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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; 0.00913 secs elapsed) Iteration: 3 (30% completed; 0.01505 secs elapsed) Iteration: 4 (40% completed; 0.03034 secs elapsed) Iteration: 5 (50% completed; 0.03689 secs elapsed) Iteration: 6 (60% completed; 0.04264 secs elapsed) Iteration: 7 (70% completed; 0.04834 secs elapsed) Iteration: 8 (80% completed; 0.05427 secs elapsed) Iteration: 9 (90% completed; 0.06819 secs elapsed) Iteration: 10 (100% completed; 0.0731 secs elapsed) Chain 2 Fitting probit regression model Iteration: 2 (20% completed; 0.0047 secs elapsed) Iteration: 3 (30% completed; 0.00957 secs elapsed) Iteration: 4 (40% completed; 0.01453 secs elapsed) Iteration: 5 (50% completed; 0.01942 secs elapsed) Iteration: 6 (60% completed; 0.02434 secs elapsed) Iteration: 7 (70% completed; 0.02943 secs elapsed) Iteration: 8 (80% completed; 0.03453 secs elapsed) Iteration: 9 (90% completed; 0.04022 secs elapsed) Iteration: 10 (100% completed; 0.0505 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 6.15 0.62 6.76