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Type 'q()' to quit R. > cat("Testing diag") Testing diag> 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 = 4) > y <- dat$y > Z <- dat$Z > X <- dat$X > set.seed(111) > > future::plan(strategy = future::sequential) > fitkm.list <- kmbayes_parallel(nchains=2, y = y, Z = Z, X = X, iter = 10, + verbose = FALSE, varsel = TRUE) Chain 1 Iteration: 2 (20% completed; 0.00981 secs elapsed) Iteration: 3 (30% completed; 0.01157 secs elapsed) Iteration: 4 (40% completed; 0.01297 secs elapsed) Iteration: 5 (50% completed; 0.01447 secs elapsed) Iteration: 6 (60% completed; 0.01591 secs elapsed) Iteration: 7 (70% completed; 0.01739 secs elapsed) Iteration: 8 (80% completed; 0.01886 secs elapsed) Iteration: 9 (90% completed; 0.02032 secs elapsed) Iteration: 10 (100% completed; 0.02178 secs elapsed) Chain 2 Iteration: 2 (20% completed; 0.00116 secs elapsed) Iteration: 3 (30% completed; 0.00266 secs elapsed) Iteration: 4 (40% completed; 0.00413 secs elapsed) Iteration: 5 (50% completed; 0.0056 secs elapsed) Iteration: 6 (60% completed; 0.00705 secs elapsed) Iteration: 7 (70% completed; 0.00849 secs elapsed) Iteration: 8 (80% completed; 0.00993 secs elapsed) Iteration: 9 (90% completed; 0.01136 secs elapsed) Iteration: 10 (100% completed; 0.01281 secs elapsed) > > kmbayes_diagnose(fitkm.list) Parallel chains Inference for the input samples (2 chains: each with iter = 10; warmup = 5): Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS beta 1.9 1.9 2.0 1.9 0.1 1.04 10 10 lambda 5.4 7.7 10.0 7.9 1.7 3.09 10 10 r1 0.9 1.0 1.0 1.0 0.0 1.00 10 10 r2 0.0 0.4 1.0 0.4 0.4 1.00 10 10 r3 0.0 0.0 0.5 0.1 0.3 1.00 10 10 r4 0.0 0.0 0.5 0.1 0.3 1.00 10 10 sigsq.eps 0.3 0.5 0.7 0.5 0.2 1.62 10 10 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). mean se_mean sd 2.5% 25% 50% 75% beta 1.93203617 0 0.05844528 1.8575739 1.8871790 1.9299827 1.9664661 lambda 7.88850451 0 1.70596830 5.1956802 7.3330689 7.6820911 9.4205228 r1 0.97274927 0 0.04044727 0.9143049 0.9338009 0.9961445 1.0000000 r2 0.41137951 0 0.44684643 0.0000000 0.0000000 0.3522992 0.7045984 r3 0.09116281 0 0.28828213 0.0000000 0.0000000 0.0000000 0.0000000 r4 0.08554571 0 0.27051928 0.0000000 0.0000000 0.0000000 0.0000000 sigsq.eps 0.49655473 0 0.17382763 0.2924886 0.3548132 0.4790162 0.6198787 97.5% n_eff Rhat valid Q5 Q50 Q95 beta 2.0261081 5 1.044932 1 1.8608397 1.9299827 2.0198896 lambda 10.0000000 5 3.091743 1 5.4201042 7.6820911 10.0000000 r1 1.0000000 5 1.000000 1 0.9143049 0.9961445 1.0000000 r2 1.0000000 5 1.000000 1 0.0000000 0.3522992 1.0000000 r3 0.7065118 5 1.000000 1 0.0000000 0.0000000 0.5013955 r4 0.6629792 5 1.000000 1 0.0000000 0.0000000 0.4705014 sigsq.eps 0.7396350 5 1.620442 1 0.3016312 0.4790162 0.7299063 MCSE_Q2.5 MCSE_Q25 MCSE_Q50 MCSE_Q75 MCSE_Q97.5 MCSE_SD Bulk_ESS beta NA NA NA NA NA NA 10 lambda NA NA NA NA NA NA 10 r1 NA NA NA NA NA NA 10 r2 NA NA NA NA NA NA 10 r3 NA NA NA NA NA NA 10 r4 NA NA NA NA NA NA 10 sigsq.eps NA NA NA NA NA NA 10 Tail_ESS beta 10 lambda 10 r1 10 r2 10 r3 10 r4 10 sigsq.eps 10 > combobj = comb_bkmrfits(fitkm.list) > stopifnot(inherits(combobj, "bkmrfit")) > kmbayes_diagnose(combobj) Single chain Inference for the input samples (1 chains: each with iter = 20; warmup = 10): Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS beta 1.9 1.9 2.0 1.9 0.1 0.97 5 5 lambda 5.4 7.7 10.0 7.9 1.7 2.46 5 10 r1 0.9 1.0 1.0 1.0 0.0 3.38 5 10 r2 0.0 0.4 1.0 0.4 0.4 3.02 5 10 r3 0.0 0.0 0.5 0.1 0.3 1.00 5 5 r4 0.0 0.0 0.5 0.1 0.3 1.00 5 5 sigsq.eps 0.3 0.5 0.7 0.5 0.2 2.05 5 5 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). mean se_mean sd 2.5% 25% 50% beta 1.93203617 0.02613752 0.05844528 1.8575739 1.8871790 1.9299827 lambda 7.88850451 0.76293222 1.70596830 5.1956802 7.3330689 7.6820911 r1 0.97274927 0.01808857 0.04044727 0.9143049 0.9338009 0.9961445 r2 0.41137951 0.19983580 0.44684643 0.0000000 0.0000000 0.3522992 r3 0.09116281 0.12892369 0.28828213 0.0000000 0.0000000 0.0000000 r4 0.08554571 0.12097990 0.27051928 0.0000000 0.0000000 0.0000000 sigsq.eps 0.49655473 0.07773808 0.17382763 0.2924886 0.3548132 0.4790162 75% 97.5% n_eff Rhat valid Q5 Q50 beta 1.9664661 2.0261081 10 0.972567 1 1.8608397 1.9299827 lambda 9.4205228 10.0000000 6 2.460039 1 5.4201042 7.6820911 r1 1.0000000 1.0000000 4 3.379189 1 0.9143049 0.9961445 r2 0.7045984 1.0000000 4 3.024547 1 0.0000000 0.3522992 r3 0.0000000 0.7065118 10 1.000000 1 0.0000000 0.0000000 r4 0.0000000 0.6629792 10 1.000000 1 0.0000000 0.0000000 sigsq.eps 0.6198787 0.7396350 4 2.048600 1 0.3016312 0.4790162 Q95 MCSE_Q2.5 MCSE_Q25 MCSE_Q50 MCSE_Q75 MCSE_Q97.5 beta 2.0198896 0.01409649 0.03553638 0.02968535 0.03965407 0.04522741 lambda 10.0000000 1.12273599 1.35541747 0.23268148 1.15895443 NA r1 1.0000000 0.00000000 0.03899202 0.04284754 NA NA r2 1.0000000 0.00000000 0.00000000 0.35229918 0.50000000 NA r3 0.5013955 0.00000000 0.00000000 0.00000000 0.00000000 0.45581407 r4 0.4705014 0.00000000 0.00000000 0.00000000 0.00000000 0.42772854 sigsq.eps 0.7299063 0.03336147 0.04408088 0.12427623 0.16730857 0.07537116 MCSE_SD Bulk_ESS Tail_ESS beta 0.01968211 5 5 lambda 0.57450427 5 10 r1 0.01362108 5 10 r2 0.15048063 5 10 r3 0.09708229 5 5 r4 0.09110045 5 5 sigsq.eps 0.05853843 5 5 > closeAllConnections() > > proc.time() user system elapsed 5.50 0.65 6.10