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Type 'q()' to quit R. > cat("Testing coda") Testing coda> 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; 0.01044 secs elapsed) Iteration: 3 (30% completed; 0.01224 secs elapsed) Iteration: 4 (40% completed; 0.01368 secs elapsed) Iteration: 5 (50% completed; 0.01512 secs elapsed) Iteration: 6 (60% completed; 0.01658 secs elapsed) Iteration: 7 (70% completed; 0.01804 secs elapsed) Iteration: 8 (80% completed; 0.01951 secs elapsed) Iteration: 9 (90% completed; 0.02096 secs elapsed) Iteration: 10 (100% completed; 0.02263 secs elapsed) Chain 2 Iteration: 2 (20% completed; 0.00114 secs elapsed) Iteration: 3 (30% completed; 0.00263 secs elapsed) Iteration: 4 (40% completed; 0.00455 secs elapsed) Iteration: 5 (50% completed; 0.00622 secs elapsed) Iteration: 6 (60% completed; 0.00769 secs elapsed) Iteration: 7 (70% completed; 0.00915 secs elapsed) Iteration: 8 (80% completed; 0.0106 secs elapsed) Iteration: 9 (90% completed; 0.01203 secs elapsed) Iteration: 10 (100% completed; 0.01348 secs elapsed) > > as.mcmc.list(fitkm.list) [[1]] Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 10 Thinning interval = 1 beta lambda r1 r2 r3 r4 delta1 delta2 delta3 delta4 1 1.989818 10.000000 1 1.0000000 1.0000000 1 1 1 1 1 2 2.423417 10.000000 1 1.0000000 0.9116281 1 1 1 1 1 3 2.180548 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 4 2.063339 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 5 2.101812 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 6 2.032327 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 7 1.941872 10.000000 1 1.0000000 0.0000000 0 1 1 0 0 8 1.868823 10.000000 1 0.7045984 0.0000000 0 1 1 0 0 9 1.882501 5.968696 1 0.7045984 0.0000000 0 1 1 0 0 10 1.974664 4.971256 1 0.7045984 0.0000000 0 1 1 0 0 sigsq.eps 1 0.7657294 2 0.4441570 3 0.2399607 4 0.2508605 5 0.2332896 6 0.3715078 7 0.3239797 8 0.3500690 9 0.2833460 10 0.3690458 [[2]] Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 10 Thinning interval = 1 beta lambda r1 r2 r3 r4 delta1 delta2 delta3 1 1.989818 10.000000 1.0000000 1.0000000 1 1.0000000 1 1 1 2 2.200184 7.682091 0.9922890 1.0000000 1 1.0000000 1 1 1 3 2.289282 7.682091 0.9922890 1.0000000 1 0.8554571 1 1 1 4 2.260352 7.682091 0.9922890 0.8716274 1 0.8554571 1 1 1 5 2.075178 7.682091 0.9922890 0.0000000 1 0.8554571 1 0 1 6 2.004689 7.682091 0.9922890 0.0000000 0 0.8554571 1 0 0 7 1.854308 7.682091 0.9922890 0.0000000 0 0.0000000 1 0 0 8 1.925381 7.682091 0.9143049 0.0000000 0 0.0000000 1 0 0 9 1.934585 7.682091 0.9143049 0.0000000 0 0.0000000 1 0 0 10 1.901213 7.216728 0.9143049 0.0000000 0 0.0000000 1 0 0 delta4 sigsq.eps 1 1 0.7657294 2 1 0.3710818 3 1 0.3390803 4 1 0.5904322 5 1 0.4706879 6 1 0.6269645 7 0 0.5865246 8 0 0.7061249 9 0 0.5986214 10 0 0.7493637 attr(,"class") [1] "mcmc.list" > > as.mcmc(fitkm.list[[1]]) Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 10 Thinning interval = 1 beta lambda r1 r2 r3 r4 delta1 delta2 delta3 delta4 1 1.989818 10.000000 1 1.0000000 1.0000000 1 1 1 1 1 2 2.423417 10.000000 1 1.0000000 0.9116281 1 1 1 1 1 3 2.180548 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 4 2.063339 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 5 2.101812 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 6 2.032327 10.000000 1 1.0000000 0.9116281 0 1 1 1 0 7 1.941872 10.000000 1 1.0000000 0.0000000 0 1 1 0 0 8 1.868823 10.000000 1 0.7045984 0.0000000 0 1 1 0 0 9 1.882501 5.968696 1 0.7045984 0.0000000 0 1 1 0 0 10 1.974664 4.971256 1 0.7045984 0.0000000 0 1 1 0 0 sigsq.eps 1 0.7657294 2 0.4441570 3 0.2399607 4 0.2508605 5 0.2332896 6 0.3715078 7 0.3239797 8 0.3500690 9 0.2833460 10 0.3690458 > as.mcmc(comb_bkmrfits(fitkm.list)) Markov Chain Monte Carlo (MCMC) output: Start = 1 End = 20 Thinning interval = 1 beta lambda r1 r2 r3 r4 delta1 delta2 1 1.989818 10.000000 1.0000000 1.0000000 1.0000000 1.0000000 1 1 2 2.423417 10.000000 1.0000000 1.0000000 0.9116281 1.0000000 1 1 3 2.180548 10.000000 1.0000000 1.0000000 0.9116281 0.0000000 1 1 4 2.063339 10.000000 1.0000000 1.0000000 0.9116281 0.0000000 1 1 5 2.101812 10.000000 1.0000000 1.0000000 0.9116281 0.0000000 1 1 6 1.989818 10.000000 1.0000000 1.0000000 1.0000000 1.0000000 1 1 7 2.200184 7.682091 0.9922890 1.0000000 1.0000000 1.0000000 1 1 8 2.289282 7.682091 0.9922890 1.0000000 1.0000000 0.8554571 1 1 9 2.260352 7.682091 0.9922890 0.8716274 1.0000000 0.8554571 1 1 10 2.075178 7.682091 0.9922890 0.0000000 1.0000000 0.8554571 1 0 11 2.032327 10.000000 1.0000000 1.0000000 0.9116281 0.0000000 1 1 12 1.941872 10.000000 1.0000000 1.0000000 0.0000000 0.0000000 1 1 13 1.868823 10.000000 1.0000000 0.7045984 0.0000000 0.0000000 1 1 14 1.882501 5.968696 1.0000000 0.7045984 0.0000000 0.0000000 1 1 15 1.974664 4.971256 1.0000000 0.7045984 0.0000000 0.0000000 1 1 16 2.004689 7.682091 0.9922890 0.0000000 0.0000000 0.8554571 1 0 17 1.854308 7.682091 0.9922890 0.0000000 0.0000000 0.0000000 1 0 18 1.925381 7.682091 0.9143049 0.0000000 0.0000000 0.0000000 1 0 19 1.934585 7.682091 0.9143049 0.0000000 0.0000000 0.0000000 1 0 20 1.901213 7.216728 0.9143049 0.0000000 0.0000000 0.0000000 1 0 delta3 delta4 sigsq.eps 1 1 1 0.7657294 2 1 1 0.4441570 3 1 0 0.2399607 4 1 0 0.2508605 5 1 0 0.2332896 6 1 1 0.7657294 7 1 1 0.3710818 8 1 1 0.3390803 9 1 1 0.5904322 10 1 1 0.4706879 11 1 0 0.3715078 12 0 0 0.3239797 13 0 0 0.3500690 14 0 0 0.2833460 15 0 0 0.3690458 16 0 1 0.6269645 17 0 0 0.5865246 18 0 0 0.7061249 19 0 0 0.5986214 20 0 0 0.7493637 > > proc.time() user system elapsed 5.56 0.37 5.92