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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; 0.00936 secs elapsed) Iteration: 3 (30% completed; 0.01544 secs elapsed) Iteration: 4 (40% completed; 0.03032 secs elapsed) Iteration: 5 (50% completed; 0.03601 secs elapsed) Iteration: 6 (60% completed; 0.04208 secs elapsed) Iteration: 7 (70% completed; 0.04779 secs elapsed) Iteration: 8 (80% completed; 0.05365 secs elapsed) Iteration: 9 (90% completed; 0.06776 secs elapsed) Iteration: 10 (100% completed; 0.07263 secs elapsed) Chain 2 Fitting probit regression model Iteration: 2 (20% completed; 0.00464 secs elapsed) Iteration: 3 (30% completed; 0.00949 secs elapsed) Iteration: 4 (40% completed; 0.01438 secs elapsed) Iteration: 5 (50% completed; 0.01933 secs elapsed) Iteration: 6 (60% completed; 0.02417 secs elapsed) Iteration: 7 (70% completed; 0.02903 secs elapsed) Iteration: 8 (80% completed; 0.03471 secs elapsed) Iteration: 9 (90% completed; 0.04051 secs elapsed) Iteration: 10 (100% completed; 0.05046 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 6.23 0.50 6.71