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Type 'q()' to quit R. > cat("Testing parallel") Testing parallel> library(bkmrhat) Loading required package: coda Diagnostics and parallel chain functioning for Bayesian kernel machine regression > set.seed(111) > dat <- bkmr::SimData(n = 100, M = 4) > expit <- function(mu) 1/(1+exp(-mu)) > y <- rbinom(100, 1, expit(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, family="binomial") Chain 1 Fitting probit regression model Iteration: 2 (20% completed; 0.00822 secs elapsed) Iteration: 3 (30% completed; 0.01389 secs elapsed) Iteration: 4 (40% completed; 0.02761 secs elapsed) Iteration: 5 (50% completed; 0.03338 secs elapsed) Iteration: 6 (60% completed; 0.03946 secs elapsed) Iteration: 7 (70% completed; 0.04494 secs elapsed) Iteration: 8 (80% completed; 0.0504 secs elapsed) Iteration: 9 (90% completed; 0.06322 secs elapsed) Iteration: 10 (100% completed; 0.06801 secs elapsed) Chain 2 Fitting probit regression model Iteration: 2 (20% completed; 0.00451 secs elapsed) Iteration: 3 (30% completed; 0.00917 secs elapsed) Iteration: 4 (40% completed; 0.0138 secs elapsed) Iteration: 5 (50% completed; 0.01842 secs elapsed) Iteration: 6 (60% completed; 0.02347 secs elapsed) Iteration: 7 (70% completed; 0.02824 secs elapsed) Iteration: 8 (80% completed; 0.03359 secs elapsed) Iteration: 9 (90% completed; 0.03908 secs elapsed) Iteration: 10 (100% completed; 0.04886 secs elapsed) Warning messages: 1: glm.fit: fitted probabilities numerically 0 or 1 occurred 2: glm.fit: fitted probabilities numerically 0 or 1 occurred > sinkit = kmbayes_diag(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 ystar1 2.1 3.3 6.1 3.7 1.6 1.52 10 10 ystar2 0.5 3.6 7.0 3.7 2.3 1.51 10 10 ystar3 0.8 2.9 5.0 3.0 1.5 1.26 10 10 ystar4 -6.4 -2.5 -1.1 -3.2 2.0 0.92 10 10 ystar5 0.3 1.1 2.6 1.2 0.8 1.25 10 10 ystar6 1.9 5.1 8.8 5.1 2.4 1.52 10 10 ystar7 5.9 8.6 10.9 8.6 1.9 0.89 10 10 ystar8 0.4 1.6 4.5 1.9 1.6 0.99 10 10 ystar9 0.2 1.7 3.7 1.8 1.4 1.32 10 10 ystar10 2.9 6.0 9.3 6.1 2.3 1.23 10 10 ystar11 0.5 1.5 4.0 1.9 1.3 3.61 10 10 ystar12 1.7 3.4 5.2 3.5 1.2 1.38 10 10 ystar13 0.8 1.8 4.3 2.1 1.3 2.01 10 10 ystar14 0.6 2.9 6.9 3.4 2.4 1.62 10 10 ystar15 0.7 4.6 7.9 4.4 2.7 1.04 10 10 ystar16 0.3 2.1 4.3 2.2 1.4 1.83 10 10 ystar17 0.5 4.3 7.2 3.8 2.7 1.62 10 10 ystar18 4.9 7.1 11.5 7.6 2.5 0.87 10 10 ystar19 -4.9 -2.7 -1.1 -2.9 1.6 1.48 10 10 ystar20 -5.2 -3.3 -2.3 -3.4 1.1 1.38 10 10 ystar21 1.4 3.9 5.1 3.6 1.5 1.38 10 10 ystar22 3.6 6.6 10.8 6.8 2.8 1.26 10 10 ystar23 4.0 5.4 10.2 6.3 2.4 2.90 10 10 ystar24 2.3 7.1 9.4 6.5 2.7 1.52 10 10 ystar25 3.6 5.8 7.7 5.7 1.6 1.34 10 10 ystar26 0.4 1.5 4.6 2.0 1.7 2.16 10 10 ystar27 -5.5 -4.1 -1.3 -3.7 1.7 0.96 10 10 ystar28 -10.8 -5.1 -2.8 -5.7 3.1 1.43 10 10 ystar29 -2.4 -0.7 -0.2 -1.0 0.9 1.26 10 10 ystar30 0.7 3.3 5.7 3.5 2.0 0.96 10 10 ystar31 -7.2 -4.7 -1.8 -4.6 2.1 1.83 10 10 ystar32 3.3 7.0 8.8 6.3 2.1 1.26 10 10 ystar33 0.7 2.3 3.9 2.2 1.3 1.69 10 10 ystar34 0.4 1.7 2.9 1.7 0.9 1.16 10 10 ystar35 -4.0 -1.3 -0.3 -1.7 1.4 1.32 10 10 ystar36 2.0 4.3 7.4 4.5 1.9 1.26 10 10 ystar37 2.1 3.1 4.2 3.1 0.8 1.05 10 10 ystar38 -4.9 -2.7 -0.5 -2.8 1.6 1.26 10 10 ystar39 1.1 2.5 5.6 2.8 1.6 2.90 10 10 ystar40 0.3 1.3 2.9 1.6 1.0 1.15 10 10 ystar41 -5.5 -3.2 -0.5 -3.1 1.8 1.38 10 10 ystar42 1.4 4.3 7.5 4.7 2.4 1.10 10 10 ystar43 0.2 1.1 3.5 1.5 1.4 1.32 10 10 ystar44 1.6 4.1 5.1 3.8 1.3 1.12 10 10 ystar45 1.4 3.8 6.7 3.8 2.0 3.61 10 10 ystar46 2.9 5.4 6.6 5.1 1.5 1.48 10 10 ystar47 1.5 4.0 7.2 4.1 2.1 1.69 10 10 ystar48 0.1 1.7 3.6 1.9 1.3 1.19 10 10 ystar49 -5.6 -3.1 -0.6 -3.2 2.1 1.04 10 10 ystar50 -5.6 -3.4 -1.1 -3.4 1.7 1.34 10 10 ystar51 0.9 2.2 5.0 2.6 1.5 1.56 10 10 ystar52 0.8 2.0 3.6 2.1 1.1 1.10 10 10 ystar53 -6.9 -3.7 -1.4 -4.0 2.0 1.22 10 10 ystar54 0.1 1.3 2.3 1.3 0.9 0.96 10 10 ystar55 5.2 7.8 11.5 8.1 2.4 1.62 10 10 ystar56 0.9 5.4 7.6 4.7 2.4 0.87 10 10 ystar57 -4.5 -2.3 -0.2 -2.4 1.5 2.90 10 10 ystar58 0.5 3.0 5.3 2.9 1.7 0.99 10 10 ystar59 0.2 2.3 4.7 2.3 1.9 1.69 10 10 ystar60 7.3 8.2 10.5 8.5 1.3 0.86 10 10 ystar61 -3.5 -1.2 -0.4 -1.6 1.1 1.48 10 10 ystar62 0.5 2.5 4.9 2.5 1.6 2.29 10 10 ystar63 3.4 4.1 8.4 5.1 2.1 1.69 10 10 ystar64 0.2 2.6 4.2 2.4 1.6 2.15 10 10 ystar65 0.0 1.2 3.8 1.6 1.4 1.52 10 10 ystar66 4.3 6.0 8.6 6.2 1.6 1.56 10 10 ystar67 0.6 1.7 3.9 2.0 1.2 1.21 10 10 ystar68 2.2 3.9 8.6 4.7 2.4 2.01 10 10 ystar69 1.0 2.7 4.6 2.6 1.4 0.99 10 10 ystar70 0.2 1.1 2.7 1.3 0.9 2.56 10 10 ystar71 2.7 5.1 8.1 5.2 2.0 1.52 10 10 ystar72 0.3 1.3 3.6 1.7 1.3 1.23 10 10 ystar73 0.4 2.6 5.1 2.8 1.7 1.22 10 10 ystar74 1.0 2.6 5.3 3.0 1.6 1.48 10 10 ystar75 3.2 5.3 9.7 5.9 2.3 1.52 10 10 ystar76 0.4 2.6 4.9 2.6 1.7 0.96 10 10 ystar77 1.6 4.8 7.5 4.8 2.2 2.01 10 10 ystar78 0.9 4.3 6.3 4.0 2.1 0.87 10 10 ystar79 1.4 3.4 7.1 3.9 2.1 1.62 10 10 ystar80 2.6 4.6 6.3 4.5 1.4 1.34 10 10 ystar81 1.8 3.5 6.6 3.7 1.8 0.90 10 10 ystar82 0.5 1.7 6.5 2.6 2.3 0.87 10 10 ystar83 -4.4 -1.6 -0.3 -2.2 1.6 0.96 10 10 ystar84 1.6 4.1 6.6 4.2 1.7 1.43 10 10 ystar85 3.9 6.7 10.4 6.9 2.3 0.96 10 10 ystar86 0.9 2.4 6.4 3.0 2.1 1.52 10 10 ystar87 1.3 2.5 4.3 2.7 1.1 0.90 10 10 ystar88 1.8 3.8 5.8 3.7 1.5 1.35 10 10 ystar89 0.3 2.5 6.8 2.8 2.4 1.26 10 10 ystar90 0.1 1.6 2.3 1.3 0.8 1.38 10 10 ystar91 0.2 1.4 3.2 1.4 1.2 1.75 10 10 ystar92 2.5 3.7 6.1 4.1 1.4 2.29 10 10 ystar93 -3.8 -2.1 -0.8 -2.3 1.1 1.02 10 10 ystar94 -3.0 -1.5 -0.3 -1.4 1.0 1.05 10 10 ystar95 2.9 5.9 8.3 5.9 2.1 1.32 10 10 ystar96 1.7 4.8 7.2 4.8 2.0 1.19 10 10 ystar97 2.9 4.4 6.3 4.7 1.3 1.62 10 10 ystar98 2.2 4.7 8.0 5.3 2.2 1.52 10 10 ystar99 1.1 2.7 5.4 3.0 1.7 1.10 10 10 ystar100 0.3 1.5 3.6 1.7 1.3 2.36 10 10 beta 1.1 1.2 1.6 1.3 0.2 1.30 10 10 lambda 3.4 4.7 7.4 5.0 1.8 1.00 10 10 r1 1.0 1.1 1.1 1.1 0.1 1.00 10 10 r2 1.0 1.0 1.0 1.0 0.0 2.41 10 10 r3 1.0 1.0 1.0 1.0 0.0 1.00 10 10 r4 1.0 1.0 1.0 1.0 0.0 1.00 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). > xx = comb_bkmrfits(fitkm.list) > sinkit = suppressWarnings(predict(xx)) > sinkit = suppressWarnings(predict(xx, ptype="sd.fit")) > > closeAllConnections() > > proc.time() user system elapsed 6.35 0.34 6.68