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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; 0.01063 secs elapsed) Iteration: 3 (30% completed; 0.01294 secs elapsed) Iteration: 4 (40% completed; 0.01488 secs elapsed) Iteration: 5 (50% completed; 0.01675 secs elapsed) Iteration: 6 (60% completed; 0.01864 secs elapsed) Iteration: 7 (70% completed; 0.0205 secs elapsed) Iteration: 8 (80% completed; 0.02238 secs elapsed) Iteration: 9 (90% completed; 0.02428 secs elapsed) Iteration: 10 (100% completed; 0.02625 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; 0.00336 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; 0.01156 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; 0.01885 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; 0.02609 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; 0.03178 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; 0.03735 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; 0.04295 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; 0.0486 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; 0.05503 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; 0.06224 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; 0.00146 secs elapsed) Iteration: 3 (30% completed; 0.00339 secs elapsed) Iteration: 4 (40% completed; 0.00499 secs elapsed) Iteration: 5 (50% completed; 0.00661 secs elapsed) Iteration: 6 (60% completed; 0.00819 secs elapsed) Iteration: 7 (70% completed; 0.00976 secs elapsed) Iteration: 8 (80% completed; 0.01132 secs elapsed) Iteration: 9 (90% completed; 0.01304 secs elapsed) Iteration: 10 (100% completed; 0.01451 secs elapsed) Chain 2 Iteration: 2 (20% completed; 0.0012 secs elapsed) Iteration: 3 (30% completed; 0.00281 secs elapsed) Iteration: 4 (40% completed; 0.00438 secs elapsed) Iteration: 5 (50% completed; 0.00589 secs elapsed) Iteration: 6 (60% completed; 0.00748 secs elapsed) Iteration: 7 (70% completed; 0.00904 secs elapsed) Iteration: 8 (80% completed; 0.01052 secs elapsed) Iteration: 9 (90% completed; 0.0121 secs elapsed) Iteration: 10 (100% completed; 0.01365 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; 0.00253 secs elapsed) Iteration: 5 (23.8% completed; 0.00803 secs elapsed) Iteration: 7 (33.3% completed; 0.01352 secs elapsed) Iteration: 9 (42.9% completed; 0.01837 secs elapsed) Iteration: 11 (52.4% completed; 0.02343 secs elapsed) Iteration: 13 (61.9% completed; 0.02826 secs elapsed) Iteration: 15 (71.4% completed; 0.03321 secs elapsed) Iteration: 17 (81% completed; 0.03817 secs elapsed) Iteration: 19 (90.5% completed; 0.04303 secs elapsed) Iteration: 21 (100% completed; 0.04785 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; 0.00253 secs elapsed) Iteration: 5 (23.8% completed; 0.00789 secs elapsed) Iteration: 7 (33.3% completed; 0.01313 secs elapsed) Iteration: 9 (42.9% completed; 0.01838 secs elapsed) Iteration: 11 (52.4% completed; 0.02368 secs elapsed) Iteration: 13 (61.9% completed; 0.02918 secs elapsed) Iteration: 15 (71.4% completed; 0.03445 secs elapsed) Iteration: 17 (81% completed; 0.03994 secs elapsed) Iteration: 19 (90.5% completed; 0.04534 secs elapsed) Iteration: 21 (100% completed; 0.05016 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; 0.00423 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; 0.00993 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; 0.01558 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; 0.02138 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; 0.00393 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; 0.00961 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; 0.01522 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; 0.02095 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; 0.02676 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; 0.0155 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; 0.022 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; 0.02847 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; 0.03501 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; 0.04164 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; 0.04869 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; 0.05537 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; 0.06243 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; 0.06893 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; 0.07541 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 6.89 0.43 7.31