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Type 'q()' to quit R. > require(confintROB) Loading required package: confintROB > require(lme4) Loading required package: lme4 Loading required package: Matrix > require(robustvarComp) Loading required package: robustvarComp > > test.varComprob <- + function(object, data = sleepstudy) { + cat("Running test for object of class ", class(object), "\n") + sample <- confintROB:::createParamSampleFunction(model = object, + data = data) + set.seed(123) + result11 <- c(sample(1), sample(1)) + set.seed(123) + result2 <- sample(2) + names(result11) <- names(result2) + stopifnot(all.equal(result11, result2)) + return(result2) + } > > participant <- sleepstudy$Subject > within <- sleepstudy$Days > > # Build the argument "groups" of the varComprob() function > n <- length(unique(participant)) # the number of participants > J <- + length(unique(within)) # the number of repeated observations per participant > groups <- + cbind(rep(1:J, each = n), rep((1:n), J)) # a numeric matrix with two columns used to group the observations according to participant. > > # Build the argument "varcov" of the varComprob() function > z1 <- + rep(1, J) #Value for intercept (=1) for the J observations by clusters > z2 <- unique(within) # Value for the time variable > > K <- + list( + # Matrix for intercept + sigma2_u0 = tcrossprod(z1, z1), + # Matrix of interaction Intercept by time variable + Covariance = tcrossprod(z1, z2) + tcrossprod(z2, z1), + # Matrix for time variable + sigma2_u1 = tcrossprod(z2, z2) + ) > > # Estimation with S-estimator > suppressWarnings( + model.S <- + varComprob( + Reaction ~ 1 + Days, + groups = groups, + data = sleepstudy, + varcov = K, + control = varComprob.control( + lower = c(0, -Inf, 0), + method = "S", + psi = "rocke", + max.it = 1, + init = list( + beta = c("(Intercept)" = 253.835569743834, Days = 10.7736608268214), + gamma = c( + sigma2_u0 = 1.59549700005736, + Covariance = -0.0711447985744645, + sigma2_u1 = 0.0765023178239254 + ), + eta0 = c("error variance" = 692.556625895202), + scale = 10752.1432565101 + ) + ) + ) + ) > print(summary(model.S), digits = 2) Method: S Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 253.8 6.6 38.2 <2e-16 *** Days 10.8 1.6 6.9 7e-12 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Random effect variances: sigma2_u0 Covariance sigma2_u1 1104.97202 -49.27180 52.98219 Residual variance: error variance 692.5566 Value of the objective function at convergence: [1] 10752.14 Robustness weights: 6 observations c(1,6,7,11,15,18) are outliers with |weight| = 0 ( < 0.005556); The remaining 12 ones are summarized as Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3215799 0.4935559 0.9459881 0.8379223 1.0917200 1.2086610 > result <- test.varComprob(model.S) Running test for object of class varComprob.S varComprob.fit varComprob > print(head(result[[1]]), digits = 5) 1 2 3 4 5 6 251.71 322.29 206.80 325.15 274.34 329.96 > print(head(result[[2]]), digits = 5) 1 2 3 4 5 6 220.15 261.71 366.48 295.06 290.76 366.08 > > # Estimation with composite-TAU estimator > control <- varComprob.control( + lower = c(0, -Inf, 0), + max.it = 1, + init = list( + beta = c("(Intercept)" = 250.945321738908, Days = 10.2320816031076), + gamma = c( + sigma2_u0 = 2.17362686604633, + Covariance = -0.0704396118106077, + sigma2_u1 = 0.132062984417908 + ), + eta0 = c("error variance" = 376.800691794604), + scales = c( + 293.57715136143, + 358.95262673052, + 465.547583256656, + 561.3346991483, + 692.21765047862, + 932.623947285384, + 641.528419359161, + 846.716921562313, + 924.543567137878, + 365.994312558323, + 481.953914967322, + 585.564052671342, + 697.829285167833, + 1009.71707572247, + 672.461886751178, + 982.606142686251, + 936.132126983003, + 248.037407578449, + 374.605889784185, + 536.450389280523, + 854.773265534817, + 632.866330961722, + 855.224511580672, + 962.333779104256, + 391.221328441633, + 629.884894368671, + 834.926952170133, + 882.869865599689, + 1022.24447287146, + 1168.56340641807, + 575.172734225926, + 715.931584462354, + 671.517853836347, + 949.863650035998, + 1052.4253043978, + 760.626391277738, + 523.076365944673, + 681.762701791185, + 943.357505068095, + 914.246654077684, + 856.56616457374, + 1309.32923881337, + 717.252457844454, + 685.620374481247, + 781.788840625603 + ) + ) + ) > > suppressWarnings( + model.cTAU <- varComprob( + Reaction ~ 1 + Days, + groups = groups, + data = sleepstudy, + varcov = K, + control = control + ) + ) > print(summary(model.cTAU), digits = 2) Method: compositeTau Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 250.9 7.8 32.2 <2e-16 *** Days 10.2 1.5 6.7 3e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Random effect variances: sigma2_u0 Covariance sigma2_u1 819.02411 -26.54169 49.76142 Residual variance: error variance 376.8007 Value of the objective function at convergence: [1] 7675.007 > result_original <- test.varComprob(model.cTAU) Running test for object of class varComprob.compositeTau varComprob.fit varComprob > print(head(result_original[[1]]), digits = 5) 1 2 3 4 5 6 251.66 308.23 213.82 315.15 274.40 321.11 > print(head(result_original[[2]]), digits = 5) 1 2 3 4 5 6 225.00 257.90 344.42 289.68 289.04 346.64 > > # the same using a permuted dataset > set.seed(1) > permutation <- sample.int(nrow(sleepstudy)) > print(head(permutation)) [1] 68 167 129 162 43 14 > groups_permuted <- groups[permutation, ] > data_permuted <- sleepstudy[permutation, ] > > suppressWarnings( + model.cTAU_permuted <- varComprob( + Reaction ~ 1 + Days, + groups = groups_permuted, + data = data_permuted, + varcov = K, + control = control + ) + ) > print(summary(model.cTAU_permuted), digits = 2) Method: compositeTau Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 250.9 7.8 32.2 <2e-16 *** Days 10.2 1.5 6.7 3e-11 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Random effect variances: sigma2_u0 Covariance sigma2_u1 819.02411 -26.54169 49.76142 Residual variance: error variance 376.8007 Value of the objective function at convergence: [1] 7675.007 > result_permuted <- test.varComprob(model.cTAU_permuted, data = data_permuted) Running test for object of class varComprob.compositeTau varComprob.fit varComprob > print(head(result_permuted[[1]]), digits = 5) 68 167 129 162 43 14 315.19 319.75 307.52 269.58 272.30 318.29 > print(head(result_permuted[[2]]), digits = 5) 68 167 129 162 43 14 323.84 268.18 335.08 214.43 293.81 309.89 > result_expected <- lapply(result_original, `[`, permutation) > stopifnot(all.equal(result_expected, result_permuted)) > > proc.time() user system elapsed 9.46 0.68 10.14