All versions of the 12-parameter model were run using 8 chains with 84,000 iterations each with a burn-in of 4000 iterations (with a thinning factor of 10, for a total of 64000 samples). The full 24-parameter model was run similarly, but for 144,000 steps.
source("R/utils.R")
source("R/mcmc.R")
source("R/functions.R")
source("R/monitornew.R")
load_pkgs()
zmargin <- theme(panel.spacing = grid::unit(0, "lines"))
theme_set(theme_bw())
library(targets)
This is the model that uses only the full/known phylogeny. (Probably irrelevant.)
tar_load(traceplot_0)
print(traceplot_0)
tar_load(ag_mcmc0)
aa <- do.call(abind, c(ag_mcmc0, list(along=3)))
aa2 <- aperm(aa,c(1,3,2), resize=TRUE)
monitor(aa2)
## Inference for the input samples (8 chains: each with iter = 8000; warmup = 0):
##
## Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
## loss.sc 4.02 4.65 5.18 4.63 0.35 1.00 10622 17905
## loss.pc 4.26 4.73 5.12 4.71 0.26 1.00 14810 22222
## loss.ag_pc0_sc0 2.53 4.15 5.59 4.11 0.93 1.00 2487 5658
## gain.sc 4.85 5.13 5.38 5.12 0.16 1.00 22389 33882
## loss.ag_pc0_sc1 2.36 4.31 6.72 4.39 1.32 1.01 1145 1616
## gain.pc 3.55 3.92 4.27 3.92 0.22 1.00 18144 30749
## loss.ag_pc1_sc0 3.82 4.58 5.20 4.55 0.42 1.00 10012 18798
## loss.ag_pc1_sc1 2.18 3.60 4.78 3.56 0.80 1.00 2438 5462
## gain.ag_pc0_sc0 1.54 2.53 3.30 2.49 0.54 1.00 6066 13184
## gain.ag_pc0_sc1 0.31 1.66 2.87 1.64 0.79 1.00 1787 2417
## gain.ag_pc1_sc0 3.53 4.25 4.84 4.23 0.40 1.00 11159 18553
## gain.ag_pc1_sc1 1.00 2.70 4.26 2.67 1.00 1.00 1590 5276
##
## 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 <= 1.01).
tar_load(mc_pairsplots_0)
knitr::include_graphics('pix/mcmc_pairs_0.png')
Contour levels are: 50%, 80% 90%, 95% (largest) highest posterior density regions.
The model that samples over the ‘tree block’ (sample of phylogeny reconstructions)
tar_load(traceplot_tb)
print(traceplot_tb)
tar_load(ag_mcmc_tb)
aa <- do.call(abind, c(ag_mcmc_tb, list(along=3)))
aa2 <- aperm(aa,c(1,3,2), resize=TRUE)
monitor(aa2)
## Inference for the input samples (8 chains: each with iter = 8000; warmup = 0):
##
## Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
## loss.sc 4.32 4.89 5.40 4.88 0.33 1.00 9389 17853
## loss.pc 4.45 4.87 5.23 4.86 0.24 1.00 15936 26910
## loss.ag_pc0_sc0 2.45 4.10 5.62 4.07 0.96 1.01 1691 3050
## gain.sc 4.98 5.24 5.48 5.23 0.15 1.00 20255 33733
## loss.ag_pc0_sc1 2.34 4.35 6.81 4.43 1.35 1.01 1147 1838
## gain.pc 3.60 3.97 4.31 3.96 0.22 1.00 17269 30483
## loss.ag_pc1_sc0 4.07 4.76 5.32 4.73 0.38 1.00 8256 14982
## loss.ag_pc1_sc1 2.25 3.69 4.92 3.65 0.81 1.00 1816 4835
## gain.ag_pc0_sc0 1.36 2.41 3.21 2.36 0.57 1.00 3634 7091
## gain.ag_pc0_sc1 0.33 1.69 2.91 1.67 0.79 1.01 2147 2583
## gain.ag_pc1_sc0 4.22 4.81 5.28 4.78 0.33 1.00 9932 11580
## gain.ag_pc1_sc1 1.03 2.76 4.38 2.74 1.01 1.00 2020 4283
##
## 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 <= 1.01).
tar_load(mc_pairsplots_tb)
knitr::include_graphics('pix/mcmc_pairs_tb.png')
tar_load(traceplot_full)
print(traceplot_full)
tar_load(ag_mcmc_full)
aa <- do.call(abind, c(ag_mcmc_full, list(along=3)))
aa2 <- aperm(aa,c(1,3,2), resize=TRUE)
monitor(aa2)
## Inference for the input samples (8 chains: each with iter = 13600; warmup = 0):
##
## Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
## ag0_pc0_loss.sc 4.00 4.63 5.17 4.61 0.36 1.00 10591 23551
## ag0_loss.pc_sc0 4.22 4.70 5.11 4.68 0.27 1.00 22411 35772
## loss.ag_pc0_sc0 2.47 4.09 5.57 4.06 0.94 1.00 2630 8505
## ag0_pc0_gain.sc 4.82 5.11 5.37 5.10 0.17 1.00 27148 43068
## ag0_loss.pc_sc1 2.42 4.44 7.05 4.55 1.39 1.01 1542 2880
## loss.ag_pc0_sc1 3.48 3.87 4.23 3.86 0.23 1.00 24062 45025
## ag0_gain.pc_sc0 3.83 4.59 5.22 4.56 0.42 1.00 10475 21721
## ag0_pc1_loss.sc 2.07 3.55 4.77 3.50 0.82 1.00 3404 7931
## loss.ag_pc1_sc0 1.36 2.43 3.25 2.38 0.58 1.00 4669 10132
## ag0_gain.pc_sc1 0.34 1.69 2.95 1.68 0.80 1.01 2310 3621
## ag0_pc1_gain.sc 3.38 4.16 4.77 4.13 0.43 1.00 10465 14993
## loss.ag_pc1_sc1 0.89 2.57 4.10 2.54 0.98 1.00 2480 7374
## gain.ag_pc0_sc0 -1.19 -0.09 0.77 -0.13 0.60 1.00 3977 7728
## ag1_pc0_loss.sc 1.96 4.26 6.56 4.26 1.40 1.01 1105 3436
## ag1_loss.pc_sc0 1.92 4.21 6.47 4.20 1.38 1.01 1439 4393
## gain.ag_pc0_sc1 2.02 4.27 6.56 4.28 1.38 1.00 878 2147
## ag1_pc0_gain.sc 1.88 4.26 6.55 4.24 1.42 1.01 1040 2628
## ag1_loss.pc_sc1 1.86 4.18 6.50 4.18 1.41 1.01 918 2721
## gain.ag_pc1_sc0 2.02 4.35 6.58 4.33 1.39 1.01 905 2427
## ag1_gain.pc_sc0 1.79 4.23 6.62 4.22 1.46 1.01 531 892
## ag1_pc1_loss.sc 1.98 4.28 6.59 4.29 1.40 1.00 1129 3046
## gain.ag_pc1_sc1 1.88 4.18 6.44 4.17 1.39 1.00 1609 4535
## ag1_gain.pc_sc1 1.95 4.27 6.65 4.29 1.43 1.01 925 1387
## ag1_pc1_gain.sc 1.92 4.21 6.52 4.21 1.40 1.01 1052 2711
##
## 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 <= 1.01).
tar_load(mc_pairsplots_full)
knitr::include_graphics('pix/mcmc_pairs_full.png')
tar_load(traceplot_tb_nogainloss)
print(traceplot_tb_nogainloss)
tar_load(ag_mcmc_tb_nogainloss)
aa <- do.call(abind, c(ag_mcmc_tb_nogainloss, list(along=3)))
aa2 <- aperm(aa,c(1,3,2), resize=TRUE)
monitor(aa2)
## Inference for the input samples (8 chains: each with iter = 8000; warmup = 0):
##
## Q5 Q50 Q95 Mean SD Rhat Bulk_ESS Tail_ESS
## loss.sc 4.19 4.88 5.44 4.86 0.38 1.00 7946 12637
## loss.pc 4.50 4.92 5.29 4.92 0.24 1.00 17867 28501
## loss.ag_pc0_sc0 1.66 3.79 5.54 3.71 1.19 1.01 1147 1783
## gain.sc 4.96 5.23 5.48 5.23 0.16 1.00 18822 21039
## loss.ag_pc0_sc1 2.09 4.94 8.01 5.00 1.79 1.02 679 1864
## gain.pc 3.50 3.90 4.26 3.89 0.23 1.00 18795 31224
## loss.ag_pc1_sc0 3.81 4.61 5.22 4.58 0.44 1.00 11963 16471
## loss.ag_pc1_sc1 1.31 3.18 4.69 3.11 1.03 1.01 1817 2968
## gain.ag_pc0_sc0 1.41 2.47 3.26 2.42 0.57 1.00 5747 8859
## gain.ag_pc0_sc1 0.17 1.69 3.01 1.65 0.87 1.01 1625 2567
## gain.ag_pc1_sc0 4.31 4.87 5.33 4.85 0.32 1.00 14195 22231
## gain.ag_pc1_sc1 1.52 3.49 5.04 3.42 1.08 1.00 1722 3046
##
## 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 <= 1.01).
tar_load(mc_pairsplots_tb_nogainloss)
knitr::include_graphics('pix/mcmc_pairs_tb_nogainloss.png')