name: cor0.estimate.kappa > ### Title: Estimating the Degree of Freedom of the Null Distribution of the > ### Correlation Coefficient > ### Aliases: cor0.estimate.kappa > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # distribution of r for kappa=7 > x <- seq(-1,1,0.01) > y <- dcor0(x, kappa=7) > > # simulated data > r <- rcor0(1000, kappa=7) > hist(r, freq=FALSE, + xlim=c(-1,1), ylim=c(0,5)) > lines(x,y,type="l") > > # estimate kappa > cor0.estimate.kappa(r) [1] 7.390819 > > > > cleanEx(); ..nameEx <- "cor0.test" > > ### * cor0.test > > flush(stderr()); flush(stdout()) > > ### Name: cor0.test > ### Title: Test of Vanishing (Partial) Correlation > ### Aliases: cor0.test > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # covariance matrix > m.cov <- rbind( + c(3,1,1,0), + c(1,3,0,1), + c(1,0,2,0), + c(0,1,0,2) + ) > > # compute partial correlations > m.pcor <- cor2pcor(m.cov) > m.pcor [,1] [,2] [,3] [,4] [1,] 1.0000000 0.4000000 0.43852901 -0.17541160 [2,] 0.4000000 1.0000000 -0.17541160 0.43852901 [3,] 0.4385290 -0.1754116 1.00000000 0.07692308 [4,] -0.1754116 0.4385290 0.07692308 1.00000000 > > # corresponding p-values > # assuming a sample size of 25, i.e. kappa=22 > kappa2n(22, 4) [1] 25 > cor0.test(m.pcor, kappa=22) [,1] [,2] [,3] [,4] [1,] 0.00000000 0.05860001 0.03633173 0.42338215 [2,] 0.05860001 0.00000000 0.42338215 0.03633173 [3,] 0.03633173 0.42338215 0.00000000 0.72719831 [4,] 0.42338215 0.03633173 0.72719831 0.00000000 > cor0.test(m.pcor, kappa=22) < 0.05 [,1] [,2] [,3] [,4] [1,] TRUE FALSE TRUE FALSE [2,] FALSE TRUE FALSE TRUE [3,] TRUE FALSE TRUE FALSE [4,] FALSE TRUE FALSE TRUE > > # p-values become smaller with larger r > cor0.test(0.7, 12) [1] 0.007722282 > cor0.test(0.8, 12) [1] 0.001024829 > cor0.test(0.9, 12) [1] 2.771608e-05 > > # comparison of various methods > cor0.test(0.2, 45, method="student") [1] 0.1826482 > cor0.test(0.2, 45, method="dcor0") [1] 0.1826482 > cor0.test(0.2, 45, method="ztransform") [1] 0.1837140 > > > > cleanEx(); ..nameEx <- "cov.shrink" > > ### * cov.shrink > > flush(stderr()); flush(stdout()) > > ### Name: cov.shrink > ### Title: Shrinkage Estimation of Covariance and Correlation Matrix > ### Aliases: cov.shrink cor.shrink varcov > ### Keywords: multivariate > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # small n, large p > p <- 100 > n <- 20 > > # generate random pxp covariance matrix > sigma <- matrix(rnorm(p*p),ncol=p) > sigma <- crossprod(sigma)+ diag(rep(0.1, p)) > > # simulate multinormal data of sample size n > sigsvd <- svd(sigma) > Y <- t(sigsvd$v %*% (t(sigsvd$u) * sqrt(sigsvd$d))) > X <- matrix(rnorm(n * ncol(sigma)), nrow = n) %*% Y > > # estimate covariance matrix > s1 <- cov(X) > s2 <- cov.shrink(X) Computing empirical covariance matrix and its variance Wait for 100 points (50 per row): .................................................. 50 .................................................. 100 Computing shrinkage covariance matrix Estimated shrinkage intensity lambda: 0.8095 > > # MSE > sum((s1-sigma)^2) [1] 5103048 > sum((s2-sigma)^2) [1] 998270.5 > > # varcov produces the same results as cov > vc <- varcov(X) Computing empirical covariance matrix and its variance Wait for 100 points (50 per row): .................................................. 50 .................................................. 100 > sum(abs(vc$S-s1)) [1] 2.606497e-11 > > # compare positive definitess > is.positive.definite(s1) [1] FALSE > is.positive.definite(s2) [1] TRUE > is.positive.definite(sigma) [1] TRUE > > # compare ranks and condition > rank.condition(s1) $rank [1] 19 $condition [1] Inf $tol [1] 2.724117e-11 > rank.condition(s2) $rank [1] 100 $condition [1] 12.34035 $tol [1] 7.504132e-12 > rank.condition(sigma) $rank [1] 100 $condition [1] 3468.244 $tol [1] 8.97783e-12 > > # compare eigenvalues > e1 <- eigen(s1, symmetric=TRUE)$values > e2 <- eigen(s2, symmetric=TRUE)$values > e3 <- eigen(sigma, symmetric=TRUE)$values > m <-max(e1, e2, e3) > yl <- c(0, m) > > par(mfrow=c(1,3)) > plot(e1, main="empirical") > plot(e2, ylim=yl, main="shrinkage") > plot(e3, ylim=yl, main="true") > par(mfrow=c(1,1)) > > > > > graphics::par(get("par.postscript", env = .CheckExEnv)) > cleanEx(); ..nameEx <- "dcor0" > > ### * dcor0 > > flush(stderr()); flush(stdout()) > > ### Name: dcor0 > ### Title: Distribution of the Vanishing Correlation Coefficient (rho=0) > ### and Related Functions > ### Aliases: dcor0 pcor0 rcor0 ibeta > ### Keywords: distribution > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # distribution of r for various degrees of freedom > x <- seq(-1,1,0.01) > y1 <- dcor0(x, kappa=7) > y2 <- dcor0(x, kappa=15) > plot(x,y2,type="l", xlab="r", ylab="pdf", + xlim=c(-1,1), ylim=c(0,2)) > lines(x,y1) > > # simulated data > r <- rcor0(1000, kappa=7) > hist(r, freq=FALSE, + xlim=c(-1,1), ylim=c(0,5)) > lines(x,y1,type="l") > > # distribution function > pcor0(-0.2, kappa=15) [1] 0.2288440 > > # incomplete beta function > ibeta(0.4, 1, 3) [1] 0.2613333 > ibeta(1, 2, 3) [1] 0.08333333 > beta(2, 3) [1] 0.08333333 > > > > cleanEx(); ..nameEx <- "density.pr" > > ### * density.pr > > flush(stderr()); flush(stdout()) > > ### Name: density.pr > ### Title: Density Estimation Via Poisson Regression > ### Aliases: density.pr > ### Keywords: univar > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data > data(faithful) > z <- faithful[,1] > rz <- range(z) > > # estimate density > d1a <- density.pr(z, plot=TRUE) Loading required package: splines > d1a Call: density.pr(x = z, plot = TRUE) Data: z (272 obs.); Bandwidth 'bw' = 0.03473 x y Min. :1.636 Min. :0.02751 1st Qu.:2.495 1st Qu.:0.07828 Median :3.355 Median :0.26336 Mean :3.355 Mean :0.28794 3rd Qu.:4.214 3rd Qu.:0.47948 Max. :5.074 Max. :0.66346 > > # discretization is not critical in this algorithm > d1b <- density.pr(z, plot=TRUE, ncells=80) > d1c <- density.pr(z, plot=TRUE, ncells=40) > > plot(d1a, col=2) > lines(d1b, col=2) > lines(d1c, col=2) > > # comparison with kernel density estimate > d2a <- density(z, from=rz[1], to=rz[2]) > d2b <- density(z, from=rz[1], to=rz[2], bw="bcv") > > plot(d1a, col=2, xlim=rz) > lines(d2a) > lines(d2b) > > > > cleanEx(); ..nameEx <- "dominant.freqs" > > ### * dominant.freqs > > flush(stderr()); flush(stdout()) > > ### Name: dominant.freqs > ### Title: Dominant Frequencies in Multiple (Genetic) Time Series > ### Aliases: dominant.freqs > ### Keywords: ts > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > > # how many samples and how many genes? > dim(caulobacter) [1] 11 1444 > > # first three dominant frequencies for each gene > dominant.freqs(caulobacter, 3) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 0.3636364 [2,] 0.2727273 0.1818182 0.1818182 0.1818182 0.1818182 0.1818182 0.2727273 [3,] 0.3636364 0.4545455 0.4545455 0.3636364 0.2727273 0.2727273 0.1818182 [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.2727273 0.0909091 0.3636364 [2,] 0.1818182 0.4545455 0.1818182 0.3636364 0.4545455 0.3636364 0.4545455 [3,] 0.2727273 0.2727273 0.3636364 0.1818182 0.0909091 0.1818182 0.1818182 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [1,] 0.1818182 0.0909091 0.0909091 0.0909091 0.0909091 0.2727273 0.3636364 [2,] 0.0909091 0.3636364 0.1818182 0.4545455 0.1818182 0.1818182 0.1818182 [3,] 0.2727273 0.1818182 0.3636364 0.2727273 0.2727273 0.0909091 0.0909091 [,22] [,23] [,24] [,25] [,26] [,27] [,28] [1,] 0.3636364 0.0909091 0.4545455 0.1818182 0.4545455 0.0909091 0.2727273 [2,] 0.1818182 0.1818182 0.0909091 0.0909091 0.1818182 0.2727273 0.0909091 [3,] 0.4545455 0.2727273 0.2727273 0.2727273 0.0909091 0.1818182 0.4545455 [,29] [,30] [,31] [,32] [,33] [,34] [,35] [1,] 0.1818182 0.0909091 0.0909091 0.2727273 0.0909091 0.4545455 0.0909091 [2,] 0.0909091 0.1818182 0.2727273 0.1818182 0.4545455 0.0909091 0.2727273 [3,] 0.2727273 0.2727273 0.1818182 0.4545455 0.3636364 0.2727273 0.4545455 [,36] [,37] [,38] [,39] [,40] [,41] [,42] [1,] 0.0909091 0.1818182 0.0909091 0.0909091 0.2727273 0.3636364 0.2727273 [2,] 0.1818182 0.0909091 0.1818182 0.1818182 0.1818182 0.2727273 0.1818182 [3,] 0.4545455 0.2727273 0.2727273 0.2727273 0.3636364 0.0909091 0.0909091 [,43] [,44] [,45] [,46] [,47] [,48] [,49] [1,] 0.0909091 0.0909091 0.2727273 0.2727273 0.1818182 0.2727273 0.2727273 [2,] 0.4545455 0.1818182 0.0909091 0.1818182 0.2727273 0.1818182 0.1818182 [3,] 0.2727273 0.2727273 0.4545455 0.4545455 0.0909091 0.0909091 0.0909091 [,50] [,51] [,52] [,53] [,54] [,55] [,56] [1,] 0.0909091 0.1818182 0.0909091 0.3636364 0.2727273 0.3636364 0.4545455 [2,] 0.1818182 0.2727273 0.1818182 0.2727273 0.1818182 0.2727273 0.3636364 [3,] 0.2727273 0.3636364 0.4545455 0.4545455 0.4545455 0.1818182 0.0909091 [,57] [,58] [,59] [,60] [,61] [,62] [,63] [1,] 0.2727273 0.0909091 0.0909091 0.0909091 0.1818182 0.2727273 0.0909091 [2,] 0.0909091 0.1818182 0.4545455 0.1818182 0.0909091 0.0909091 0.3636364 [3,] 0.1818182 0.4545455 0.1818182 0.2727273 0.2727273 0.1818182 0.4545455 [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.2727273 0.1818182 0.2727273 [2,] 0.1818182 0.1818182 0.4545455 0.1818182 0.4545455 0.2727273 0.3636364 [3,] 0.2727273 0.4545455 0.1818182 0.4545455 0.1818182 0.3636364 0.0909091 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [1,] 0.1818182 0.1818182 0.1818182 0.1818182 0.1818182 0.0909091 0.0909091 [2,] 0.2727273 0.4545455 0.4545455 0.0909091 0.4545455 0.4545455 0.1818182 [3,] 0.4545455 0.0909091 0.0909091 0.4545455 0.2727273 0.1818182 0.4545455 [,78] [,79] [,80] [,81] [,82] [,83] [,84] [1,] 0.0909091 0.0909091 0.0909091 0.1818182 0.4545455 0.4545455 0.0909091 [2,] 0.3636364 0.1818182 0.1818182 0.4545455 0.1818182 0.1818182 0.3636364 [3,] 0.4545455 0.2727273 0.2727273 0.0909091 0.2727273 0.2727273 0.4545455 [,85] [,86] [,87] [,88] [,89] [,90] [,91] [1,] 0.3636364 0.4545455 0.2727273 0.4545455 0.4545455 0.4545455 0.3636364 [2,] 0.4545455 0.0909091 0.1818182 0.3636364 0.2727273 0.3636364 0.1818182 [3,] 0.0909091 0.2727273 0.0909091 0.1818182 0.3636364 0.1818182 0.4545455 [,92] [,93] [,94] [,95] [,96] [,97] [,98] [1,] 0.4545455 0.0909091 0.0909091 0.2727273 0.0909091 0.0909091 0.2727273 [2,] 0.0909091 0.4545455 0.4545455 0.0909091 0.3636364 0.4545455 0.0909091 [3,] 0.1818182 0.2727273 0.3636364 0.1818182 0.2727273 0.3636364 0.4545455 [,99] [,100] [,101] [,102] [,103] [,104] [,105] [1,] 0.1818182 0.1818182 0.0909091 0.0909091 0.4545455 0.4545455 0.2727273 [2,] 0.0909091 0.0909091 0.2727273 0.1818182 0.2727273 0.1818182 0.0909091 [3,] 0.3636364 0.3636364 0.1818182 0.2727273 0.1818182 0.0909091 0.3636364 [,106] [,107] [,108] [,109] [,110] [,111] [,112] [1,] 0.0909091 0.4545455 0.4545455 0.0909091 0.4545455 0.0909091 0.0909091 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[,1101] [,1102] [,1103] [,1104] [,1105] [,1106] [1,] 0.3636364 0.3636364 0.0909091 0.3636364 0.0909091 0.2727273 0.0909091 [2,] 0.4545455 0.2727273 0.4545455 0.4545455 0.1818182 0.0909091 0.1818182 [3,] 0.0909091 0.4545455 0.2727273 0.2727273 0.4545455 0.3636364 0.3636364 [,1107] [,1108] [,1109] [,1110] [,1111] [,1112] [,1113] [1,] 0.0909091 0.0909091 0.2727273 0.4545455 0.0909091 0.0909091 0.0909091 [2,] 0.1818182 0.2727273 0.4545455 0.2727273 0.1818182 0.4545455 0.2727273 [3,] 0.4545455 0.4545455 0.0909091 0.3636364 0.4545455 0.3636364 0.1818182 [,1114] [,1115] [,1116] [,1117] [,1118] [,1119] [,1120] [1,] 0.2727273 0.0909091 0.0909091 0.3636364 0.2727273 0.1818182 0.1818182 [2,] 0.4545455 0.3636364 0.1818182 0.2727273 0.0909091 0.0909091 0.2727273 [3,] 0.1818182 0.1818182 0.2727273 0.1818182 0.3636364 0.4545455 0.4545455 [,1121] [,1122] [,1123] [,1124] [,1125] [,1126] [,1127] [1,] 0.0909091 0.0909091 0.2727273 0.1818182 0.3636364 0.0909091 0.0909091 [2,] 0.4545455 0.1818182 0.1818182 0.0909091 0.0909091 0.4545455 0.1818182 [3,] 0.2727273 0.3636364 0.0909091 0.3636364 0.4545455 0.1818182 0.2727273 [,1128] [,1129] [,1130] [,1131] [,1132] [,1133] [,1134] [1,] 0.1818182 0.0909091 0.1818182 0.0909091 0.2727273 0.4545455 0.0909091 [2,] 0.0909091 0.1818182 0.2727273 0.4545455 0.0909091 0.2727273 0.3636364 [3,] 0.4545455 0.4545455 0.3636364 0.1818182 0.3636364 0.1818182 0.4545455 [,1135] [,1136] [,1137] [,1138] [,1139] [,1140] [,1141] [1,] 0.3636364 0.1818182 0.4545455 0.0909091 0.0909091 0.0909091 0.0909091 [2,] 0.4545455 0.0909091 0.2727273 0.3636364 0.1818182 0.1818182 0.1818182 [3,] 0.2727273 0.2727273 0.1818182 0.4545455 0.4545455 0.4545455 0.4545455 [,1142] [,1143] [,1144] [,1145] [,1146] [,1147] [,1148] [1,] 0.2727273 0.0909091 0.2727273 0.0909091 0.0909091 0.1818182 0.0909091 [2,] 0.3636364 0.1818182 0.0909091 0.1818182 0.2727273 0.0909091 0.1818182 [3,] 0.0909091 0.2727273 0.3636364 0.4545455 0.1818182 0.3636364 0.2727273 [,1149] [,1150] [,1151] [,1152] [,1153] [,1154] [,1155] [1,] 0.0909091 0.2727273 0.4545455 0.2727273 0.0909091 0.0909091 0.0909091 [2,] 0.1818182 0.0909091 0.3636364 0.0909091 0.1818182 0.1818182 0.4545455 [3,] 0.3636364 0.4545455 0.2727273 0.4545455 0.3636364 0.3636364 0.1818182 [,1156] [,1157] [,1158] [,1159] [,1160] [,1161] [,1162] [1,] 0.0909091 0.2727273 0.2727273 0.2727273 0.2727273 0.0909091 0.1818182 [2,] 0.3636364 0.1818182 0.0909091 0.1818182 0.1818182 0.2727273 0.2727273 [3,] 0.4545455 0.3636364 0.1818182 0.0909091 0.4545455 0.1818182 0.0909091 [,1163] [,1164] [,1165] [,1166] [,1167] [,1168] [,1169] [1,] 0.1818182 0.0909091 0.2727273 0.4545455 0.0909091 0.0909091 0.0909091 [2,] 0.0909091 0.3636364 0.3636364 0.3636364 0.1818182 0.1818182 0.3636364 [3,] 0.4545455 0.1818182 0.4545455 0.2727273 0.4545455 0.4545455 0.2727273 [,1170] [,1171] [,1172] [,1173] [,1174] [,1175] [,1176] [1,] 0.0909091 0.1818182 0.0909091 0.1818182 0.2727273 0.1818182 0.0909091 [2,] 0.1818182 0.0909091 0.1818182 0.3636364 0.0909091 0.2727273 0.4545455 [3,] 0.4545455 0.3636364 0.3636364 0.0909091 0.4545455 0.4545455 0.2727273 [,1177] [,1178] [,1179] [,1180] [,1181] [,1182] [,1183] [1,] 0.0909091 0.4545455 0.0909091 0.2727273 0.2727273 0.0909091 0.3636364 [2,] 0.1818182 0.3636364 0.2727273 0.1818182 0.0909091 0.1818182 0.4545455 [3,] 0.2727273 0.0909091 0.3636364 0.0909091 0.1818182 0.2727273 0.2727273 [,1184] [,1185] [,1186] [,1187] [,1188] [,1189] [,1190] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.3636364 0.0909091 0.2727273 [2,] 0.1818182 0.4545455 0.1818182 0.4545455 0.0909091 0.3636364 0.4545455 [3,] 0.2727273 0.1818182 0.3636364 0.2727273 0.1818182 0.1818182 0.3636364 [,1191] [,1192] [,1193] [,1194] [,1195] [,1196] [,1197] [1,] 0.1818182 0.0909091 0.2727273 0.0909091 0.0909091 0.0909091 0.0909091 [2,] 0.3636364 0.2727273 0.3636364 0.2727273 0.2727273 0.1818182 0.3636364 [3,] 0.0909091 0.1818182 0.4545455 0.4545455 0.4545455 0.2727273 0.2727273 [,1198] [,1199] [,1200] [,1201] [,1202] [,1203] [,1204] [1,] 0.1818182 0.2727273 0.0909091 0.0909091 0.0909091 0.4545455 0.0909091 [2,] 0.3636364 0.3636364 0.3636364 0.2727273 0.2727273 0.0909091 0.4545455 [3,] 0.2727273 0.0909091 0.2727273 0.1818182 0.1818182 0.2727273 0.2727273 [,1205] [,1206] [,1207] [,1208] [,1209] [,1210] [,1211] [1,] 0.2727273 0.2727273 0.1818182 0.2727273 0.0909091 0.2727273 0.0909091 [2,] 0.0909091 0.4545455 0.2727273 0.4545455 0.3636364 0.4545455 0.2727273 [3,] 0.1818182 0.3636364 0.3636364 0.1818182 0.4545455 0.1818182 0.1818182 [,1212] [,1213] [,1214] [,1215] [,1216] [,1217] [,1218] [1,] 0.1818182 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 0.1818182 [2,] 0.4545455 0.1818182 0.1818182 0.4545455 0.2727273 0.2727273 0.2727273 [3,] 0.3636364 0.4545455 0.4545455 0.1818182 0.1818182 0.1818182 0.3636364 [,1219] [,1220] [,1221] [,1222] [,1223] [,1224] [,1225] [1,] 0.3636364 0.1818182 0.2727273 0.0909091 0.4545455 0.0909091 0.0909091 [2,] 0.0909091 0.2727273 0.0909091 0.1818182 0.3636364 0.3636364 0.4545455 [3,] 0.1818182 0.0909091 0.3636364 0.4545455 0.0909091 0.1818182 0.1818182 [,1226] [,1227] [,1228] [,1229] [,1230] [,1231] [,1232] [1,] 0.0909091 0.3636364 0.2727273 0.1818182 0.4545455 0.0909091 0.1818182 [2,] 0.2727273 0.0909091 0.4545455 0.0909091 0.2727273 0.1818182 0.0909091 [3,] 0.3636364 0.1818182 0.1818182 0.2727273 0.0909091 0.3636364 0.2727273 [,1233] [,1234] [,1235] [,1236] [,1237] [,1238] [,1239] [1,] 0.0909091 0.0909091 0.2727273 0.0909091 0.1818182 0.0909091 0.0909091 [2,] 0.1818182 0.1818182 0.0909091 0.1818182 0.0909091 0.1818182 0.1818182 [3,] 0.2727273 0.3636364 0.4545455 0.2727273 0.2727273 0.2727273 0.3636364 [,1240] [,1241] [,1242] [,1243] [,1244] [,1245] [,1246] [1,] 0.0909091 0.0909091 0.4545455 0.0909091 0.0909091 0.1818182 0.1818182 [2,] 0.1818182 0.4545455 0.1818182 0.4545455 0.2727273 0.2727273 0.0909091 [3,] 0.2727273 0.2727273 0.2727273 0.1818182 0.1818182 0.3636364 0.2727273 [,1247] [,1248] [,1249] [,1250] [,1251] [,1252] [,1253] [1,] 0.0909091 0.4545455 0.0909091 0.0909091 0.0909091 0.4545455 0.4545455 [2,] 0.2727273 0.3636364 0.4545455 0.1818182 0.2727273 0.2727273 0.0909091 [3,] 0.3636364 0.1818182 0.2727273 0.4545455 0.1818182 0.0909091 0.2727273 [,1254] [,1255] [,1256] [,1257] [,1258] [,1259] [,1260] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.4545455 0.0909091 0.0909091 [2,] 0.3636364 0.1818182 0.1818182 0.1818182 0.3636364 0.3636364 0.1818182 [3,] 0.2727273 0.2727273 0.3636364 0.2727273 0.2727273 0.4545455 0.4545455 [,1261] [,1262] [,1263] [,1264] [,1265] [,1266] [,1267] [1,] 0.0909091 0.0909091 0.1818182 0.0909091 0.2727273 0.0909091 0.0909091 [2,] 0.3636364 0.1818182 0.2727273 0.1818182 0.1818182 0.1818182 0.4545455 [3,] 0.2727273 0.3636364 0.4545455 0.2727273 0.3636364 0.2727273 0.1818182 [,1268] [,1269] [,1270] [,1271] [,1272] [,1273] [,1274] [1,] 0.0909091 0.0909091 0.1818182 0.1818182 0.0909091 0.0909091 0.0909091 [2,] 0.4545455 0.1818182 0.0909091 0.0909091 0.3636364 0.1818182 0.1818182 [3,] 0.2727273 0.2727273 0.3636364 0.2727273 0.2727273 0.3636364 0.3636364 [,1275] [,1276] [,1277] [,1278] [,1279] [,1280] [,1281] [1,] 0.1818182 0.1818182 0.0909091 0.1818182 0.0909091 0.1818182 0.1818182 [2,] 0.0909091 0.0909091 0.2727273 0.0909091 0.1818182 0.0909091 0.4545455 [3,] 0.3636364 0.2727273 0.4545455 0.3636364 0.4545455 0.4545455 0.0909091 [,1282] [,1283] [,1284] [,1285] [,1286] [,1287] [,1288] [1,] 0.3636364 0.0909091 0.0909091 0.2727273 0.0909091 0.4545455 0.0909091 [2,] 0.1818182 0.4545455 0.1818182 0.3636364 0.2727273 0.2727273 0.1818182 [3,] 0.4545455 0.1818182 0.2727273 0.4545455 0.1818182 0.0909091 0.4545455 [,1289] [,1290] [,1291] [,1292] [,1293] [,1294] [,1295] [1,] 0.4545455 0.0909091 0.1818182 0.4545455 0.3636364 0.0909091 0.1818182 [2,] 0.1818182 0.4545455 0.2727273 0.0909091 0.4545455 0.1818182 0.0909091 [3,] 0.2727273 0.3636364 0.3636364 0.1818182 0.2727273 0.2727273 0.3636364 [,1296] [,1297] [,1298] [,1299] [,1300] [,1301] [,1302] [1,] 0.2727273 0.0909091 0.1818182 0.1818182 0.1818182 0.0909091 0.1818182 [2,] 0.4545455 0.1818182 0.0909091 0.2727273 0.3636364 0.4545455 0.3636364 [3,] 0.1818182 0.4545455 0.2727273 0.3636364 0.0909091 0.2727273 0.0909091 [,1303] [,1304] [,1305] [,1306] [,1307] [,1308] [,1309] [1,] 0.1818182 0.0909091 0.1818182 0.0909091 0.3636364 0.0909091 0.2727273 [2,] 0.0909091 0.3636364 0.2727273 0.1818182 0.2727273 0.2727273 0.1818182 [3,] 0.4545455 0.2727273 0.4545455 0.2727273 0.1818182 0.3636364 0.3636364 [,1310] [,1311] [,1312] [,1313] [,1314] [,1315] [,1316] [1,] 0.4545455 0.0909091 0.3636364 0.0909091 0.0909091 0.4545455 0.1818182 [2,] 0.1818182 0.1818182 0.0909091 0.2727273 0.2727273 0.0909091 0.2727273 [3,] 0.0909091 0.3636364 0.1818182 0.1818182 0.3636364 0.2727273 0.4545455 [,1317] [,1318] [,1319] [,1320] [,1321] [,1322] [,1323] [1,] 0.0909091 0.1818182 0.0909091 0.0909091 0.4545455 0.0909091 0.4545455 [2,] 0.1818182 0.0909091 0.2727273 0.1818182 0.3636364 0.4545455 0.1818182 [3,] 0.3636364 0.2727273 0.1818182 0.4545455 0.1818182 0.3636364 0.2727273 [,1324] [,1325] [,1326] [,1327] [,1328] [,1329] [,1330] [1,] 0.1818182 0.0909091 0.0909091 0.1818182 0.2727273 0.2727273 0.3636364 [2,] 0.0909091 0.1818182 0.4545455 0.4545455 0.3636364 0.4545455 0.1818182 [3,] 0.4545455 0.2727273 0.2727273 0.2727273 0.1818182 0.1818182 0.0909091 [,1331] [,1332] [,1333] [,1334] [,1335] [,1336] [,1337] [1,] 0.0909091 0.0909091 0.1818182 0.1818182 0.3636364 0.4545455 0.0909091 [2,] 0.1818182 0.1818182 0.0909091 0.3636364 0.1818182 0.0909091 0.1818182 [3,] 0.3636364 0.3636364 0.3636364 0.4545455 0.0909091 0.1818182 0.3636364 [,1338] [,1339] [,1340] [,1341] [,1342] [,1343] [,1344] [1,] 0.2727273 0.4545455 0.0909091 0.1818182 0.2727273 0.0909091 0.2727273 [2,] 0.0909091 0.0909091 0.4545455 0.4545455 0.0909091 0.3636364 0.0909091 [3,] 0.1818182 0.2727273 0.1818182 0.3636364 0.1818182 0.2727273 0.1818182 [,1345] [,1346] [,1347] [,1348] [,1349] [,1350] [,1351] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.1818182 0.3636364 0.1818182 [2,] 0.3636364 0.1818182 0.1818182 0.4545455 0.0909091 0.0909091 0.3636364 [3,] 0.2727273 0.4545455 0.4545455 0.1818182 0.4545455 0.1818182 0.0909091 [,1352] [,1353] [,1354] [,1355] [,1356] [,1357] [,1358] [1,] 0.0909091 0.1818182 0.1818182 0.1818182 0.0909091 0.0909091 0.0909091 [2,] 0.3636364 0.4545455 0.0909091 0.0909091 0.1818182 0.3636364 0.1818182 [3,] 0.2727273 0.3636364 0.3636364 0.4545455 0.2727273 0.2727273 0.4545455 [,1359] [,1360] [,1361] [,1362] [,1363] [,1364] [,1365] [1,] 0.0909091 0.0909091 0.0909091 0.1818182 0.1818182 0.1818182 0.0909091 [2,] 0.1818182 0.2727273 0.1818182 0.0909091 0.4545455 0.0909091 0.1818182 [3,] 0.3636364 0.3636364 0.2727273 0.2727273 0.0909091 0.2727273 0.3636364 [,1366] [,1367] [,1368] [,1369] [,1370] [,1371] [,1372] [1,] 0.0909091 0.2727273 0.0909091 0.2727273 0.4545455 0.0909091 0.0909091 [2,] 0.4545455 0.0909091 0.1818182 0.3636364 0.2727273 0.1818182 0.1818182 [3,] 0.3636364 0.3636364 0.2727273 0.1818182 0.3636364 0.2727273 0.2727273 [,1373] [,1374] [,1375] [,1376] [,1377] [,1378] [,1379] [1,] 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 0.0909091 [2,] 0.1818182 0.1818182 0.4545455 0.1818182 0.1818182 0.2727273 0.1818182 [3,] 0.2727273 0.3636364 0.1818182 0.2727273 0.3636364 0.3636364 0.3636364 [,1380] [,1381] [,1382] [,1383] [,1384] [,1385] [,1386] [1,] 0.1818182 0.0909091 0.4545455 0.4545455 0.0909091 0.0909091 0.3636364 [2,] 0.4545455 0.2727273 0.0909091 0.1818182 0.1818182 0.1818182 0.2727273 [3,] 0.0909091 0.1818182 0.3636364 0.2727273 0.2727273 0.3636364 0.4545455 [,1387] [,1388] [,1389] [,1390] [,1391] [,1392] [,1393] [1,] 0.0909091 0.1818182 0.0909091 0.0909091 0.0909091 0.0909091 0.4545455 [2,] 0.1818182 0.2727273 0.1818182 0.3636364 0.4545455 0.4545455 0.1818182 [3,] 0.2727273 0.0909091 0.3636364 0.4545455 0.3636364 0.1818182 0.0909091 [,1394] [,1395] [,1396] [,1397] [,1398] [,1399] [,1400] [1,] 0.4545455 0.3636364 0.0909091 0.1818182 0.0909091 0.1818182 0.0909091 [2,] 0.0909091 0.4545455 0.4545455 0.2727273 0.1818182 0.2727273 0.1818182 [3,] 0.1818182 0.1818182 0.2727273 0.0909091 0.3636364 0.4545455 0.4545455 [,1401] [,1402] [,1403] [,1404] [,1405] [,1406] [,1407] [1,] 0.0909091 0.0909091 0.3636364 0.1818182 0.3636364 0.0909091 0.4545455 [2,] 0.1818182 0.2727273 0.0909091 0.0909091 0.0909091 0.4545455 0.0909091 [3,] 0.2727273 0.1818182 0.4545455 0.3636364 0.1818182 0.2727273 0.3636364 [,1408] [,1409] [,1410] [,1411] [,1412] [,1413] [,1414] [1,] 0.0909091 0.1818182 0.0909091 0.0909091 0.0909091 0.1818182 0.0909091 [2,] 0.4545455 0.0909091 0.1818182 0.1818182 0.1818182 0.4545455 0.1818182 [3,] 0.1818182 0.3636364 0.2727273 0.2727273 0.2727273 0.0909091 0.2727273 [,1415] [,1416] [,1417] [,1418] [,1419] [,1420] [,1421] [1,] 0.0909091 0.2727273 0.0909091 0.1818182 0.0909091 0.1818182 0.0909091 [2,] 0.2727273 0.0909091 0.1818182 0.0909091 0.4545455 0.0909091 0.1818182 [3,] 0.3636364 0.1818182 0.2727273 0.2727273 0.1818182 0.3636364 0.4545455 [,1422] [,1423] [,1424] [,1425] [,1426] [,1427] [,1428] [1,] 0.0909091 0.1818182 0.2727273 0.1818182 0.1818182 0.3636364 0.4545455 [2,] 0.2727273 0.0909091 0.4545455 0.0909091 0.4545455 0.2727273 0.0909091 [3,] 0.4545455 0.4545455 0.3636364 0.4545455 0.0909091 0.4545455 0.3636364 [,1429] [,1430] [,1431] [,1432] [,1433] [,1434] [,1435] [1,] 0.1818182 0.0909091 0.2727273 0.2727273 0.0909091 0.1818182 0.1818182 [2,] 0.0909091 0.1818182 0.1818182 0.1818182 0.3636364 0.4545455 0.4545455 [3,] 0.4545455 0.2727273 0.0909091 0.0909091 0.2727273 0.2727273 0.3636364 [,1436] [,1437] [,1438] [,1439] [,1440] [,1441] [,1442] [1,] 0.1818182 0.0909091 0.0909091 0.0909091 0.1818182 0.2727273 0.0909091 [2,] 0.2727273 0.1818182 0.1818182 0.1818182 0.4545455 0.3636364 0.2727273 [3,] 0.3636364 0.3636364 0.2727273 0.2727273 0.0909091 0.1818182 0.3636364 [,1443] [,1444] [1,] 0.0909091 0.0909091 [2,] 0.3636364 0.1818182 [3,] 0.1818182 0.3636364 > > # first four dominant frequencies for gene no. 1000 > dominant.freqs(caulobacter[,1000], 4) [,1] [1,] 0.2727273 [2,] 0.0909091 [3,] 0.1818182 [4,] 0.3636364 > > > > cleanEx(); ..nameEx <- "fast.svd" > > ### * fast.svd > > flush(stderr()); flush(stdout()) > > ### Name: fast.svd > ### Title: Efficient Computation of the Singular Value Decomposition for > ### Fat and Thin Matrices > ### Aliases: fast.svd > ### Keywords: algebra > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # generate a "fat" data matrix > n <- 50 > p <- 5000 > X <- matrix(rnorm(n*p), n, p) > > # compute SVD > system.time( s1 <- svd(X) ) [1] 0.65 0.05 0.69 0.00 0.00 > system.time( s2 <- fast.svd(X) ) [1] 0.07 0.02 0.10 0.00 0.00 > > eps <- 1e-10 > sum(abs(s1$d-s2$d) > eps) [1] 0 > sum(abs(abs(s1$u)-abs(s2$u)) > eps) [1] 0 > sum(abs(abs(s1$v)-abs(s2$v)) > eps) [1] 0 > > > > cleanEx(); ..nameEx <- "fdr.control" > > ### * fdr.control > > flush(stderr()); flush(stdout()) > > ### Name: fdr.control > ### Title: Controlling the False Discovery Rate in Multiple Testing > ### Aliases: fdr.control > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > > # how many genes and how many samples? > dim(caulobacter) [1] 11 1444 > > # p-values from Fisher's g test > pval.caulobacter <- fisher.g.test(caulobacter) > > # FDR test on the level 0.05 > fdr.control(pval.caulobacter, Q = 0.05) $qvalues [1] 0.95811412 0.03272761 0.02239812 0.02089051 0.08208556 0.08447214 [7] 0.04145927 0.01874064 0.79396819 0.95926113 0.46151810 0.97623786 [13] 0.99998754 0.95191096 0.75034864 0.99998754 0.66925441 0.05903125 [19] 0.98880162 0.99998754 0.93071158 0.99998754 0.71444177 0.94212371 [25] 0.87598178 0.99186476 0.70624905 0.96368310 0.83804161 0.69238929 [31] 0.60549182 0.89611337 0.83804161 0.99631496 0.75347358 0.19823457 [37] 0.89615754 0.79688311 0.47966509 0.82814966 0.99186476 0.99998754 [43] 0.86512076 0.93655054 0.82019765 0.79688311 0.95191096 0.82814966 [49] 0.73889617 0.79396819 0.98537100 0.83804161 0.99998754 0.44178006 [55] 0.63008628 0.83812768 0.97058499 0.19040190 0.71752892 0.50064393 [61] 0.49355179 0.92734087 0.95143291 0.35906508 0.63230249 0.95758005 [67] 0.95811412 0.83066050 0.88438570 0.85947396 0.94212371 0.76972269 [73] 0.99998754 0.99998754 0.70101815 0.49824821 0.99998754 0.90912611 [79] 0.14134399 0.54489777 0.42897218 0.99998754 0.75347358 0.93487993 [85] 0.95538883 0.96618291 0.79934880 0.98999815 0.95955633 0.99998754 [91] 0.99186476 0.99998754 0.20743697 0.98668738 0.79396819 0.55839584 [97] 0.83812768 0.99186476 0.97851743 0.63008628 0.59611389 0.48823943 [103] 0.73049341 0.94212371 0.79688311 0.14113845 0.99998754 0.99998754 [109] 0.99998754 0.99998754 0.99998754 0.79688311 0.70532560 0.63622788 [115] 0.99998754 0.95320576 0.99186476 0.79688311 0.65225017 0.96618291 [121] 0.71988122 0.77436254 0.98883669 0.96368310 0.86893946 0.99998754 [127] 0.99998754 0.88537970 0.99998754 0.82814966 0.82814966 0.60362371 [133] 0.80323837 0.01874064 0.02089051 0.23065554 0.83804161 0.99998754 [139] 0.83804161 0.88537970 0.76089638 0.98880162 0.16195709 0.91778305 [145] 0.79688311 0.76762070 0.89611337 0.20241259 0.06953117 0.90287798 [151] 0.82171023 0.62659977 0.79688311 0.76089638 0.96618291 0.89247580 [157] 0.26527307 0.71752892 0.40358040 0.86512076 0.05159085 0.66692303 [163] 0.58699937 0.97723105 0.66692303 0.83804161 0.99186476 0.90287798 [169] 0.90080059 0.99998754 0.88537970 0.66692303 0.73049341 0.83066050 [175] 0.99998754 0.99998754 0.81036934 0.72331685 0.82941122 0.97851743 [181] 0.77951606 0.82171023 0.65101217 0.96372425 0.90284585 0.99998754 [187] 0.76089638 0.95538883 0.99998754 0.98537100 0.22014637 0.89247580 [193] 0.98880162 0.83066050 0.70101815 0.45664879 0.79198573 0.48199367 [199] 0.91541903 0.83781868 0.99186476 0.91099367 0.94212371 0.98668738 [205] 0.99998754 0.82814966 0.23547834 0.84654680 0.06953117 0.99998754 [211] 0.58896252 0.10335077 0.79216762 0.95758005 0.72206522 0.83804161 [217] 0.99998754 0.99998754 0.19721483 0.76089638 0.07210396 0.99998754 [223] 0.79198573 0.92655529 0.94212371 0.94212371 0.83914323 0.99186476 [229] 0.82814966 0.65225017 0.75034864 0.83804161 0.83825450 0.22859726 [235] 0.96618291 0.21045505 0.99998754 0.98537100 0.36464560 0.96368310 [241] 0.49721012 0.95926113 0.73303537 0.98848962 0.51987432 0.95758005 [247] 0.75837768 0.95811412 0.88917389 0.96368310 0.01874064 0.96614165 [253] 0.88537970 0.08838456 0.06725618 0.06837287 0.08838456 0.59996737 [259] 0.30523485 0.99998754 0.97340674 0.99998754 0.99998754 0.13965525 [265] 0.70624905 0.20051374 0.08447214 0.99186476 0.99998754 0.82606668 [271] 0.49710567 0.66925441 0.99998754 0.21405876 0.98883669 0.70101815 [277] 0.97543507 0.66925441 0.31992380 0.92655529 0.65225017 0.43012182 [283] 0.04145927 0.76089638 0.99998754 0.77417639 0.08500756 0.99631496 [289] 0.34976119 0.94422839 0.99998754 0.80766411 0.57459916 0.99998754 [295] 0.76346644 0.99186476 0.80323837 0.44178006 0.79669426 0.87362027 [301] 0.81036934 0.80766411 0.41058627 0.24991466 0.78302714 0.99998754 [307] 0.99464348 0.83804161 0.12520649 0.44941867 0.91541903 0.99998754 [313] 0.98668738 0.62147802 0.99998754 0.99998754 0.91890498 0.83804161 [319] 0.99998754 0.80766411 0.99998754 0.99998754 0.99998754 0.96614165 [325] 0.64213977 0.90080059 0.96368310 0.58699937 0.79934880 0.99998754 [331] 0.71579081 0.07116070 0.30523485 0.17515159 0.58896252 0.20653547 [337] 0.96368310 0.66692303 0.70101815 0.57459916 0.63008628 0.99998754 [343] 0.83117741 0.67908923 0.22791125 0.81388331 0.48414566 0.09270070 [349] 0.63972839 0.99998754 0.96618291 0.69238929 0.96372425 0.01874064 [355] 0.08421969 0.80893032 0.99186476 0.99998754 0.83825450 0.06369417 [361] 0.79198573 0.70612525 0.99998754 0.99186476 0.68935402 0.96614165 [367] 0.04145927 0.24991466 0.76346644 0.57459916 0.70624905 0.99998754 [373] 0.99186476 0.73049341 0.66692303 0.99998754 0.63622788 0.81036934 [379] 0.01874064 0.05494517 0.71579081 0.71573477 0.74003698 0.70101815 [385] 0.95955633 0.89615754 0.70459001 0.96372425 0.76972269 0.83066050 [391] 0.99186476 0.87288398 0.96368310 0.91541903 0.77704148 0.99186476 [397] 0.99998754 0.64360593 0.99998754 0.95758005 0.65321821 0.88537970 [403] 0.80323837 0.82814966 0.99631496 0.99998754 0.80766411 0.99998754 [409] 0.99998754 0.99998754 0.99998754 0.83804161 0.63008628 0.76089638 [415] 0.52098285 0.99998754 0.09254192 0.88554344 0.51987432 0.83804161 [421] 0.84247929 0.36909003 0.81036934 0.94212371 0.50456125 0.99998754 [427] 0.99998754 0.64089898 0.41056319 0.71148746 0.98880162 0.76972269 [433] 0.83804161 0.09627176 0.63008628 0.99875819 0.94314657 0.74226466 [439] 0.82814966 0.71752892 0.99998754 0.98438961 0.83615956 0.68120367 [445] 0.96034467 0.99998754 0.71579081 0.99481740 0.53047943 0.06272731 [451] 0.30523485 0.86512076 0.23736320 0.95758005 0.94192890 0.82862055 [457] 0.63801556 0.92891474 0.99998754 0.09871227 0.13594418 0.64793066 [463] 0.93727255 0.86512076 0.77951606 0.89841248 0.15061093 0.79688311 [469] 0.58896252 0.96372425 0.29434905 0.02089051 0.40358040 0.26664746 [475] 0.03420197 0.68819744 0.99998754 0.99998754 0.63986176 0.96368310 [481] 0.83812768 0.99998754 0.99998754 0.70612525 0.52170732 0.51987432 [487] 0.78234450 0.99631496 0.69238929 0.35906508 0.17515159 0.75470971 [493] 0.83804161 0.63008628 0.83812768 0.91541903 0.89301816 0.43442856 [499] 0.88537970 0.70101815 0.93908979 0.99998754 0.91541903 0.96745607 [505] 0.97851743 0.82814966 0.99186476 0.96368310 0.99998754 0.91692200 [511] 0.32479542 0.87862019 0.99998754 0.26664746 0.80766411 0.99998754 [517] 0.41058627 0.99998754 0.89807513 0.79688311 0.77435870 0.97851743 [523] 0.99998754 0.60362371 0.99998754 0.84247929 0.71573477 0.02543119 [529] 0.08926154 0.54185221 0.95955633 0.99186476 0.91796331 0.84247929 [535] 0.96814032 0.98880162 0.94894098 0.94314657 0.30101663 0.91780129 [541] 0.80766411 0.95947667 0.19488363 0.94937607 0.67364238 0.70532560 [547] 0.22014637 0.71579081 0.73650261 0.83812768 0.59384932 0.99998754 [553] 0.99998754 0.72879527 0.38403999 0.42458125 0.02295419 0.97851743 [559] 0.78302714 0.79426052 0.99998754 0.64213977 0.75470971 0.70101815 [565] 0.96368310 0.82171023 0.99998754 0.91541903 0.83804161 0.95538883 [571] 0.99998754 0.74003698 0.87889510 0.83812768 0.79396819 0.83804161 [577] 0.79397312 0.99998754 0.87862019 0.82814966 0.95320576 0.98880162 [583] 0.12705058 0.89106029 0.72206522 0.94894098 0.99186476 0.69238929 [589] 0.80142881 0.99998754 0.37385566 0.54185221 0.62872565 0.33982175 [595] 0.20653547 0.95538883 0.88917389 0.76089638 0.99998754 0.20241259 [601] 0.35329231 0.42458125 0.69238929 0.68819744 0.96618291 0.95320576 [607] 0.99998754 0.73049341 0.53935140 0.83354148 0.96368310 0.99186476 [613] 0.08208556 0.66692303 0.73666839 0.87862019 0.63972839 0.43632091 [619] 0.72035443 0.99631496 0.99998754 0.01874064 0.94785370 0.42897218 [625] 0.99998754 0.96946641 0.83804161 0.57459916 0.95811412 0.99998754 [631] 0.60512740 0.94122432 0.99186476 0.99998754 0.42458125 0.87862019 [637] 0.53308421 0.49459702 0.83804161 0.97623786 0.20924104 0.06384990 [643] 0.77951606 0.99998754 0.81864557 0.94894098 0.99465837 0.95811412 [649] 0.17623763 0.65427215 0.14313662 0.82814966 0.94714174 0.91541903 [655] 0.95538883 0.95758005 0.65988611 0.63622788 0.73143228 0.99186476 [661] 0.65225017 0.94634972 0.14813083 0.02029857 0.02439149 0.99998754 [667] 0.75034864 0.12520649 0.19721483 0.94314657 0.99998754 0.99998754 [673] 0.91541903 0.99998754 0.99998754 0.94314657 0.82941122 0.02029857 [679] 0.99998754 0.73143228 0.08926154 0.88917389 0.99998754 0.76346644 [685] 0.72331685 0.99998754 0.99998754 0.95926113 0.71148746 0.58699937 [691] 0.25866597 0.82615611 0.79198573 0.99998754 0.25640672 0.98880162 [697] 0.99998754 0.99998754 0.99998754 0.99998754 0.74226466 0.95538883 [703] 0.60512740 0.88554344 0.70101815 0.99998754 0.39974075 0.22706223 [709] 0.08838456 0.01874064 0.72331685 0.79396819 0.83804161 0.99998754 [715] 0.68021227 0.50064393 0.57459916 0.95758005 0.07189097 0.68021227 [721] 0.65040854 0.94937607 0.83812768 0.79216762 0.76089638 0.67026252 [727] 0.11134648 0.82814966 0.99998754 0.50064393 0.09496630 0.04415956 [733] 0.99631496 0.86939835 0.91541903 0.12246665 0.99998754 0.99998754 [739] 0.90287798 0.99998754 0.86512076 0.99998754 0.11134648 0.09871073 [745] 0.13594418 0.99998754 0.81615238 0.95758005 0.05494517 0.14313662 [751] 0.79396819 0.66226707 0.49579133 0.75470971 0.47662079 0.83354148 [757] 0.99465837 0.87862019 0.95990451 0.55858294 0.83136493 0.73577468 [763] 0.65225017 0.60512740 0.60831972 0.40358040 0.99998754 0.82814966 [769] 0.04454181 0.23765808 0.99631496 0.93003664 0.98558013 0.73049341 [775] 0.65704451 0.89615754 0.86512076 0.96368310 0.99998754 0.99998754 [781] 0.90703393 0.70459001 0.91541903 0.70000359 0.95538883 0.65101217 [787] 0.89841248 0.77951606 0.95811412 0.24130540 0.14313662 0.81857116 [793] 0.99998754 0.83804161 0.96618291 0.77951606 0.68352337 0.49710567 [799] 0.70101815 0.13594418 0.51805191 0.90080059 0.71784004 0.78302714 [805] 0.79999112 0.83825450 0.83804161 0.72378010 0.91996980 0.99998754 [811] 0.95990451 0.99998754 0.99186476 0.91099367 0.66925441 0.74484228 [817] 0.26990933 0.65988611 0.89615754 0.91541903 0.89611337 0.94314657 [823] 0.70101815 0.88917389 0.72378010 0.63972839 0.98438961 0.85371294 [829] 0.91541903 0.96368310 0.62004018 0.81275959 0.99998754 0.94894098 [835] 0.70101815 0.65988611 0.83804161 0.97208149 0.79688311 0.95955633 [841] 0.84311185 0.95538883 0.99998754 0.99998754 0.20653547 0.97851743 [847] 0.95538883 0.05218996 0.95811412 0.21462704 0.22797602 0.23343069 [853] 0.79658559 0.84247929 0.46115386 0.42458125 0.14508109 0.68021227 [859] 0.26004264 0.78887199 0.99998754 0.83804161 0.70101815 0.79688311 [865] 0.94894098 0.01874064 0.79747425 0.83066050 0.66925441 0.19721483 [871] 0.90331982 0.01874064 0.26990933 0.80284487 0.65068196 0.19488363 [877] 0.56872992 0.40501858 0.71148746 0.13594418 0.82814966 0.48480794 [883] 0.99186476 0.99998754 0.90703393 0.79198573 0.99998754 0.99998754 [889] 0.57459916 0.99998754 0.83066050 0.28060176 0.06384990 0.70101815 [895] 0.65040854 0.78302714 0.66925441 0.83804161 0.02089051 0.01874064 [901] 0.83825450 0.99998754 0.82814966 0.83804161 0.83825450 0.49079723 [907] 0.99998754 0.96368310 0.01874064 0.09911584 0.88917389 0.96368310 [913] 0.99998754 0.79396819 0.94894098 0.48480794 0.79198573 0.88432251 [919] 0.95926113 0.99998754 0.95926113 0.99186476 0.87360722 0.78960168 [925] 0.60512740 0.74003698 0.84961457 0.93279982 0.60362371 0.91541903 [931] 0.99998754 0.95811412 0.80766411 0.91541903 0.99998754 0.85868136 [937] 0.83795066 0.79198573 0.74226466 0.87576929 0.73049341 0.57459916 [943] 0.11158076 0.91541903 0.94082721 0.95811412 0.92140687 0.83804161 [949] 0.99998754 0.04415956 0.11333385 0.68120367 0.79396819 0.99186476 [955] 0.97851743 0.65101217 0.99998754 0.97340674 0.99998754 0.70101815 [961] 0.22616844 0.14313662 0.79426052 0.77680114 0.94894098 0.79396819 [967] 0.80381432 0.98537100 0.99998754 0.91841930 0.66925441 0.79396819 [973] 0.79047333 0.99998754 0.11198649 0.77505976 0.04096754 0.95538883 [979] 0.99998754 0.86574943 0.87362027 0.71100646 0.83066050 0.47662079 [985] 0.99465837 0.95811412 0.79747425 0.52271749 0.05903125 0.41056319 [991] 0.28262981 0.80683093 0.59611389 0.95538883 0.91541903 0.91541903 [997] 0.70101815 0.66925441 0.68120367 0.73112758 0.99998754 0.98453875 [1003] 0.99186476 0.97618739 0.83615956 0.99998754 0.12705058 0.88737825 [1009] 0.70532560 0.65101217 0.96618291 0.04415956 0.39929055 0.99998754 [1015] 0.99998754 0.79198573 0.91841930 0.99998754 0.89247580 0.78302714 [1021] 0.56661941 0.78302714 0.99465837 0.98880162 0.71148746 0.83804161 [1027] 0.99998754 0.76089638 0.99998754 0.24991466 0.60549182 0.91541903 [1033] 0.88597213 0.88537970 0.83066050 0.99998754 0.99998754 0.99998754 [1039] 0.94634972 0.63230249 0.34646461 0.76762070 0.90287798 0.99998754 [1045] 0.70101815 0.91890498 0.99998754 0.96372425 0.99998754 0.04622448 [1051] 0.68935402 0.62147802 0.99998754 0.11313737 0.95758005 0.14313662 [1057] 0.99998754 0.60549182 0.60831972 0.09254192 0.79198573 0.65459807 [1063] 0.09911584 0.53935140 0.91541903 0.96618291 0.99998754 0.95538883 [1069] 0.99998754 0.66925441 0.67026252 0.71148746 0.70624905 0.39929055 [1075] 0.99998754 0.71100646 0.88438570 0.79688311 0.87032458 0.47966509 [1081] 0.94082721 0.76346644 0.83825450 0.65225017 0.14047342 0.91099367 [1087] 0.99998754 0.99631496 0.66925441 0.56974105 0.99998754 0.82814966 [1093] 0.77951606 0.01874064 0.98880162 0.62550291 0.99998754 0.85947396 [1099] 0.50064393 0.95624026 0.97600448 0.99998754 0.79198573 0.88438570 [1105] 0.50496227 0.79688311 0.23343069 0.96618291 0.79688311 0.94192890 [1111] 0.05400170 0.75347358 0.82814966 0.89012225 0.85947396 0.19108424 [1117] 0.83804161 0.99998754 0.98229676 0.79396819 0.09627176 0.09192033 [1123] 0.99998754 0.67106168 0.66692303 0.79999112 0.91541903 0.87362027 [1129] 0.87862019 0.96618291 0.70624905 0.76346644 0.95758005 0.82171023 [1135] 0.95811412 0.99998754 0.72378010 0.46151810 0.19721483 0.78302714 [1141] 0.21045505 0.96614165 0.65427215 0.99998754 0.71573477 0.23209209 [1147] 0.91796331 0.12326698 0.03420197 0.99998754 0.95538883 0.99186476 [1153] 0.09192033 0.02089051 0.26096854 0.50064393 0.91890498 0.99998754 [1159] 0.94894098 0.90080059 0.96368310 0.96433651 0.70101815 0.99998754 [1165] 0.99186476 0.99186476 0.56018788 0.75347358 0.35906508 0.02089051 [1171] 0.89807513 0.99998754 0.99998754 0.95926113 0.65225017 0.99998754 [1177] 0.04955303 0.76762070 0.70612525 0.79219197 0.99998754 0.99186476 [1183] 0.93279982 0.67243374 0.56018788 0.86334577 0.99998754 0.94192890 [1189] 0.71148746 0.98453875 0.99998754 0.53935140 0.83028153 0.63008628 [1195] 0.89482611 0.78302714 0.76089638 0.88537970 0.72869073 0.82171023 [1201] 0.96622883 0.19823457 0.95624026 0.81857116 0.88537970 0.83812768 [1207] 0.51987432 0.88597213 0.24130540 0.91692200 0.65988611 0.82814966 [1213] 0.12233210 0.04785973 0.75021453 0.83804161 0.98880162 0.83804161 [1219] 0.95811412 0.95538883 0.95538883 0.67727498 0.91541903 0.05456303 [1225] 0.89012225 0.71148746 0.68819744 0.86764617 0.65040854 0.91692200 [1231] 0.79396819 0.66925441 0.79396819 0.01874064 0.99186476 0.96618291 [1237] 0.70532560 0.19721483 0.70101815 0.82814966 0.66925441 0.91541903 [1243] 0.99998754 0.95538883 0.99998754 0.99998754 0.19998703 0.99186476 [1249] 0.70532560 0.23209209 0.57459916 0.99186476 0.89049484 0.76089638 [1255] 0.48480794 0.63622788 0.99998754 0.98880162 0.83804161 0.79658559 [1261] 0.99998754 0.11158076 0.97543507 0.65988611 0.99998754 0.83804161 [1267] 0.83804161 0.99998754 0.61459000 0.65225017 0.72331685 0.53308421 [1273] 0.04145927 0.57459916 0.38759236 0.84654680 0.81036934 0.94361963 [1279] 0.66925441 0.90912611 0.99998754 0.98668738 0.09911584 0.04955303 [1285] 0.90912611 0.42897218 0.89611337 0.70101815 0.99186476 0.93849348 [1291] 0.79688311 0.80081767 0.99998754 0.55839584 0.61459000 0.99998754 [1297] 0.99631496 0.71579081 0.99998754 0.96368310 0.97851743 0.99998754 [1303] 0.99998754 0.79669426 0.98880162 0.83804161 0.99998754 0.80502201 [1309] 0.99631496 0.99998754 0.83804161 0.99998754 0.51987432 0.99186476 [1315] 0.99998754 0.96618291 0.24130540 0.89807513 0.99998754 0.70101815 [1321] 0.79198573 0.99998754 0.95926113 0.70101815 0.73889617 0.63972839 [1327] 0.84247929 0.96906213 0.90080059 0.75021453 0.01903434 0.27329628 [1333] 0.80502201 0.99998754 0.95538883 0.91692200 0.08251742 0.71784004 [1339] 0.70101815 0.83804161 0.91541903 0.36909003 0.99998754 0.99998754 [1345] 0.99998754 0.22706223 0.80323837 0.80323837 0.95758005 0.89049484 [1351] 0.94314657 0.92587301 0.70101815 0.65225017 0.97851743 0.11685126 [1357] 0.67106168 0.67243374 0.09054552 0.86512076 0.09192033 0.57459916 [1363] 0.83825450 0.91890498 0.94894098 0.99998754 0.91841930 0.95758005 [1369] 0.99998754 0.81036934 0.99998754 0.57459916 0.60771590 0.04955303 [1375] 0.76089638 0.60512740 0.54185221 0.09247992 0.08838456 0.76685334 [1381] 0.95817738 0.99998754 0.91541903 0.77951606 0.01874064 0.99998754 [1387] 0.01903434 0.96368310 0.89611337 0.99631496 0.70101815 0.64213977 [1393] 0.98453875 0.56522690 0.88120116 0.74789717 0.63972839 0.65427215 [1399] 0.95538883 0.66692303 0.17303513 0.99998754 0.99998754 0.96368310 [1405] 0.73049341 0.39199030 0.70101815 0.83066050 0.83804161 0.10561552 [1411] 0.09520075 0.77436254 0.28060176 0.82171023 0.99998754 0.99186476 [1417] 0.89301816 0.99186476 0.99998754 0.99998754 0.95758005 0.88537970 [1423] 0.80403017 0.99998754 0.13639106 0.23343069 0.96368310 0.83804161 [1429] 0.77951606 0.70101815 0.98880162 0.95143291 0.66692303 0.66692303 [1435] 0.81476692 0.82941122 0.57459916 0.26703562 0.98999815 0.99998754 [1441] 0.99998754 0.99998754 0.96614165 0.06953117 $significant [1] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [133] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [277] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [349] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [361] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [373] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [469] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [553] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [661] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [673] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [709] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [769] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [865] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [949] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [973] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1009] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1045] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1093] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE [1153] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1165] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1177] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1189] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1213] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE [1237] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1249] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1273] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [1285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1297] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE [1333] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1369] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1381] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [1393] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1405] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1441] FALSE FALSE FALSE FALSE $num.significant [1] 45 $pvalue.cutoff [1] 0.001544243 > > > > cleanEx(); ..nameEx <- "fdr.estimate.eta0" > > ### * fdr.estimate.eta0 > > flush(stderr()); flush(stdout()) > > ### Name: fdr.estimate.eta0 > ### Title: Estimate the Proportion of Null p-Values > ### Aliases: fdr.estimate.eta0 > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > > # how many genes and how many samples? > dim(caulobacter) [1] 11 1444 > > # p-values from Fisher's g test > pval.caulobacter <- fisher.g.test(caulobacter) > > # Proportion of null p-values for different methods > fdr.estimate.eta0(pval.caulobacter) [1] 1 > fdr.estimate.eta0(pval.caulobacter, method="adaptive") [1] 0.9328255 > fdr.estimate.eta0(pval.caulobacter, method="bootstrap") [1] 0.8575716 > fdr.estimate.eta0(pval.caulobacter, method="smoother") [1] 1 > > > > cleanEx(); ..nameEx <- "fisher.g.test" > > ### * fisher.g.test > > flush(stderr()); flush(stdout()) > > ### Name: fisher.g.test > ### Title: Fisher's Exact g Test for Multiple (Genetic) Time Series > ### Aliases: fisher.g.test > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # load data set > data(caulobacter) > > # how many samples and and how many genes? > dim(caulobacter) [1] 11 1444 > > # p-values from Fisher's g test > pval.caulobacter <- fisher.g.test(caulobacter) > pval.caulobacter [1] 6.957582e-01 6.572720e-04 3.877791e-04 3.409797e-04 3.808679e-03 [6] 4.153409e-03 1.033611e-03 1.806916e-04 3.492938e-01 7.050289e-01 [11] 7.780124e-02 7.650025e-01 8.918939e-01 6.649296e-01 2.865021e-01 [16] 9.292695e-01 1.919834e-01 2.166660e-03 8.005379e-01 8.951159e-01 [21] 6.200447e-01 9.659602e-01 2.458986e-01 6.380772e-01 5.083606e-01 [26] 8.201076e-01 2.373970e-01 7.290474e-01 4.540155e-01 2.125520e-01 [31] 1.377189e-01 5.476873e-01 4.402326e-01 8.459343e-01 2.900579e-01 [36] 1.990583e-02 5.511967e-01 3.620677e-01 8.208651e-02 4.125649e-01 [41] 8.274811e-01 9.306406e-01 4.932974e-01 6.265290e-01 3.981707e-01 [46] 3.629268e-01 6.651511e-01 4.142419e-01 2.754448e-01 3.448970e-01 [51] 7.857757e-01 4.431577e-01 9.677192e-01 7.312011e-02 1.506115e-01 [56] 4.591084e-01 7.548248e-01 1.780073e-02 2.524271e-01 9.146059e-02 [61] 8.749949e-02 6.158725e-01 6.634993e-01 5.154622e-02 1.528813e-01 [66] 6.909086e-01 6.993570e-01 4.265096e-01 5.204751e-01 4.868765e-01 [71] 6.406963e-01 3.104530e-01 9.702399e-01 9.016301e-01 2.198965e-01 [76] 9.040238e-02 9.695813e-01 5.733719e-01 1.184392e-02 1.086777e-01 [81] 6.909210e-02 9.349006e-01 2.896606e-01 6.247639e-01 6.732243e-01 [86] 7.469375e-01 3.673823e-01 8.049709e-01 7.100718e-01 9.403190e-01 [91] 8.265153e-01 8.644746e-01 2.197912e-02 7.905798e-01 3.501532e-01 [96] 1.118495e-01 4.600084e-01 8.255402e-01 7.700308e-01 1.501435e-01 [101] 1.312772e-01 8.588145e-02 2.666295e-01 6.406726e-01 3.616478e-01 [106] 1.172896e-02 8.566740e-01 9.602021e-01 9.858587e-01 9.268793e-01 [111] 9.165017e-01 3.588045e-01 2.338072e-01 1.554536e-01 8.868706e-01 [116] 6.680362e-01 8.220551e-01 3.622606e-01 1.729644e-01 7.473866e-01 [121] 2.547502e-01 3.150158e-01 8.029990e-01 7.218624e-01 4.988579e-01 [126] 8.920214e-01 9.914074e-01 5.229729e-01 9.348177e-01 4.150144e-01 [131] 4.167467e-01 1.345287e-01 3.742887e-01 1.185967e-04 3.472107e-04 [136] 2.667554e-02 4.563386e-01 9.931242e-01 4.555756e-01 5.272976e-01 [141] 2.970034e-01 7.997943e-01 1.458062e-02 5.993556e-01 3.598636e-01 [146] 3.087767e-01 5.488368e-01 2.088606e-02 2.983366e-03 5.645703e-01 [151] 4.013581e-01 1.475373e-01 3.631304e-01 2.960130e-01 7.458307e-01 [156] 5.426550e-01 3.416952e-02 2.517090e-01 6.155889e-02 4.925830e-01 [161] 1.643476e-03 1.856964e-01 1.268309e-01 7.667609e-01 1.875144e-01 [166] 4.573246e-01 8.311574e-01 5.658619e-01 5.614408e-01 8.675004e-01 [171] 5.273037e-01 1.866561e-01 2.681174e-01 4.251116e-01 8.902975e-01 [176] 8.807892e-01 3.876443e-01 2.594724e-01 4.193321e-01 7.738691e-01 [181] 3.219802e-01 4.009737e-01 1.682982e-01 7.316735e-01 5.633408e-01 [186] 8.885674e-01 3.003461e-01 6.717160e-01 9.021131e-01 7.861132e-01 [191] 2.439295e-02 5.423629e-01 7.982583e-01 4.236666e-01 2.233259e-01 [196] 7.621355e-02 3.359703e-01 8.311387e-02 5.880570e-01 4.345749e-01 [201] 8.321142e-01 5.766262e-01 6.402952e-01 7.894946e-01 9.695539e-01 [206] 4.138271e-01 2.821174e-02 4.760360e-01 2.985410e-03 9.865482e-01 [211] 1.280038e-01 6.870965e-03 3.423217e-01 6.882136e-01 2.568232e-01 [216] 4.385322e-01 8.677993e-01 8.643668e-01 1.946444e-02 2.987561e-01 [221] 3.245677e-03 9.439477e-01 3.371625e-01 6.143802e-01 6.377270e-01 [226] 6.390556e-01 4.672238e-01 8.078267e-01 4.155513e-01 1.729735e-01 [231] 2.866205e-01 4.468063e-01 4.661044e-01 2.627919e-02 7.450308e-01 [236] 2.273614e-02 9.825899e-01 7.858125e-01 5.303018e-02 7.220298e-01 [241] 8.986970e-02 7.056805e-01 2.710809e-01 7.927084e-01 9.900654e-02 [246] 6.928949e-01 2.941077e-01 7.003644e-01 5.356449e-01 7.219664e-01 [251] 1.021960e-04 7.384524e-01 5.259256e-01 4.640287e-03 2.701425e-03 [256] 2.793628e-03 4.634926e-03 1.325413e-01 4.227630e-02 9.692349e-01 [261] 7.589140e-01 9.175020e-01 8.945046e-01 1.141227e-02 2.378904e-01 [266] 2.041241e-02 4.127767e-03 8.166171e-01 9.822976e-01 4.050244e-01 [271] 8.927769e-02 1.928945e-01 8.979106e-01 2.327370e-02 8.032586e-01 [276] 2.281262e-01 7.619742e-01 1.936227e-01 4.453233e-02 6.147091e-01 [281] 1.716166e-01 6.999905e-02 9.991640e-04 2.963258e-01 9.473392e-01 [286] 3.136379e-01 4.238604e-03 8.421128e-01 4.965446e-02 6.473588e-01 [291] 9.362357e-01 3.836964e-01 1.208302e-01 8.977753e-01 3.031602e-01 [296] 8.311371e-01 3.749187e-01 7.282960e-02 3.556775e-01 5.053673e-01 [301] 3.885984e-01 3.815538e-01 6.454507e-02 3.114086e-02 3.288292e-01 [306] 9.162602e-01 8.355281e-01 4.570446e-01 9.537891e-03 7.469562e-02 [311] 5.900778e-01 9.852590e-01 7.889871e-01 1.454706e-01 9.362889e-01 [316] 8.670954e-01 6.054716e-01 4.472269e-01 8.596157e-01 3.823569e-01 [321] 9.441047e-01 9.592196e-01 9.930065e-01 7.386568e-01 1.623137e-01 [326] 5.596849e-01 7.278630e-01 1.266092e-01 3.675676e-01 9.643305e-01 [331] 2.500090e-01 3.104657e-03 4.223752e-02 1.601321e-02 1.281211e-01 [336] 2.171780e-02 7.258995e-01 1.871228e-01 2.255636e-01 1.218652e-01 [341] 1.518490e-01 8.796307e-01 4.276765e-01 2.012813e-01 2.588466e-02 [346] 3.917236e-01 8.382023e-02 5.520956e-03 1.594891e-01 9.903471e-01 [351] 7.455499e-01 2.133748e-01 7.308912e-01 1.312181e-04 4.024348e-03 [356] 3.848581e-01 8.202119e-01 9.960704e-01 4.645862e-01 2.426024e-03 [361] 3.411462e-01 2.353550e-01 9.999875e-01 8.191420e-01 2.100525e-01 [366] 7.375736e-01 1.011863e-03 3.131536e-02 3.041193e-01 1.201271e-01 [371] 2.379065e-01 9.381795e-01 8.325070e-01 2.674887e-01 1.866361e-01 [376] 9.629465e-01 1.556878e-01 3.866703e-01 9.244960e-05 1.906270e-03 [381] 2.503285e-01 2.478306e-01 2.774533e-01 2.199961e-01 7.110286e-01 [386] 5.513217e-01 2.312851e-01 7.328042e-01 3.111535e-01 4.267100e-01 [391] 8.178665e-01 5.029359e-01 7.187750e-01 5.830873e-01 3.180274e-01 [396] 8.205862e-01 8.768745e-01 1.631300e-01 9.297346e-01 6.866389e-01 [401] 1.741614e-01 5.270236e-01 3.732738e-01 4.161881e-01 8.454710e-01 [406] 9.231293e-01 3.804401e-01 8.657298e-01 9.728014e-01 9.315583e-01 [411] 9.850426e-01 4.498170e-01 1.515954e-01 3.012194e-01 9.957844e-02 [416] 9.288190e-01 5.421034e-03 5.281743e-01 9.803985e-02 4.546400e-01 [421] 4.719984e-01 5.415663e-02 3.881779e-01 6.406709e-01 9.364433e-02 [426] 9.915986e-01 9.466315e-01 1.606686e-01 6.388864e-02 2.443890e-01 [431] 7.959988e-01 3.113006e-01 4.425099e-01 5.969156e-03 1.516459e-01 [436] 8.507428e-01 6.439749e-01 2.801349e-01 4.110360e-01 2.522978e-01 [441] 8.590671e-01 7.805582e-01 4.331353e-01 2.047385e-01 7.136079e-01 [446] 9.915615e-01 2.503229e-01 8.391188e-01 1.024957e-01 2.345758e-03 [451] 4.203264e-02 4.919907e-01 2.860194e-02 6.856095e-01 6.359977e-01 [456] 4.183271e-01 1.568529e-01 6.175610e-01 9.803484e-01 6.289148e-03 [461] 1.087317e-02 1.646749e-01 6.276610e-01 4.947336e-01 3.223862e-01 [466] 5.568415e-01 1.345485e-02 3.595046e-01 1.284787e-01 7.326520e-01 [471] 3.995319e-02 3.397330e-04 6.204629e-02 3.471587e-02 7.342529e-04 [476] 2.085420e-01 9.270975e-01 8.650647e-01 1.599654e-01 7.240588e-01 [481] 4.611222e-01 9.941928e-01 8.702079e-01 2.357011e-01 1.000782e-01 [486] 9.775334e-02 3.250739e-01 8.474362e-01 2.120962e-01 5.178397e-02 [491] 1.613238e-02 2.914256e-01 4.438390e-01 1.516367e-01 4.605520e-01 [496] 5.809246e-01 5.441381e-01 7.100079e-02 5.240143e-01 2.267033e-01 [501] 6.301787e-01 9.989272e-01 5.849209e-01 7.497115e-01 7.727085e-01 [506] 4.175159e-01 8.307857e-01 7.253600e-01 9.434394e-01 5.963877e-01 [511] 4.543537e-02 5.128698e-01 9.552839e-01 3.456565e-02 3.834347e-01 [516] 9.102414e-01 6.435882e-02 8.893139e-01 5.548900e-01 3.602259e-01 [521] 3.142481e-01 7.708980e-01 8.553345e-01 1.339819e-01 9.184362e-01 [526] 4.702357e-01 2.477462e-01 4.931256e-04 4.778655e-03 1.075658e-01 [531] 7.097352e-01 8.319253e-01 6.013804e-01 4.704843e-01 7.509122e-01 [536] 8.006773e-01 6.570155e-01 6.427713e-01 4.106667e-02 6.000031e-01 [541] 3.816553e-01 7.083117e-01 1.862461e-02 6.607500e-01 1.987338e-01 [546] 2.335917e-01 2.435867e-02 2.494450e-01 2.733832e-01 4.620150e-01 [551] 1.299559e-01 9.319400e-01 9.581400e-01 2.639612e-01 5.691451e-02 [556] 6.769699e-02 4.133026e-04 7.688968e-01 3.267121e-01 3.525769e-01 [561] 9.949599e-01 1.620463e-01 2.921626e-01 2.208596e-01 7.252941e-01 [566] 4.023193e-01 9.415503e-01 5.927569e-01 4.512701e-01 6.809320e-01 [571] 9.093878e-01 2.774012e-01 5.143119e-01 4.616561e-01 3.488988e-01 [576] 4.492857e-01 3.513496e-01 9.844882e-01 5.131194e-01 4.077239e-01 [581] 6.675256e-01 8.009013e-01 9.847941e-03 5.399430e-01 2.570232e-01 [586] 6.591305e-01 8.264347e-01 2.125855e-01 3.707441e-01 9.853167e-01 [591] 5.514630e-02 1.076950e-01 1.484733e-01 4.777273e-02 2.174058e-02 [596] 6.763464e-01 5.357211e-01 2.997573e-01 8.678383e-01 2.076426e-02 [601] 5.040043e-02 6.792124e-02 2.129812e-01 2.082991e-01 7.441106e-01 [606] 6.669969e-01 9.936190e-01 2.673133e-01 1.059217e-01 4.306246e-01 [611] 7.291241e-01 8.154711e-01 3.806358e-03 1.874063e-01 2.739549e-01 [616] 5.105968e-01 1.581880e-01 7.161223e-02 2.554165e-01 8.479717e-01 [621] 9.626166e-01 1.053702e-04 6.524699e-01 6.943743e-02 8.744291e-01 [626] 7.532835e-01 4.445215e-01 1.209160e-01 6.946932e-01 9.364102e-01 [631] 1.362558e-01 6.335665e-01 8.249599e-01 9.379926e-01 6.775424e-02 [636] 5.122305e-01 1.037373e-01 8.802731e-02 4.500645e-01 7.653056e-01 [641] 2.231518e-02 2.520391e-03 3.218609e-01 9.480053e-01 3.968503e-01 [646] 6.591328e-01 8.368015e-01 6.963739e-01 1.635446e-02 1.758017e-01 [651] 1.238093e-02 4.136834e-01 6.513239e-01 5.943220e-01 6.795014e-01 [656] 6.899170e-01 1.788828e-01 1.556662e-01 2.699104e-01 8.150919e-01 [661] 1.720421e-01 6.499139e-01 1.313071e-02 2.448135e-04 4.560735e-04 [666] 8.990290e-01 2.868369e-01 9.515209e-03 1.953028e-02 6.453108e-01 [671] 9.857320e-01 9.718811e-01 5.812481e-01 9.814259e-01 9.987588e-01 [676] 6.444588e-01 4.201170e-01 2.530293e-04 9.722392e-01 2.699816e-01 [681] 4.821607e-03 5.351362e-01 9.474984e-01 3.050693e-01 2.593710e-01 [686] 9.816021e-01 9.969266e-01 7.046633e-01 2.427893e-01 1.260618e-01 [691] 3.278107e-02 4.056404e-01 3.383441e-01 9.616519e-01 3.231719e-02 [696] 7.951003e-01 9.065626e-01 9.445137e-01 9.367120e-01 9.597458e-01 [701] 2.797956e-01 6.804222e-01 1.370337e-01 5.286277e-01 2.250303e-01 [706] 8.697296e-01 6.062550e-02 2.550944e-02 4.651819e-03 1.690289e-04 [711] 2.579959e-01 3.492557e-01 4.389570e-01 9.201090e-01 2.027734e-01 [716] 9.241783e-02 1.211306e-01 6.918357e-01 3.186303e-03 2.023917e-01 [721] 1.659317e-01 6.602783e-01 4.616060e-01 3.419425e-01 3.014077e-01 [726] 1.954159e-01 7.633865e-03 4.141055e-01 9.718490e-01 9.246178e-02 [731] 5.721654e-03 1.179999e-03 8.443594e-01 4.997234e-01 5.871057e-01 [736] 9.074745e-03 9.906487e-01 9.369051e-01 5.646390e-01 9.605563e-01 [741] 4.938036e-01 9.828271e-01 7.625638e-03 6.220690e-03 1.092072e-02 [746] 9.529104e-01 3.939461e-01 6.915874e-01 1.940584e-03 1.211826e-02 [751] 3.458169e-01 1.816189e-01 8.858322e-02 2.916807e-01 8.119717e-02 [756] 4.300965e-01 8.382959e-01 5.135425e-01 7.126161e-01 1.125676e-01 [761] 4.283487e-01 2.726035e-01 1.734516e-01 1.366409e-01 1.402843e-01 [766] 6.186259e-02 9.923443e-01 4.106757e-01 1.233845e-03 2.880205e-02 [771] 8.455883e-01 6.189510e-01 7.869625e-01 2.658458e-01 1.774566e-01 [776] 5.523409e-01 4.920584e-01 7.194589e-01 9.383944e-01 9.438998e-01 [781] 5.703510e-01 2.308209e-01 5.890828e-01 2.162061e-01 6.739332e-01 [786] 1.690648e-01 5.566905e-01 3.195920e-01 6.974147e-01 2.974540e-02 [791] 1.248976e-02 3.959422e-01 9.609203e-01 4.528756e-01 7.468850e-01 [796] 3.233588e-01 2.059090e-01 8.950656e-02 2.186007e-01 1.082131e-02 [801] 9.686566e-02 5.609556e-01 2.535308e-01 3.289491e-01 3.684633e-01 [806] 4.661023e-01 4.519616e-01 2.606708e-01 6.077917e-01 9.348124e-01 [811] 7.123743e-01 9.125393e-01 8.091288e-01 5.761414e-01 1.933778e-01 [816] 2.816370e-01 3.568191e-02 1.805090e-01 5.523296e-01 5.813616e-01 [821] 5.498313e-01 6.442930e-01 2.290860e-01 5.353114e-01 2.605694e-01 [826] 1.588067e-01 7.802966e-01 4.812482e-01 5.924484e-01 7.172330e-01 [831] 1.442753e-01 3.906199e-01 8.875678e-01 6.567467e-01 2.252117e-01 [836] 1.799217e-01 4.511542e-01 7.566618e-01 3.623843e-01 7.104871e-01 [841] 4.729367e-01 6.802957e-01 8.803732e-01 8.815120e-01 2.155963e-02 [846] 7.692687e-01 6.814754e-01 1.698704e-03 7.007347e-01 2.348412e-02 [851] 2.604989e-02 2.772953e-02 3.547123e-01 4.708619e-01 7.728479e-02 [856] 6.763311e-02 1.275990e-02 2.030273e-01 3.313563e-02 3.327029e-01 [861] 9.897549e-01 4.532453e-01 2.177856e-01 3.637008e-01 6.545863e-01 [866] 1.673984e-04 3.655036e-01 4.251163e-01 1.917127e-01 1.917994e-02 [871] 5.667644e-01 9.574902e-05 3.570130e-02 3.719551e-01 1.671766e-01 [876] 1.860429e-02 1.165818e-01 6.254788e-02 2.437254e-01 1.084871e-02 [881] 4.156172e-01 8.447736e-02 8.123455e-01 9.806683e-01 5.701668e-01 [886] 3.409775e-01 9.020057e-01 9.944149e-01 1.204878e-01 9.804804e-01 [891] 4.234213e-01 3.768866e-02 2.507265e-03 2.215405e-01 1.664054e-01 [896] 3.287257e-01 1.897209e-01 4.374497e-01 3.457080e-04 2.034930e-05 [901] 4.650868e-01 9.766230e-01 4.107538e-01 4.567503e-01 4.652241e-01 [906] 8.667126e-02 9.522607e-01 7.294360e-01 2.682115e-05 6.456708e-03 [911] 5.337513e-01 7.174880e-01 9.642602e-01 3.507976e-01 6.591116e-01 [916] 8.490271e-02 3.379324e-01 5.187127e-01 7.063862e-01 9.983893e-01 [921] 7.074883e-01 8.250495e-01 5.039576e-01 3.335575e-01 1.368796e-01 [926] 2.777701e-01 4.783495e-01 6.222744e-01 1.346031e-01 5.914629e-01 [931] 9.454357e-01 6.988868e-01 3.831617e-01 5.836115e-01 9.881672e-01 [936] 4.846436e-01 4.352237e-01 3.384386e-01 2.801484e-01 5.076308e-01 [941] 2.673246e-01 1.207577e-01 7.804471e-03 5.928348e-01 6.321820e-01 [946] 6.978468e-01 6.093792e-01 4.535714e-01 8.992148e-01 1.136191e-03 [951] 8.162549e-03 2.045287e-01 3.464981e-01 8.306309e-01 7.726178e-01 [956] 1.684516e-01 9.996171e-01 7.590415e-01 9.602712e-01 2.274324e-01 [961] 2.521684e-02 1.229460e-02 3.520556e-01 3.173910e-01 6.577730e-01 [966] 3.472595e-01 3.757442e-01 7.851130e-01 9.299585e-01 6.032045e-01 [971] 1.924160e-01 3.501480e-01 3.344731e-01 8.751086e-01 7.910403e-03 [976] 3.161428e-01 9.078679e-04 6.733678e-01 9.372217e-01 4.958274e-01 [981] 5.046865e-01 2.407771e-01 4.253970e-01 8.106489e-02 8.376135e-01 [986] 6.981286e-01 3.656011e-01 1.006340e-01 2.136966e-03 6.397280e-02 [991] 3.816677e-02 3.793893e-01 1.312685e-01 6.768749e-01 5.879401e-01 [996] 5.946420e-01 2.200591e-01 1.917649e-01 2.046407e-01 2.688565e-01 [1001] 9.927945e-01 7.827219e-01 8.216932e-01 7.639140e-01 4.331255e-01 [1006] 8.536059e-01 9.854338e-03 5.315666e-01 2.339500e-01 1.689562e-01 [1011] 7.458535e-01 1.192675e-03 6.015991e-02 9.362453e-01 9.719672e-01 [1016] 3.407929e-01 6.035872e-01 8.779567e-01 5.418943e-01 3.296956e-01 [1021] 1.157567e-01 3.269984e-01 8.370632e-01 7.939335e-01 2.418308e-01 [1026] 4.429760e-01 8.868583e-01 2.996306e-01 9.445083e-01 3.132587e-02 [1031] 1.382059e-01 5.831313e-01 5.301108e-01 5.219493e-01 4.237601e-01 [1036] 9.123520e-01 9.353518e-01 9.996158e-01 6.501239e-01 1.532589e-01 [1041] 4.894652e-02 3.088557e-01 5.654534e-01 9.177715e-01 2.206476e-01 [1046] 6.046674e-01 9.320981e-01 7.321181e-01 9.333605e-01 1.312468e-03 [1051] 2.096150e-01 1.451235e-01 9.899671e-01 8.070048e-03 6.894543e-01 [1056] 1.240755e-02 8.955552e-01 1.383742e-01 1.402303e-01 5.447412e-03 [1061] 3.371312e-01 1.763426e-01 6.471613e-03 1.060774e-01 5.844932e-01 [1066] 7.436239e-01 9.984208e-01 6.785675e-01 8.963255e-01 1.913970e-01 [1071] 1.952804e-01 2.424176e-01 2.373751e-01 6.028071e-02 9.587592e-01 [1076] 2.404029e-01 5.205871e-01 3.591339e-01 5.008585e-01 8.238015e-02 [1081] 6.326477e-01 3.042068e-01 4.632327e-01 1.716829e-01 1.157641e-02 [1086] 5.763309e-01 8.710321e-01 8.431919e-01 1.905977e-01 1.171836e-01 [1091] 9.786325e-01 4.112923e-01 3.232382e-01 7.087520e-05 8.007405e-01 [1096] 1.468459e-01 9.646273e-01 4.857101e-01 9.223585e-02 6.834072e-01 [1101] 7.630949e-01 8.706647e-01 3.394838e-01 5.202456e-01 9.406846e-02 [1106] 3.601428e-01 2.774861e-02 7.452649e-01 3.591567e-01 6.355553e-01 [1111] 1.795070e-03 2.898256e-01 4.080636e-01 5.369867e-01 4.866465e-01 [1116] 1.799685e-02 4.571740e-01 9.103782e-01 7.775382e-01 3.503138e-01 [1121] 6.000318e-03 5.173214e-03 9.032226e-01 1.962604e-01 1.855214e-01 [1126] 3.689710e-01 5.909792e-01 5.057802e-01 5.113507e-01 7.409818e-01 [1131] 2.381879e-01 3.047865e-01 6.874998e-01 4.012675e-01 7.012597e-01 [1136] 8.578441e-01 2.611423e-01 7.798505e-02 1.918213e-02 3.294593e-01 [1141] 2.262600e-02 7.366264e-01 1.756298e-01 9.999396e-01 2.473014e-01 [1146] 2.716313e-02 6.011133e-01 9.219414e-03 7.160083e-04 8.744555e-01 [1151] 6.794000e-01 8.299704e-01 5.097563e-03 2.943119e-04 3.343434e-02 [1156] 9.257059e-02 6.064518e-01 9.259609e-01 6.565869e-01 5.610267e-01 [1161] 7.204515e-01 7.339376e-01 2.239772e-01 8.675129e-01 8.315208e-01 [1166] 8.288535e-01 1.135566e-01 2.901186e-01 5.196995e-02 3.191262e-04 [1171] 5.553692e-01 8.841875e-01 9.747981e-01 7.050407e-01 1.726083e-01 [1176] 9.139211e-01 1.531392e-03 3.084814e-01 2.351344e-01 3.428809e-01 [1181] 9.146032e-01 8.140290e-01 6.227279e-01 1.979116e-01 1.136669e-01 [1186] 4.896677e-01 8.994918e-01 6.355617e-01 2.439593e-01 7.818651e-01 [1191] 9.338323e-01 1.053343e-01 4.214656e-01 1.512838e-01 5.459431e-01 [1196] 3.294818e-01 2.981122e-01 5.259052e-01 2.634187e-01 4.003674e-01 [1201] 7.480913e-01 1.983166e-02 6.829663e-01 3.962474e-01 5.267314e-01 [1206] 4.600526e-01 9.891912e-02 5.297584e-01 2.971309e-02 5.974698e-01 [1211] 1.793098e-01 4.173478e-01 8.980057e-03 1.392042e-03 2.852270e-01 [1216] 4.509667e-01 7.959867e-01 4.457942e-01 7.013342e-01 6.792209e-01 [1221] 6.812030e-01 2.002745e-01 5.902089e-01 1.851516e-03 5.375253e-01 [1226] 2.442172e-01 2.087469e-01 4.975146e-01 1.666559e-01 5.975315e-01 [1231] 3.474704e-01 1.938561e-01 3.459574e-01 1.816959e-04 8.303703e-01 [1236] 7.429790e-01 2.333124e-01 1.938687e-02 2.271341e-01 4.170813e-01 [1241] 1.898019e-01 5.945289e-01 8.953346e-01 6.802865e-01 9.942728e-01 [1246] 8.850626e-01 2.022030e-02 8.182462e-01 2.339688e-01 2.704030e-02 [1251] 1.226598e-01 8.201587e-01 5.387029e-01 3.011918e-01 8.494211e-02 [1256] 1.559728e-01 9.177945e-01 8.018606e-01 4.501169e-01 3.544321e-01 [1261] 9.348873e-01 7.737535e-03 7.618657e-01 1.801546e-01 9.658815e-01 [1266] 4.418612e-01 4.367811e-01 8.790757e-01 1.425815e-01 1.730006e-01 [1271] 2.591742e-01 1.036088e-01 1.023781e-03 1.226695e-01 5.770939e-02 [1276] 4.759576e-01 3.889099e-01 6.462880e-01 1.941950e-01 5.735553e-01 [1281] 9.287005e-01 7.904743e-01 6.520779e-03 1.502478e-03 5.734128e-01 [1286] 6.951488e-02 5.481882e-01 2.277598e-01 8.254309e-01 6.291286e-01 [1291] 3.642264e-01 3.699068e-01 8.852641e-01 1.121432e-01 1.423599e-01 [1296] 8.734297e-01 8.447231e-01 2.491936e-01 9.448490e-01 7.271265e-01 [1301] 7.736852e-01 8.639188e-01 9.836097e-01 3.558641e-01 7.968994e-01 [1306] 4.374252e-01 9.695788e-01 3.775602e-01 8.468990e-01 9.795808e-01 [1311] 4.545902e-01 9.838040e-01 9.832613e-02 8.120928e-01 9.238701e-01 [1316] 7.437641e-01 2.941839e-02 5.553886e-01 9.999447e-01 2.266798e-01 [1321] 3.399635e-01 9.510012e-01 7.068365e-01 2.291417e-01 2.758068e-01 [1326] 1.592216e-01 4.715313e-01 7.522982e-01 5.599509e-01 2.848102e-01 [1331] 2.067470e-04 3.633856e-02 3.779812e-01 9.095188e-01 6.807155e-01 [1336] 5.981583e-01 3.885862e-03 2.533911e-01 2.288072e-01 4.535321e-01 [1341] 5.880058e-01 5.418773e-02 8.804519e-01 9.159724e-01 9.809949e-01 [1346] 2.563099e-02 3.741069e-01 3.745867e-01 6.929856e-01 5.389837e-01 [1351] 6.447368e-01 6.129741e-01 2.237609e-01 1.709164e-01 7.717357e-01 [1356] 8.496802e-03 1.965783e-01 1.975437e-01 4.953667e-03 4.948682e-01 [1361] 5.219853e-03 1.222875e-01 4.661485e-01 6.063303e-01 6.558735e-01 [1366] 9.610701e-01 6.024654e-01 6.926055e-01 9.816451e-01 3.883081e-01 [1371] 9.294555e-01 1.229579e-01 1.393033e-01 1.544243e-03 2.982837e-01 [1376] 1.362086e-01 1.076612e-01 5.315674e-03 4.620034e-03 3.069538e-01 [1381] 7.020441e-01 8.834083e-01 5.904733e-01 3.214284e-01 6.673249e-05 [1386] 9.926557e-01 2.109068e-04 7.285113e-01 5.496351e-01 8.463240e-01 [1391] 2.172265e-01 1.617920e-01 7.824169e-01 1.150808e-01 5.162716e-01 [1396] 2.833101e-01 1.594076e-01 1.755835e-01 6.752280e-01 1.874966e-01 [1401] 1.569779e-02 9.969462e-01 9.909602e-01 7.247149e-01 2.679907e-01 [1406] 5.863567e-02 2.216278e-01 4.268352e-01 4.400036e-01 7.094671e-03 [1411] 5.801707e-03 3.153221e-01 3.769857e-02 4.017904e-01 8.760763e-01 [1416] 8.170560e-01 5.442216e-01 8.252882e-01 8.964636e-01 9.393250e-01 [1421] 6.860821e-01 5.231100e-01 3.764019e-01 8.569157e-01 1.105108e-02 [1426] 2.780476e-02 7.239196e-01 4.412772e-01 3.224004e-01 2.278989e-01 [1431] 8.012767e-01 6.630372e-01 1.852143e-01 1.836574e-01 3.927131e-01 [1436] 4.204495e-01 1.195382e-01 3.495134e-02 8.055733e-01 8.717563e-01 [1441] 8.854306e-01 9.796768e-01 7.382750e-01 2.952665e-03 > > # test with FDR controlled at on the level 0.05 > fdr.control(pval.caulobacter, Q = 0.05) $qvalues [1] 0.95811412 0.03272761 0.02239812 0.02089051 0.08208556 0.08447214 [7] 0.04145927 0.01874064 0.79396819 0.95926113 0.46151810 0.97623786 [13] 0.99998754 0.95191096 0.75034864 0.99998754 0.66925441 0.05903125 [19] 0.98880162 0.99998754 0.93071158 0.99998754 0.71444177 0.94212371 [25] 0.87598178 0.99186476 0.70624905 0.96368310 0.83804161 0.69238929 [31] 0.60549182 0.89611337 0.83804161 0.99631496 0.75347358 0.19823457 [37] 0.89615754 0.79688311 0.47966509 0.82814966 0.99186476 0.99998754 [43] 0.86512076 0.93655054 0.82019765 0.79688311 0.95191096 0.82814966 [49] 0.73889617 0.79396819 0.98537100 0.83804161 0.99998754 0.44178006 [55] 0.63008628 0.83812768 0.97058499 0.19040190 0.71752892 0.50064393 [61] 0.49355179 0.92734087 0.95143291 0.35906508 0.63230249 0.95758005 [67] 0.95811412 0.83066050 0.88438570 0.85947396 0.94212371 0.76972269 [73] 0.99998754 0.99998754 0.70101815 0.49824821 0.99998754 0.90912611 [79] 0.14134399 0.54489777 0.42897218 0.99998754 0.75347358 0.93487993 [85] 0.95538883 0.96618291 0.79934880 0.98999815 0.95955633 0.99998754 [91] 0.99186476 0.99998754 0.20743697 0.98668738 0.79396819 0.55839584 [97] 0.83812768 0.99186476 0.97851743 0.63008628 0.59611389 0.48823943 [103] 0.73049341 0.94212371 0.79688311 0.14113845 0.99998754 0.99998754 [109] 0.99998754 0.99998754 0.99998754 0.79688311 0.70532560 0.63622788 [115] 0.99998754 0.95320576 0.99186476 0.79688311 0.65225017 0.96618291 [121] 0.71988122 0.77436254 0.98883669 0.96368310 0.86893946 0.99998754 [127] 0.99998754 0.88537970 0.99998754 0.82814966 0.82814966 0.60362371 [133] 0.80323837 0.01874064 0.02089051 0.23065554 0.83804161 0.99998754 [139] 0.83804161 0.88537970 0.76089638 0.98880162 0.16195709 0.91778305 [145] 0.79688311 0.76762070 0.89611337 0.20241259 0.06953117 0.90287798 [151] 0.82171023 0.62659977 0.79688311 0.76089638 0.96618291 0.89247580 [157] 0.26527307 0.71752892 0.40358040 0.86512076 0.05159085 0.66692303 [163] 0.58699937 0.97723105 0.66692303 0.83804161 0.99186476 0.90287798 [169] 0.90080059 0.99998754 0.88537970 0.66692303 0.73049341 0.83066050 [175] 0.99998754 0.99998754 0.81036934 0.72331685 0.82941122 0.97851743 [181] 0.77951606 0.82171023 0.65101217 0.96372425 0.90284585 0.99998754 [187] 0.76089638 0.95538883 0.99998754 0.98537100 0.22014637 0.89247580 [193] 0.98880162 0.83066050 0.70101815 0.45664879 0.79198573 0.48199367 [199] 0.91541903 0.83781868 0.99186476 0.91099367 0.94212371 0.98668738 [205] 0.99998754 0.82814966 0.23547834 0.84654680 0.06953117 0.99998754 [211] 0.58896252 0.10335077 0.79216762 0.95758005 0.72206522 0.83804161 [217] 0.99998754 0.99998754 0.19721483 0.76089638 0.07210396 0.99998754 [223] 0.79198573 0.92655529 0.94212371 0.94212371 0.83914323 0.99186476 [229] 0.82814966 0.65225017 0.75034864 0.83804161 0.83825450 0.22859726 [235] 0.96618291 0.21045505 0.99998754 0.98537100 0.36464560 0.96368310 [241] 0.49721012 0.95926113 0.73303537 0.98848962 0.51987432 0.95758005 [247] 0.75837768 0.95811412 0.88917389 0.96368310 0.01874064 0.96614165 [253] 0.88537970 0.08838456 0.06725618 0.06837287 0.08838456 0.59996737 [259] 0.30523485 0.99998754 0.97340674 0.99998754 0.99998754 0.13965525 [265] 0.70624905 0.20051374 0.08447214 0.99186476 0.99998754 0.82606668 [271] 0.49710567 0.66925441 0.99998754 0.21405876 0.98883669 0.70101815 [277] 0.97543507 0.66925441 0.31992380 0.92655529 0.65225017 0.43012182 [283] 0.04145927 0.76089638 0.99998754 0.77417639 0.08500756 0.99631496 [289] 0.34976119 0.94422839 0.99998754 0.80766411 0.57459916 0.99998754 [295] 0.76346644 0.99186476 0.80323837 0.44178006 0.79669426 0.87362027 [301] 0.81036934 0.80766411 0.41058627 0.24991466 0.78302714 0.99998754 [307] 0.99464348 0.83804161 0.12520649 0.44941867 0.91541903 0.99998754 [313] 0.98668738 0.62147802 0.99998754 0.99998754 0.91890498 0.83804161 [319] 0.99998754 0.80766411 0.99998754 0.99998754 0.99998754 0.96614165 [325] 0.64213977 0.90080059 0.96368310 0.58699937 0.79934880 0.99998754 [331] 0.71579081 0.07116070 0.30523485 0.17515159 0.58896252 0.20653547 [337] 0.96368310 0.66692303 0.70101815 0.57459916 0.63008628 0.99998754 [343] 0.83117741 0.67908923 0.22791125 0.81388331 0.48414566 0.09270070 [349] 0.63972839 0.99998754 0.96618291 0.69238929 0.96372425 0.01874064 [355] 0.08421969 0.80893032 0.99186476 0.99998754 0.83825450 0.06369417 [361] 0.79198573 0.70612525 0.99998754 0.99186476 0.68935402 0.96614165 [367] 0.04145927 0.24991466 0.76346644 0.57459916 0.70624905 0.99998754 [373] 0.99186476 0.73049341 0.66692303 0.99998754 0.63622788 0.81036934 [379] 0.01874064 0.05494517 0.71579081 0.71573477 0.74003698 0.70101815 [385] 0.95955633 0.89615754 0.70459001 0.96372425 0.76972269 0.83066050 [391] 0.99186476 0.87288398 0.96368310 0.91541903 0.77704148 0.99186476 [397] 0.99998754 0.64360593 0.99998754 0.95758005 0.65321821 0.88537970 [403] 0.80323837 0.82814966 0.99631496 0.99998754 0.80766411 0.99998754 [409] 0.99998754 0.99998754 0.99998754 0.83804161 0.63008628 0.76089638 [415] 0.52098285 0.99998754 0.09254192 0.88554344 0.51987432 0.83804161 [421] 0.84247929 0.36909003 0.81036934 0.94212371 0.50456125 0.99998754 [427] 0.99998754 0.64089898 0.41056319 0.71148746 0.98880162 0.76972269 [433] 0.83804161 0.09627176 0.63008628 0.99875819 0.94314657 0.74226466 [439] 0.82814966 0.71752892 0.99998754 0.98438961 0.83615956 0.68120367 [445] 0.96034467 0.99998754 0.71579081 0.99481740 0.53047943 0.06272731 [451] 0.30523485 0.86512076 0.23736320 0.95758005 0.94192890 0.82862055 [457] 0.63801556 0.92891474 0.99998754 0.09871227 0.13594418 0.64793066 [463] 0.93727255 0.86512076 0.77951606 0.89841248 0.15061093 0.79688311 [469] 0.58896252 0.96372425 0.29434905 0.02089051 0.40358040 0.26664746 [475] 0.03420197 0.68819744 0.99998754 0.99998754 0.63986176 0.96368310 [481] 0.83812768 0.99998754 0.99998754 0.70612525 0.52170732 0.51987432 [487] 0.78234450 0.99631496 0.69238929 0.35906508 0.17515159 0.75470971 [493] 0.83804161 0.63008628 0.83812768 0.91541903 0.89301816 0.43442856 [499] 0.88537970 0.70101815 0.93908979 0.99998754 0.91541903 0.96745607 [505] 0.97851743 0.82814966 0.99186476 0.96368310 0.99998754 0.91692200 [511] 0.32479542 0.87862019 0.99998754 0.26664746 0.80766411 0.99998754 [517] 0.41058627 0.99998754 0.89807513 0.79688311 0.77435870 0.97851743 [523] 0.99998754 0.60362371 0.99998754 0.84247929 0.71573477 0.02543119 [529] 0.08926154 0.54185221 0.95955633 0.99186476 0.91796331 0.84247929 [535] 0.96814032 0.98880162 0.94894098 0.94314657 0.30101663 0.91780129 [541] 0.80766411 0.95947667 0.19488363 0.94937607 0.67364238 0.70532560 [547] 0.22014637 0.71579081 0.73650261 0.83812768 0.59384932 0.99998754 [553] 0.99998754 0.72879527 0.38403999 0.42458125 0.02295419 0.97851743 [559] 0.78302714 0.79426052 0.99998754 0.64213977 0.75470971 0.70101815 [565] 0.96368310 0.82171023 0.99998754 0.91541903 0.83804161 0.95538883 [571] 0.99998754 0.74003698 0.87889510 0.83812768 0.79396819 0.83804161 [577] 0.79397312 0.99998754 0.87862019 0.82814966 0.95320576 0.98880162 [583] 0.12705058 0.89106029 0.72206522 0.94894098 0.99186476 0.69238929 [589] 0.80142881 0.99998754 0.37385566 0.54185221 0.62872565 0.33982175 [595] 0.20653547 0.95538883 0.88917389 0.76089638 0.99998754 0.20241259 [601] 0.35329231 0.42458125 0.69238929 0.68819744 0.96618291 0.95320576 [607] 0.99998754 0.73049341 0.53935140 0.83354148 0.96368310 0.99186476 [613] 0.08208556 0.66692303 0.73666839 0.87862019 0.63972839 0.43632091 [619] 0.72035443 0.99631496 0.99998754 0.01874064 0.94785370 0.42897218 [625] 0.99998754 0.96946641 0.83804161 0.57459916 0.95811412 0.99998754 [631] 0.60512740 0.94122432 0.99186476 0.99998754 0.42458125 0.87862019 [637] 0.53308421 0.49459702 0.83804161 0.97623786 0.20924104 0.06384990 [643] 0.77951606 0.99998754 0.81864557 0.94894098 0.99465837 0.95811412 [649] 0.17623763 0.65427215 0.14313662 0.82814966 0.94714174 0.91541903 [655] 0.95538883 0.95758005 0.65988611 0.63622788 0.73143228 0.99186476 [661] 0.65225017 0.94634972 0.14813083 0.02029857 0.02439149 0.99998754 [667] 0.75034864 0.12520649 0.19721483 0.94314657 0.99998754 0.99998754 [673] 0.91541903 0.99998754 0.99998754 0.94314657 0.82941122 0.02029857 [679] 0.99998754 0.73143228 0.08926154 0.88917389 0.99998754 0.76346644 [685] 0.72331685 0.99998754 0.99998754 0.95926113 0.71148746 0.58699937 [691] 0.25866597 0.82615611 0.79198573 0.99998754 0.25640672 0.98880162 [697] 0.99998754 0.99998754 0.99998754 0.99998754 0.74226466 0.95538883 [703] 0.60512740 0.88554344 0.70101815 0.99998754 0.39974075 0.22706223 [709] 0.08838456 0.01874064 0.72331685 0.79396819 0.83804161 0.99998754 [715] 0.68021227 0.50064393 0.57459916 0.95758005 0.07189097 0.68021227 [721] 0.65040854 0.94937607 0.83812768 0.79216762 0.76089638 0.67026252 [727] 0.11134648 0.82814966 0.99998754 0.50064393 0.09496630 0.04415956 [733] 0.99631496 0.86939835 0.91541903 0.12246665 0.99998754 0.99998754 [739] 0.90287798 0.99998754 0.86512076 0.99998754 0.11134648 0.09871073 [745] 0.13594418 0.99998754 0.81615238 0.95758005 0.05494517 0.14313662 [751] 0.79396819 0.66226707 0.49579133 0.75470971 0.47662079 0.83354148 [757] 0.99465837 0.87862019 0.95990451 0.55858294 0.83136493 0.73577468 [763] 0.65225017 0.60512740 0.60831972 0.40358040 0.99998754 0.82814966 [769] 0.04454181 0.23765808 0.99631496 0.93003664 0.98558013 0.73049341 [775] 0.65704451 0.89615754 0.86512076 0.96368310 0.99998754 0.99998754 [781] 0.90703393 0.70459001 0.91541903 0.70000359 0.95538883 0.65101217 [787] 0.89841248 0.77951606 0.95811412 0.24130540 0.14313662 0.81857116 [793] 0.99998754 0.83804161 0.96618291 0.77951606 0.68352337 0.49710567 [799] 0.70101815 0.13594418 0.51805191 0.90080059 0.71784004 0.78302714 [805] 0.79999112 0.83825450 0.83804161 0.72378010 0.91996980 0.99998754 [811] 0.95990451 0.99998754 0.99186476 0.91099367 0.66925441 0.74484228 [817] 0.26990933 0.65988611 0.89615754 0.91541903 0.89611337 0.94314657 [823] 0.70101815 0.88917389 0.72378010 0.63972839 0.98438961 0.85371294 [829] 0.91541903 0.96368310 0.62004018 0.81275959 0.99998754 0.94894098 [835] 0.70101815 0.65988611 0.83804161 0.97208149 0.79688311 0.95955633 [841] 0.84311185 0.95538883 0.99998754 0.99998754 0.20653547 0.97851743 [847] 0.95538883 0.05218996 0.95811412 0.21462704 0.22797602 0.23343069 [853] 0.79658559 0.84247929 0.46115386 0.42458125 0.14508109 0.68021227 [859] 0.26004264 0.78887199 0.99998754 0.83804161 0.70101815 0.79688311 [865] 0.94894098 0.01874064 0.79747425 0.83066050 0.66925441 0.19721483 [871] 0.90331982 0.01874064 0.26990933 0.80284487 0.65068196 0.19488363 [877] 0.56872992 0.40501858 0.71148746 0.13594418 0.82814966 0.48480794 [883] 0.99186476 0.99998754 0.90703393 0.79198573 0.99998754 0.99998754 [889] 0.57459916 0.99998754 0.83066050 0.28060176 0.06384990 0.70101815 [895] 0.65040854 0.78302714 0.66925441 0.83804161 0.02089051 0.01874064 [901] 0.83825450 0.99998754 0.82814966 0.83804161 0.83825450 0.49079723 [907] 0.99998754 0.96368310 0.01874064 0.09911584 0.88917389 0.96368310 [913] 0.99998754 0.79396819 0.94894098 0.48480794 0.79198573 0.88432251 [919] 0.95926113 0.99998754 0.95926113 0.99186476 0.87360722 0.78960168 [925] 0.60512740 0.74003698 0.84961457 0.93279982 0.60362371 0.91541903 [931] 0.99998754 0.95811412 0.80766411 0.91541903 0.99998754 0.85868136 [937] 0.83795066 0.79198573 0.74226466 0.87576929 0.73049341 0.57459916 [943] 0.11158076 0.91541903 0.94082721 0.95811412 0.92140687 0.83804161 [949] 0.99998754 0.04415956 0.11333385 0.68120367 0.79396819 0.99186476 [955] 0.97851743 0.65101217 0.99998754 0.97340674 0.99998754 0.70101815 [961] 0.22616844 0.14313662 0.79426052 0.77680114 0.94894098 0.79396819 [967] 0.80381432 0.98537100 0.99998754 0.91841930 0.66925441 0.79396819 [973] 0.79047333 0.99998754 0.11198649 0.77505976 0.04096754 0.95538883 [979] 0.99998754 0.86574943 0.87362027 0.71100646 0.83066050 0.47662079 [985] 0.99465837 0.95811412 0.79747425 0.52271749 0.05903125 0.41056319 [991] 0.28262981 0.80683093 0.59611389 0.95538883 0.91541903 0.91541903 [997] 0.70101815 0.66925441 0.68120367 0.73112758 0.99998754 0.98453875 [1003] 0.99186476 0.97618739 0.83615956 0.99998754 0.12705058 0.88737825 [1009] 0.70532560 0.65101217 0.96618291 0.04415956 0.39929055 0.99998754 [1015] 0.99998754 0.79198573 0.91841930 0.99998754 0.89247580 0.78302714 [1021] 0.56661941 0.78302714 0.99465837 0.98880162 0.71148746 0.83804161 [1027] 0.99998754 0.76089638 0.99998754 0.24991466 0.60549182 0.91541903 [1033] 0.88597213 0.88537970 0.83066050 0.99998754 0.99998754 0.99998754 [1039] 0.94634972 0.63230249 0.34646461 0.76762070 0.90287798 0.99998754 [1045] 0.70101815 0.91890498 0.99998754 0.96372425 0.99998754 0.04622448 [1051] 0.68935402 0.62147802 0.99998754 0.11313737 0.95758005 0.14313662 [1057] 0.99998754 0.60549182 0.60831972 0.09254192 0.79198573 0.65459807 [1063] 0.09911584 0.53935140 0.91541903 0.96618291 0.99998754 0.95538883 [1069] 0.99998754 0.66925441 0.67026252 0.71148746 0.70624905 0.39929055 [1075] 0.99998754 0.71100646 0.88438570 0.79688311 0.87032458 0.47966509 [1081] 0.94082721 0.76346644 0.83825450 0.65225017 0.14047342 0.91099367 [1087] 0.99998754 0.99631496 0.66925441 0.56974105 0.99998754 0.82814966 [1093] 0.77951606 0.01874064 0.98880162 0.62550291 0.99998754 0.85947396 [1099] 0.50064393 0.95624026 0.97600448 0.99998754 0.79198573 0.88438570 [1105] 0.50496227 0.79688311 0.23343069 0.96618291 0.79688311 0.94192890 [1111] 0.05400170 0.75347358 0.82814966 0.89012225 0.85947396 0.19108424 [1117] 0.83804161 0.99998754 0.98229676 0.79396819 0.09627176 0.09192033 [1123] 0.99998754 0.67106168 0.66692303 0.79999112 0.91541903 0.87362027 [1129] 0.87862019 0.96618291 0.70624905 0.76346644 0.95758005 0.82171023 [1135] 0.95811412 0.99998754 0.72378010 0.46151810 0.19721483 0.78302714 [1141] 0.21045505 0.96614165 0.65427215 0.99998754 0.71573477 0.23209209 [1147] 0.91796331 0.12326698 0.03420197 0.99998754 0.95538883 0.99186476 [1153] 0.09192033 0.02089051 0.26096854 0.50064393 0.91890498 0.99998754 [1159] 0.94894098 0.90080059 0.96368310 0.96433651 0.70101815 0.99998754 [1165] 0.99186476 0.99186476 0.56018788 0.75347358 0.35906508 0.02089051 [1171] 0.89807513 0.99998754 0.99998754 0.95926113 0.65225017 0.99998754 [1177] 0.04955303 0.76762070 0.70612525 0.79219197 0.99998754 0.99186476 [1183] 0.93279982 0.67243374 0.56018788 0.86334577 0.99998754 0.94192890 [1189] 0.71148746 0.98453875 0.99998754 0.53935140 0.83028153 0.63008628 [1195] 0.89482611 0.78302714 0.76089638 0.88537970 0.72869073 0.82171023 [1201] 0.96622883 0.19823457 0.95624026 0.81857116 0.88537970 0.83812768 [1207] 0.51987432 0.88597213 0.24130540 0.91692200 0.65988611 0.82814966 [1213] 0.12233210 0.04785973 0.75021453 0.83804161 0.98880162 0.83804161 [1219] 0.95811412 0.95538883 0.95538883 0.67727498 0.91541903 0.05456303 [1225] 0.89012225 0.71148746 0.68819744 0.86764617 0.65040854 0.91692200 [1231] 0.79396819 0.66925441 0.79396819 0.01874064 0.99186476 0.96618291 [1237] 0.70532560 0.19721483 0.70101815 0.82814966 0.66925441 0.91541903 [1243] 0.99998754 0.95538883 0.99998754 0.99998754 0.19998703 0.99186476 [1249] 0.70532560 0.23209209 0.57459916 0.99186476 0.89049484 0.76089638 [1255] 0.48480794 0.63622788 0.99998754 0.98880162 0.83804161 0.79658559 [1261] 0.99998754 0.11158076 0.97543507 0.65988611 0.99998754 0.83804161 [1267] 0.83804161 0.99998754 0.61459000 0.65225017 0.72331685 0.53308421 [1273] 0.04145927 0.57459916 0.38759236 0.84654680 0.81036934 0.94361963 [1279] 0.66925441 0.90912611 0.99998754 0.98668738 0.09911584 0.04955303 [1285] 0.90912611 0.42897218 0.89611337 0.70101815 0.99186476 0.93849348 [1291] 0.79688311 0.80081767 0.99998754 0.55839584 0.61459000 0.99998754 [1297] 0.99631496 0.71579081 0.99998754 0.96368310 0.97851743 0.99998754 [1303] 0.99998754 0.79669426 0.98880162 0.83804161 0.99998754 0.80502201 [1309] 0.99631496 0.99998754 0.83804161 0.99998754 0.51987432 0.99186476 [1315] 0.99998754 0.96618291 0.24130540 0.89807513 0.99998754 0.70101815 [1321] 0.79198573 0.99998754 0.95926113 0.70101815 0.73889617 0.63972839 [1327] 0.84247929 0.96906213 0.90080059 0.75021453 0.01903434 0.27329628 [1333] 0.80502201 0.99998754 0.95538883 0.91692200 0.08251742 0.71784004 [1339] 0.70101815 0.83804161 0.91541903 0.36909003 0.99998754 0.99998754 [1345] 0.99998754 0.22706223 0.80323837 0.80323837 0.95758005 0.89049484 [1351] 0.94314657 0.92587301 0.70101815 0.65225017 0.97851743 0.11685126 [1357] 0.67106168 0.67243374 0.09054552 0.86512076 0.09192033 0.57459916 [1363] 0.83825450 0.91890498 0.94894098 0.99998754 0.91841930 0.95758005 [1369] 0.99998754 0.81036934 0.99998754 0.57459916 0.60771590 0.04955303 [1375] 0.76089638 0.60512740 0.54185221 0.09247992 0.08838456 0.76685334 [1381] 0.95817738 0.99998754 0.91541903 0.77951606 0.01874064 0.99998754 [1387] 0.01903434 0.96368310 0.89611337 0.99631496 0.70101815 0.64213977 [1393] 0.98453875 0.56522690 0.88120116 0.74789717 0.63972839 0.65427215 [1399] 0.95538883 0.66692303 0.17303513 0.99998754 0.99998754 0.96368310 [1405] 0.73049341 0.39199030 0.70101815 0.83066050 0.83804161 0.10561552 [1411] 0.09520075 0.77436254 0.28060176 0.82171023 0.99998754 0.99186476 [1417] 0.89301816 0.99186476 0.99998754 0.99998754 0.95758005 0.88537970 [1423] 0.80403017 0.99998754 0.13639106 0.23343069 0.96368310 0.83804161 [1429] 0.77951606 0.70101815 0.98880162 0.95143291 0.66692303 0.66692303 [1435] 0.81476692 0.82941122 0.57459916 0.26703562 0.98999815 0.99998754 [1441] 0.99998754 0.99998754 0.96614165 0.06953117 $significant [1] FALSE TRUE TRUE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [133] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [277] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [349] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [361] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [373] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [469] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [553] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [661] FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [673] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [709] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [769] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [865] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [949] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [973] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1009] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1045] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1093] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE [1153] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1165] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1177] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1189] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1213] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1225] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE [1237] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1249] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1261] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1273] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE [1285] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1297] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1309] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1321] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE [1333] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1345] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1357] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1369] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE [1381] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE [1393] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1405] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1417] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1429] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE [1441] FALSE FALSE FALSE FALSE $num.significant [1] 45 $pvalue.cutoff [1] 0.001544243 > > > > cleanEx(); ..nameEx <- "ggm.estimate.pcor" > > ### * ggm.estimate.pcor > > flush(stderr()); flush(stdout()) > > ### Name: ggm.estimate.pcor > ### Title: Graphical Gaussian Models: Small Sample Estimation of Partial > ### Correlation > ### Aliases: ggm.estimate.pcor > ### Keywords: htest > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # generate random network with 40 nodes > # it contains 780=40*39/2 edges of which 5 percent (=39) are non-zero > true.pcor <- ggm.simulate.pcor(40) > > # simulate data set with 40 observations > m.sim <- ggm.simulate.data(40, true.pcor) > > # simple estimate of partial correlations > estimated.pcor <- partial.cor(m.sim) > > # comparison of estimated and true model > sum((true.pcor-estimated.pcor)^2) [1] 587.429 > > # a slightly better estimate ... > estimated.pcor.2 <- ggm.estimate.pcor(m.sim, method = c("bagged.pcor")) > sum((true.pcor-estimated.pcor.2)^2) [1] 27.78266 > > # this is even better! > estimated.pcor.3 <- ggm.estimate.pcor(m.sim, method = c("shrinkage")) Estimated shrinkage intensity lambda: 0.0555 > sum((true.pcor-estimated.pcor.3)^2) [1] 11.62967 > > > > > cleanEx(); ..nameEx <- "ggm.plot.graph" > > ### * ggm.plot.graph > > flush(stderr()); flush(stdout()) > > ### Name: ggm.plot.graph > ### Title: Graphical Gaussian Models: Plotting the Network > ### Aliases: ggm.plot.graph ggm.make.graph show.edge.weights > ### Keywords: hplot > > ### ** Examples > > # load GeneTS library > library(GeneTS) > > # generate random network with 20 nodes and 10 percent edges (=19 edges) > true.pcor <- ggm.simulate.pcor(20, 0.1) > > # convert to edge list > test.results <- ggm.test.edges(true.pcor, eta0=0.9, kappa=1000)[1:19,] > > # generate graph object > # NOTE: this requires the installation of the "graph" package > # (in the following "try" is used to avoid an error if the library is not installed) > try( gr <- ggm.make.graph( test.results, 20) ) Loading required package: cluster Loading required package: Ruuid Creating a new generic function for 'print' in 'Ruuid' > try( gr ) A graph with undirected edges Number of Nodes = 20 Number of Edges = 19 > try( show.edge.weights(gr) ) 1--13 2--15 3--4 3--17 4--16 4--18 5--6 6--9 7--16 8--17 8--11 0.78 -0.32 -0.53 0.31 -0.54 -0.02 0.76 0.53 -0.65 -0.53 -0.21 8--19 9--10 10--11 13--18 14--15 14--17 14--19 17--19 0.19 0.34 -0.69 -0.61 0.49 0.36 -0.25 -0.24