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Type 'q()' to quit R. > library(bkmrhat) Loading required package: coda Diagnostics and parallel chain functioning for Bayesian kernel machine regression > > set.seed(111) > dat <- bkmr::SimData(n = 50, M = 5, ind=1:3, Zgen="realistic") > y <- dat$y > Z <- dat$Z > X <- cbind(dat$X, rnorm(50)) > > # run 10 initial iterations for a model with only 2 exposures > Z2 = Z > kmfitbma.start <- suppressWarnings(bkmr::kmbayes(y = y, Z = Z2, X = X, iter = 10, verbose = FALSE, varsel = TRUE, est.h = TRUE)) Iteration: 2 (20% completed; 0.01047 secs elapsed) Iteration: 3 (30% completed; 0.01276 secs elapsed) Iteration: 4 (40% completed; 0.01469 secs elapsed) Iteration: 5 (50% completed; 0.01659 secs elapsed) Iteration: 6 (60% completed; 0.01846 secs elapsed) Iteration: 7 (70% completed; 0.02035 secs elapsed) Iteration: 8 (80% completed; 0.02228 secs elapsed) Iteration: 9 (90% completed; 0.02423 secs elapsed) Iteration: 10 (100% completed; 0.02618 secs elapsed) > > # run 20 additional iterations > moreiterations = suppressWarnings(kmbayes_continue(kmfitbma.start, iter=20)) Validating control.params... Validating starting.values... Iteration: 3 (14.3% completed; 0.00329 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5 2 r/delta (overall) 0.5 3 r/delta (move 1) 0.5 4 r/delta (move 2) NaN Iteration: 5 (23.8% completed; 0.01142 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5 2 r/delta (overall) 0.5 3 r/delta (move 1) 0.5 4 r/delta (move 2) NaN Iteration: 7 (33.3% completed; 0.01856 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.6666667 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.4000000 4 r/delta (move 2) 1.0000000 Iteration: 9 (42.9% completed; 0.02583 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.6250000 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.3333333 4 r/delta (move 2) 1.0000000 Iteration: 11 (52.4% completed; 0.03136 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.6 2 r/delta (overall) 0.6 3 r/delta (move 1) 0.5 4 r/delta (move 2) 1.0 Iteration: 13 (61.9% completed; 0.03686 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5833333 2 r/delta (overall) 0.5833333 3 r/delta (move 1) 0.5555556 4 r/delta (move 2) 0.6666667 Iteration: 15 (71.4% completed; 0.04241 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5714286 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.5000000 4 r/delta (move 2) 0.5000000 Iteration: 17 (81% completed; 0.048 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5625000 2 r/delta (overall) 0.5625000 3 r/delta (move 1) 0.5000000 4 r/delta (move 2) 0.6666667 Iteration: 19 (90.5% completed; 0.05436 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5000000 2 r/delta (overall) 0.5555556 3 r/delta (move 1) 0.5454545 4 r/delta (move 2) 0.5714286 Iteration: 21 (100% completed; 0.06155 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.450 2 r/delta (overall) 0.550 3 r/delta (move 1) 0.500 4 r/delta (move 2) 0.625 > res = kmbayes_diag(moreiterations) Single chain Inference for the input samples (1 chains: each with iter = 30; warmup = 15): Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS h.hat1 1.9 2.1 2.4 2.1 0.2 0.95 14 15 h.hat2 2.3 2.7 2.9 2.7 0.2 0.93 16 15 h.hat3 2.3 2.5 2.8 2.5 0.2 1.11 16 15 h.hat4 2.5 2.9 3.7 3.1 0.5 0.94 16 15 h.hat5 2.0 2.1 2.5 2.2 0.2 1.14 16 15 h.hat6 1.2 1.5 1.9 1.5 0.2 1.21 7 15 h.hat7 3.3 3.6 4.1 3.7 0.3 1.00 16 15 h.hat8 3.5 3.7 4.2 3.8 0.3 1.06 16 15 h.hat9 2.5 2.9 3.4 2.9 0.3 1.00 11 15 h.hat10 2.5 2.8 3.1 2.8 0.2 0.95 16 15 h.hat11 2.3 2.6 3.1 2.7 0.3 0.93 10 15 h.hat12 2.3 2.6 2.9 2.6 0.2 0.94 16 15 h.hat13 0.5 1.0 1.6 1.0 0.4 1.03 11 15 h.hat14 1.5 1.8 2.0 1.8 0.2 1.03 16 15 h.hat15 1.1 1.4 1.7 1.4 0.2 1.11 9 15 h.hat16 3.0 3.4 3.7 3.4 0.3 1.03 16 16 h.hat17 1.8 2.1 2.3 2.1 0.2 1.13 14 16 h.hat18 1.7 1.9 2.2 1.9 0.2 1.17 10 16 h.hat19 3.5 3.8 4.3 3.8 0.3 0.98 16 15 h.hat20 1.7 1.9 2.2 1.9 0.2 0.98 16 15 h.hat21 1.9 2.1 2.4 2.1 0.2 0.95 16 15 h.hat22 1.8 2.2 2.7 2.2 0.3 0.95 14 15 h.hat23 1.4 1.6 2.0 1.6 0.2 1.16 8 15 h.hat24 2.4 2.8 3.3 2.8 0.3 0.94 10 15 h.hat25 1.6 1.8 2.2 1.9 0.2 1.10 16 16 h.hat26 1.3 1.6 1.9 1.6 0.2 1.08 10 15 h.hat27 3.4 3.9 5.0 4.1 0.5 1.10 16 15 h.hat28 0.1 0.6 1.1 0.7 0.4 1.08 11 15 h.hat29 2.3 2.5 2.9 2.5 0.2 0.95 16 15 h.hat30 3.2 3.5 3.9 3.5 0.2 1.02 16 16 h.hat31 2.8 3.2 3.7 3.2 0.3 0.94 16 15 h.hat32 2.7 3.0 3.3 2.9 0.2 1.24 13 15 h.hat33 0.5 1.0 1.5 1.0 0.3 0.98 16 15 h.hat34 2.8 3.2 3.5 3.2 0.3 1.23 16 15 h.hat35 3.5 3.9 4.3 3.9 0.3 1.13 16 15 h.hat36 0.8 1.3 1.7 1.3 0.3 0.94 16 15 h.hat37 1.6 1.8 2.2 1.8 0.2 1.21 9 15 h.hat38 0.9 1.3 1.6 1.3 0.3 1.08 11 15 h.hat39 2.2 2.4 2.7 2.4 0.2 1.11 16 15 h.hat40 3.1 3.5 3.8 3.5 0.3 1.00 16 16 h.hat41 3.5 3.6 4.0 3.7 0.2 1.00 16 15 h.hat42 3.2 3.5 3.9 3.5 0.2 0.99 16 15 h.hat43 2.4 2.8 3.0 2.8 0.2 0.94 16 16 h.hat44 1.3 1.6 1.9 1.6 0.2 1.03 15 15 h.hat45 1.0 1.4 1.8 1.4 0.2 0.96 10 15 h.hat46 1.6 1.8 2.1 1.8 0.2 1.04 16 15 h.hat47 1.8 2.1 2.6 2.1 0.3 1.01 16 15 h.hat48 1.2 1.5 1.8 1.5 0.2 1.04 12 15 h.hat49 -0.2 0.5 1.0 0.5 0.4 0.95 14 15 h.hat50 0.2 0.7 1.1 0.7 0.3 1.02 14 15 beta1 1.9 2.0 2.0 1.9 0.0 1.02 11 15 beta2 -0.1 0.0 0.2 0.1 0.1 0.93 16 16 lambda 4.5 6.3 8.2 6.1 1.5 2.36 4 4 r1 0.0 0.0 0.0 0.0 0.0 1.00 16 16 r2 0.9 0.9 1.1 1.0 0.1 1.14 9 5 r3 0.0 0.0 0.0 0.0 0.0 1.00 15 15 r4 0.0 0.0 0.0 0.0 0.0 0.93 8 15 r5 0.0 0.0 0.0 0.0 0.0 1.00 15 15 sigsq.eps 0.3 0.4 0.5 0.4 0.1 1.08 16 16 For each parameter, Bulk_ESS and Tail_ESS are crude measures of effective sample size for bulk and tail quantities respectively (an ESS > 100 per chain is considered good), and Rhat is the potential scale reduction factor on rank normalized split chains (at convergence, Rhat <= 1.05). > > #bkmr::TracePlot(moreiterations, par="r", comp=5) > #bkmr::TracePlot(moreiterations, par="beta", comp=1) > #bkmr::TracePlot(moreiterations, par="h", comp=50) > > > stopifnot(kmfitbma.start$iter stopifnot(all(kmfitbma.start$sigsq.eps %in% moreiterations$sigsq.eps)) > stopifnot(all(kmfitbma.start$beta[,1] %in% moreiterations$beta[,1])) > stopifnot(all(kmfitbma.start$r[,1] %in% moreiterations$r[,1])) > stopifnot(all(kmfitbma.start$h.hat[,1] %in% moreiterations$h.hat[,1])) > stopifnot(ncol(kmfitbma.start$beta) == ncol(moreiterations$beta)) > stopifnot(ncol(kmfitbma.start$r) == ncol(moreiterations$r)) > stopifnot(ncol(kmfitbma.start$h.hat) == ncol(moreiterations$h.hat)) > > > # now in paralelel > kmfitbma.start2 <- suppressWarnings(kmbayes_parallel(nchains=2,y = y, Z = Z2, X = X, iter = 10, verbose = FALSE, varsel = TRUE, est.h = FALSE)) Chain 1 Iteration: 2 (20% completed; 0.0011 secs elapsed) Iteration: 3 (30% completed; 0.00265 secs elapsed) Iteration: 4 (40% completed; 0.00405 secs elapsed) Iteration: 5 (50% completed; 0.00545 secs elapsed) Iteration: 6 (60% completed; 0.00704 secs elapsed) Iteration: 7 (70% completed; 0.00839 secs elapsed) Iteration: 8 (80% completed; 0.0097 secs elapsed) Iteration: 9 (90% completed; 0.01098 secs elapsed) Iteration: 10 (100% completed; 0.01214 secs elapsed) Chain 2 Iteration: 2 (20% completed; 0.00099 secs elapsed) Iteration: 3 (30% completed; 0.00243 secs elapsed) Iteration: 4 (40% completed; 0.00381 secs elapsed) Iteration: 5 (50% completed; 0.00516 secs elapsed) Iteration: 6 (60% completed; 0.00654 secs elapsed) Iteration: 7 (70% completed; 0.0079 secs elapsed) Iteration: 8 (80% completed; 0.00924 secs elapsed) Iteration: 9 (90% completed; 0.0106 secs elapsed) Iteration: 10 (100% completed; 0.01196 secs elapsed) > > # run 20 additional iterations > moreiterations2 = suppressWarnings(kmbayes_parallel_continue(kmfitbma.start2, iter=20)) Chain 1 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0 2 r/delta (overall) 1 3 r/delta (move 1) 1 4 r/delta (move 2) NaN Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5000000 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r/delta (overall) 0.6666667 3 r/delta (move 1) 0.8000000 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.25 2 r/delta (overall) 0.50 3 r/delta (move 1) 0.80 4 r/delta (move 2) 0.00 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.2 2 r/delta (overall) 0.4 3 r/delta (move 1) 0.8 4 r/delta (move 2) 0.0 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.2500000 2 r/delta (overall) 0.4166667 3 r/delta (move 1) 0.7142857 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3571429 2 r/delta (overall) 0.4285714 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3125000 2 r/delta (overall) 0.3750000 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r/delta (overall) 0.3888889 3 r/delta (move 1) 0.7000000 4 r/delta (move 2) 0.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.35 2 r/delta (overall) 0.40 3 r/delta (move 1) 0.70 4 r/delta (move 2) 0.10 Validating control.params... Validating starting.values... Iteration: 3 (14.3% completed; 0.0021 secs elapsed) Iteration: 5 (23.8% completed; 0.00658 secs elapsed) Iteration: 7 (33.3% completed; 0.01085 secs elapsed) Iteration: 9 (42.9% completed; 0.01516 secs elapsed) Iteration: 11 (52.4% completed; 0.01948 secs elapsed) Iteration: 13 (61.9% completed; 0.02382 secs elapsed) Iteration: 15 (71.4% completed; 0.02809 secs elapsed) Iteration: 17 (81% completed; 0.03238 secs elapsed) Iteration: 19 (90.5% completed; 0.03668 secs elapsed) Iteration: 21 (100% completed; 0.04097 secs elapsed) Chain 2 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5 2 r/delta (overall) 1.0 3 r/delta (move 1) 1.0 4 r/delta (move 2) 1.0 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.25 2 r/delta (overall) 1.00 3 r/delta (move 1) 1.00 4 r/delta (move 2) 1.00 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r/delta (overall) 1.0000000 3 r/delta (move 1) 1.0000000 4 r/delta (move 2) 1.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.375 2 r/delta (overall) 0.875 3 r/delta (move 1) 0.800 4 r/delta (move 2) 1.000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.4000000 2 r/delta (overall) 0.9000000 3 r/delta (move 1) 0.8571429 4 r/delta (move 2) 1.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r/delta (overall) 0.9166667 3 r/delta (move 1) 0.8888889 4 r/delta (move 2) 1.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.2857143 2 r/delta (overall) 0.9285714 3 r/delta (move 1) 0.9000000 4 r/delta (move 2) 1.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3125000 2 r/delta (overall) 0.9375000 3 r/delta (move 1) 0.9090909 4 r/delta (move 2) 1.0000000 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r/delta (overall) 0.8888889 3 r/delta (move 1) 0.9166667 4 r/delta (move 2) 0.8333333 Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3500000 2 r/delta (overall) 0.8500000 3 r/delta (move 1) 0.9230769 4 r/delta (move 2) 0.7142857 Validating control.params... Validating starting.values... Iteration: 3 (14.3% completed; 0.00219 secs elapsed) Iteration: 5 (23.8% completed; 0.00647 secs elapsed) Iteration: 7 (33.3% completed; 0.0107 secs elapsed) Iteration: 9 (42.9% completed; 0.01492 secs elapsed) Iteration: 11 (52.4% completed; 0.01917 secs elapsed) Iteration: 13 (61.9% completed; 0.02342 secs elapsed) Iteration: 15 (71.4% completed; 0.02762 secs elapsed) Iteration: 17 (81% completed; 0.03182 secs elapsed) Iteration: 19 (90.5% completed; 0.03613 secs elapsed) Iteration: 21 (100% completed; 0.04038 secs elapsed) > res2 = kmbayes_diag(moreiterations2) Parallel chains Inference for the input samples (2 chains: each with iter = 30; warmup = 15): Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS beta1 1.9 2.0 2.0 2.0 0.1 1.03 23 14 beta2 -0.1 0.1 0.2 0.1 0.1 1.06 23 41 lambda 5.3 8.2 13.3 9.2 3.2 1.10 15 13 r1 0.0 0.0 0.0 0.0 0.0 4.23 10 30 r2 0.0 0.0 1.2 0.1 0.4 1.68 9 30 r3 0.0 0.0 1.0 0.3 0.5 2.31 8 30 r4 0.0 0.0 0.0 0.0 0.0 2.44 10 30 r5 0.0 0.0 0.0 0.0 0.1 1.29 13 11 sigsq.eps 0.3 0.4 0.5 0.4 0.1 1.35 13 28 For each parameter, Bulk_ESS and Tail_ESS are crude measures of effective sample size for bulk and tail quantities respectively (an ESS > 100 per chain is considered good), and Rhat is the potential scale reduction factor on rank normalized split chains (at convergence, Rhat <= 1.05). > > > stopifnot(kmfitbma.start2[[1]]$iter < moreiterations2[[1]]$iter) > stopifnot(all(kmfitbma.start2[[1]]$sigsq.eps %in% moreiterations2[[1]]$sigsq.eps)) > stopifnot(all(kmfitbma.start2[[1]]$beta[,1] %in% moreiterations2[[1]]$beta[,1])) > stopifnot(all(kmfitbma.start2[[1]]$r[,1] %in% moreiterations2[[1]]$r[,1])) > stopifnot(all(kmfitbma.start2[[1]]$h.hat[,1] %in% moreiterations2[[1]]$h.hat[,1])) > stopifnot(ncol(kmfitbma.start2[[1]]$beta) == ncol(moreiterations2[[1]]$beta)) > stopifnot(ncol(kmfitbma.start2[[1]]$r) == ncol(moreiterations2[[1]]$r)) > stopifnot(ncol(kmfitbma.start2[[1]]$h.hat) == ncol(moreiterations2[[1]]$h.hat)) > > > # just see if it will work with probit model > y <- 1.0*(dat$y>median(dat$y)) > fitty1 = suppressWarnings(bkmr::kmbayes(y=y,Z=Z,X=X, est.h=TRUE, iter=5, family="binomial")) Fitting probit regression model Iteration: 2 (40% completed; 0.00352 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0 2 r1 0 3 r2 1 4 r3 1 5 r4 0 6 r5 0 Iteration: 3 (60% completed; 0.00856 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5 2 r1 0.5 3 r2 0.5 4 r3 0.5 5 r4 0.5 6 r5 0.0 Iteration: 4 (80% completed; 0.01361 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r1 0.6666667 3 r2 0.3333333 4 r3 0.3333333 5 r4 0.6666667 6 r5 0.3333333 Iteration: 5 (100% completed; 0.01872 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.50 2 r1 0.75 3 r2 0.50 4 r3 0.50 5 r4 0.75 6 r5 0.50 > # do some diagnostics here to see if 1000 iterations (default) is enough > # add 3000 additional iterations > fitty2 = suppressWarnings(kmbayes_continue(fitty1, iter=5)) Fitting probit regression model Validating control.params... Validating starting.values... Iteration: 2 (33.3% completed; 0.00356 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0 2 r1 1 3 r2 1 4 r3 1 5 r4 0 6 r5 0 Iteration: 3 (50% completed; 0.00893 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.5 2 r1 1.0 3 r2 1.0 4 r3 1.0 5 r4 0.5 6 r5 0.5 Iteration: 4 (66.7% completed; 0.01409 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.3333333 2 r1 1.0000000 3 r2 1.0000000 4 r3 0.6666667 5 r4 0.6666667 6 r5 0.3333333 Iteration: 5 (83.3% completed; 0.01921 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.25 2 r1 1.00 3 r2 1.00 4 r3 0.75 5 r4 0.50 6 r5 0.50 Iteration: 6 (100% completed; 0.02431 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.2 2 r1 1.0 3 r2 0.8 4 r3 0.8 5 r4 0.6 6 r5 0.6 > stopifnot(ncol(fitty1$ystar[,1]) %in% ncol(fitty2$ystar[,1])) > > > > # force old version > kmfitbma.start2 = kmfitbma.start > kmfitbma.start2$delta = kmfitbma.start2$delta*0 > moreiterations = suppressWarnings(kmbayes_continue(kmfitbma.start2, iter=20)) Validating control.params... Validating starting.values... Iteration: 3 (14.3% completed; 0.0082 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 1.0 2 r/delta (overall) 0.5 3 r/delta (move 1) 1.0 4 r/delta (move 2) 0.0 Iteration: 5 (23.8% completed; 0.01347 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 1.00 2 r/delta (overall) 0.75 3 r/delta (move 1) 1.00 4 r/delta (move 2) 0.00 Iteration: 7 (33.3% completed; 0.01868 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.8333333 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.6000000 4 r/delta (move 2) 0.0000000 Iteration: 9 (42.9% completed; 0.02411 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.7500000 2 r/delta (overall) 0.5000000 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.0000000 Iteration: 11 (52.4% completed; 0.02937 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.7000000 2 r/delta (overall) 0.4000000 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.0000000 Iteration: 13 (61.9% completed; 0.03477 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.6666667 2 r/delta (overall) 0.4166667 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.1666667 Iteration: 15 (71.4% completed; 0.04002 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.7142857 2 r/delta (overall) 0.3571429 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.1250000 Iteration: 17 (81% completed; 0.04526 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.6875000 2 r/delta (overall) 0.3125000 3 r/delta (move 1) 0.6666667 4 r/delta (move 2) 0.1000000 Iteration: 19 (90.5% completed; 0.05045 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.7222222 2 r/delta (overall) 0.2777778 3 r/delta (move 1) 0.5000000 4 r/delta (move 2) 0.1000000 Iteration: 21 (100% completed; 0.05569 secs elapsed) Acceptance rates for Metropolis-Hastings algorithm: param rate 1 lambda 0.65000000 2 r/delta (overall) 0.25000000 3 r/delta (move 1) 0.44444444 4 r/delta (move 2) 0.09090909 > res = kmbayes_diag(moreiterations) Single chain Inference for the input samples (1 chains: each with iter = 30; warmup = 15): Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS h.hat1 2.0 2.1 2.4 2.2 0.2 0.96 16 16 h.hat2 1.9 2.1 2.5 2.2 0.2 0.95 16 16 h.hat3 2.1 2.3 2.5 2.3 0.1 1.02 16 15 h.hat4 3.2 3.9 4.3 3.9 0.4 1.38 6 15 h.hat5 1.9 2.2 2.5 2.2 0.2 0.98 16 15 h.hat6 1.8 2.1 2.4 2.1 0.2 1.03 16 15 h.hat7 2.8 3.3 3.7 3.3 0.3 0.98 16 15 h.hat8 2.6 3.0 3.5 3.0 0.3 0.95 16 15 h.hat9 2.6 2.9 3.2 2.9 0.2 0.94 16 16 h.hat10 2.7 2.9 3.0 2.9 0.1 1.19 9 15 h.hat11 2.5 2.6 3.0 2.7 0.2 1.04 16 16 h.hat12 2.0 2.2 2.4 2.2 0.2 0.93 16 15 h.hat13 0.5 0.7 1.0 0.7 0.2 0.96 16 16 h.hat14 1.5 1.8 1.9 1.7 0.2 0.99 16 15 h.hat15 1.1 1.3 1.6 1.4 0.2 0.97 16 16 h.hat16 3.2 3.6 3.8 3.5 0.2 0.93 16 16 h.hat17 2.2 2.3 2.5 2.3 0.1 0.96 16 15 h.hat18 2.3 2.6 2.9 2.6 0.2 0.93 16 15 h.hat19 3.4 3.5 3.8 3.6 0.1 1.04 11 15 h.hat20 1.4 1.7 1.9 1.7 0.2 1.00 16 15 h.hat21 1.7 2.0 2.2 2.0 0.2 1.02 16 15 h.hat22 1.9 2.3 2.4 2.2 0.2 1.17 16 16 h.hat23 1.9 2.1 2.4 2.1 0.2 0.98 16 15 h.hat24 2.6 2.8 3.0 2.8 0.2 1.01 16 16 h.hat25 1.7 2.1 2.4 2.0 0.3 0.93 16 16 h.hat26 1.4 1.6 1.7 1.6 0.1 0.97 16 16 h.hat27 3.6 3.9 4.2 3.9 0.2 1.04 16 15 h.hat28 0.1 0.5 0.9 0.5 0.3 1.05 16 15 h.hat29 2.0 2.3 2.5 2.3 0.2 1.07 16 15 h.hat30 3.1 3.3 3.5 3.3 0.2 0.99 14 15 h.hat31 3.4 3.9 4.6 3.9 0.4 1.23 10 16 h.hat32 2.9 3.2 3.4 3.2 0.2 1.08 13 16 h.hat33 0.5 0.7 1.0 0.8 0.2 0.97 16 16 h.hat34 3.0 3.4 3.7 3.3 0.2 0.94 16 16 h.hat35 3.3 3.8 4.2 3.8 0.4 0.95 16 16 h.hat36 0.8 1.1 1.3 1.1 0.2 0.98 16 16 h.hat37 2.0 2.4 2.6 2.3 0.2 1.03 12 16 h.hat38 1.4 1.8 2.2 1.8 0.3 0.95 16 15 h.hat39 2.3 2.5 2.9 2.5 0.2 0.93 16 16 h.hat40 3.0 3.3 3.6 3.3 0.2 1.21 16 15 h.hat41 2.6 2.8 3.5 3.0 0.3 0.93 16 15 h.hat42 3.2 3.3 3.5 3.3 0.1 0.96 15 15 h.hat43 2.8 3.4 3.6 3.3 0.3 1.19 16 16 h.hat44 1.5 1.7 1.8 1.7 0.1 0.95 16 16 h.hat45 1.2 1.5 1.8 1.5 0.2 0.93 16 16 h.hat46 2.1 2.4 2.8 2.4 0.2 0.99 16 15 h.hat47 1.5 1.9 2.2 1.9 0.2 1.46 16 16 h.hat48 1.5 1.8 2.0 1.8 0.2 1.02 16 16 h.hat49 -0.1 0.5 0.7 0.4 0.3 1.17 9 15 h.hat50 0.2 0.7 1.0 0.6 0.3 1.04 14 15 beta1 1.9 2.0 2.0 2.0 0.0 1.11 16 15 beta2 0.0 0.1 0.3 0.1 0.1 1.13 16 16 lambda 9.9 11.1 23.5 14.0 5.0 1.36 5 15 r1 0.0 0.0 0.0 0.0 0.0 1.00 15 15 r2 0.0 0.0 0.0 0.0 0.0 2.45 4 15 r3 0.0 0.0 0.0 0.0 0.0 1.00 15 15 r4 0.0 0.0 0.0 0.0 0.0 1.00 15 15 r5 0.0 0.0 0.0 0.0 0.0 1.00 15 15 sigsq.eps 0.2 0.4 0.5 0.4 0.1 0.93 16 15 For each parameter, Bulk_ESS and Tail_ESS are crude measures of effective sample size for bulk and tail quantities respectively (an ESS > 100 per chain is considered good), and Rhat is the potential scale reduction factor on rank normalized split chains (at convergence, Rhat <= 1.05). > > proc.time() user system elapsed 6.37 0.48 6.85