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Rmd | 15c31aa | Davis McCarthy | 2019-10-30 | Fixing bug in simulations analysis |
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knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
dir.create("figures/simulations", showWarnings = FALSE, recursive = TRUE)
library(ggpubr)
library(tidyverse)
library(cardelino)
library(viridis)
library(cowplot)
library(latex2exp)
library(ggrepel)
lines <- c("euts", "fawm", "feec", "fikt", "garx", "gesg", "heja", "hipn",
"ieki", "joxm", "kuco", "laey", "lexy", "naju", "nusw", "oaaz",
"oilg", "pipw", "puie", "qayj", "qolg", "qonc", "rozh", "sehl",
"ualf", "vass", "vuna", "wahn", "wetu", "xugn", "zoxy", "vils")
Define functions for simulation.
# assess cardelino with simulation
dat_dir <- "data/"
data(config_all)
data(simulation_input)
simu_input <- list("D" = D_input)
get_prob_label <- function(X){
max_idx <- rep(0, nrow(X))
for (j in seq_len(nrow(X))) {
max_idx[j] <- which.max(X[j, ])
}
max_idx
}
get_prob_value <- function(X, mode = "best") {
max_val <- rep(0, nrow(X))
for (i in seq_len(nrow(X))) {
sorted_val <- sort(X[i,], decreasing = TRUE)
if (mode == "delta") {
max_val[i] <- sorted_val[1] - sorted_val[2]
} else if (mode == "second") {
max_val[i] <- sorted_val[2]
} else {#default mode: best
max_val[i] <- sorted_val[1]
}
}
max_val
}
demuxlet <- function(A, D, Config, theta1 = 0.5, theta0 = 0.01) {
P0_mat <- dbinom(A, D, theta0, log = TRUE)
P1_mat <- dbinom(A, D, theta1, log = TRUE)
P0_mat[which(is.na(P0_mat))] <- 0
P1_mat[which(is.na(P1_mat))] <- 0
logLik_mat <- t(P0_mat) %*% (1 - Config) + t(P1_mat) %*% Config
prob_mat <- exp(logLik_mat) / rowSums(exp(logLik_mat))
prob_mat
}
simulate_joint <- function(Config_all, D_all, n_clone = 4, mut_size = 5,
missing = NULL, error_mean = c(0.01, 0.44),
error_var = c(30, 4.8), n_repeat = 1) {
simu_data_full <- list()
for (i in seq_len(n_repeat)) {
Config <- sample(Config_all[[n_clone - 2]], size = 1)[[1]]
Config <- matrix(rep(c(t(Config)), mut_size), ncol = ncol(Config),
byrow = TRUE)
row.names(Config) <- paste0("SNV", seq_len(nrow(Config)))
colnames(Config) <- paste0("Clone", seq_len(ncol(Config)))
D_input <- sample_seq_depth(D_all, n_cells = 200, missing_rate = missing,
n_sites = nrow(Config))
sim_dat <- sim_read_count(Config, D_input, Psi = NULL, cell_num = 200,
means = error_mean, vars = error_var)
sim_dat[["Config"]] <- Config
simu_data_full[[i]] <- sim_dat
}
simu_data_full
}
assign_score <- function(prob_mat, simu_mat, threshold=0.2, mode="delta") {
assign_0 <- get_prob_label(simu_mat)
assign_1 <- get_prob_label(prob_mat)
prob_val <- get_prob_value(prob_mat, mode = mode)
idx <- prob_val >= threshold
rt_list <- list("ass" = mean(idx),
"acc" = mean(assign_0 == assign_1),
"acc_ass" = mean((assign_0 == assign_1)[idx]))
rt_list
}
assign_curve <- function(prob_mat, simu_mat, mode="delta"){
assign_0 <- get_prob_label(simu_mat)
assign_1 <- get_prob_label(prob_mat)
prob_val <- get_prob_value(prob_mat, mode = mode)
thresholds <- sort(unique(prob_val))
ACC <- rep(0, length(thresholds))
ASS <- rep(0, length(thresholds))
for (i in seq_len(length(thresholds))) {
idx <- prob_val >= thresholds[i]
ASS[i] <- mean(idx)
ACC[i] <- mean((assign_0 == assign_1)[idx])
}
thresholds <- c(thresholds, 1.0)
ACC <- c(ACC, 1.0)
ASS <- c(ASS, 0.0)
AUC <- AUC_acc_ass <- 0.0
for (i in seq_len(length(thresholds) - 1)) {
AUC <- AUC + 0.5 * (thresholds[i] - thresholds[i + 1]) *
(ACC[i] + ACC[i + 1])
AUC_acc_ass <- AUC_acc_ass + 0.5 * (ASS[i] - ASS[i + 1]) *
(ACC[i] + ACC[i + 1])
}
AUC <- AUC / (thresholds[1] - thresholds[length(thresholds)])
AUC_acc_ass <- AUC_acc_ass / (ASS[1] - ASS[length(thresholds)])
rt_list <- list("ACC" = ACC, "ASS" = ASS, "AUC" = AUC,
"AUC_acc_ass" = AUC_acc_ass, "thresholds" = thresholds)
rt_list
}
assign_macro_ROC <- function(prob_mat, simu_mat) {
thresholds <- seq(0, 0.999, 0.001)
ACC <- rep(0, length(thresholds))
ASS <- rep(0, length(thresholds))
FPR <- rep(0, length(thresholds))
TPR <- rep(0, length(thresholds))
for (i in seq_len(length(thresholds))) {
idx <- prob_mat >= thresholds[i]
ASS[i] <- mean(idx) # not very meaningful
ACC[i] <- mean(simu_mat[idx])
FPR[i] <- sum(simu_mat[idx] == 0) / sum(simu_mat == 0)
TPR[i] <- sum(simu_mat[idx] == 1) / sum(simu_mat == 1)
}
AUC <- 0.0
for (i in seq_len(length(thresholds) - 1)) {
AUC <- AUC + 0.5 * (FPR[i] - FPR[i + 1]) * (TPR[i] + TPR[i + 1])
}
rt_list <- list("FPR" = FPR, "TPR" = TPR, "AUC" = AUC,
"thresholds" = thresholds, "ACC" = ACC, "ASS" = ASS)
rt_list
}
Run simulations.
set.seed(1)
ACC_all <- c()
ASS_all <- c()
AUC_all <- c()
ERR_all <- c()
labels_all <- c()
method_all <- c()
variable_all <- c()
type_use <- c("mut_size", "n_clone", "missing", "FNR", "shapes1")
value_list <- list(c(3, 5, 7, 10, 15, 25),
seq(3, 8),
seq(0.7, 0.95, 0.05),
seq(0.35, 0.6, 0.05),
c(0.5, 1.0, 2.0, 4.0, 8.0, 16.0))
for (mm in seq_len(length(value_list))) {
values_all <- value_list[[mm]]
for (k in seq_len(length(values_all))) {
if (mm == 1) {
simu_data <- simulate_joint(Config_all, simu_input$D, n_clone = 4,
mut_size = values_all[k], missing = 0.8,
error_mean = c(0.01, 0.44), n_repeat = 20)
} else if (mm == 2) {
simu_data <- simulate_joint(Config_all, simu_input$D,
n_clone = values_all[k], mut_size = 10,
missing = 0.8, error_mean = c(0.01, 0.44),
n_repeat = 20)
} else if (mm == 3) {
simu_data <- simulate_joint(Config_all, simu_input$D, n_clone = 4,
mut_size = 10, missing = values_all[k],
error_mean = c(0.01, 0.44), n_repeat = 20)
} else if (mm == 4) {
simu_data <- simulate_joint(Config_all, simu_input$D, n_clone = 4,
mut_size = 10, missing = 0.8,
error_mean = c(0.01, values_all[k]),
n_repeat = 20)
} else if (mm == 5) {
simu_data <- simulate_joint(Config_all, simu_input$D, n_clone = 4,
mut_size = 10, missing = 0.8,
error_mean = c(0.01, 0.44),
error_var = c(30, values_all[k]),
n_repeat = 20)
}
for (d_tmp in simu_data) {
prob_all <- list()
methods_use <- c("demuxlet", "Binom_EM", "Binom_Gibbs",
"Binom_gmline")
prob_all[[1]] <- demuxlet(d_tmp$A_sim, d_tmp$D_sim, d_tmp$Config,
theta0 = mean(d_tmp$theta0_binom, na.rm = TRUE))
prob_all[[2]] <- clone_id_EM(d_tmp$A_sim, d_tmp$D_sim, d_tmp$Config,
Psi = NULL, verbose = FALSE)$prob
prob_all[[3]] <- clone_id_Gibbs(d_tmp$A_sim, d_tmp$D_sim,
d_tmp$Config, Psi = NULL,
min_iter = 1000, wise = "variant",
prior1 = c(2.11, 2.69),
verbose = FALSE)$prob
for (i in seq_len(length(prob_all))) {
prob_mat <- prob_all[[i]]
assign_scr <- assign_score(prob_mat, d_tmp$I_sim,
threshold = 0.5001,
mode = "best")
ACC_all <- c(ACC_all, assign_scr$acc_ass)
ASS_all <- c(ASS_all, assign_scr$ass)
AUC_all <- c(AUC_all, assign_curve(prob_mat, d_tmp$I_sim,
mode = "best")$AUC_acc_ass)
ERR_all <- c(ERR_all, mean(abs(prob_mat - d_tmp$I_sim)))
labels_all <- c(labels_all, values_all[k])
method_all <- c(method_all, methods_use[i])
variable_all <- c(variable_all, type_use[mm])
}
}
}
}
[1] "Converged in 1000 iterations."
DIC: 913.44 D_mean: 753.35 D_post: 719.68 logLik_var: 48.44
[1] "Converged in 1000 iterations."
DIC: 655.47 D_mean: 487.89 D_post: 454.2 logLik_var: 50.32
[1] "Converged in 1000 iterations."
DIC: 356.11 D_mean: 187.82 D_post: 159.53 logLik_var: 49.15
[1] "Converged in 1000 iterations."
DIC: 814.96 D_mean: 473.63 D_post: 429.81 logLik_var: 96.29
[1] "Converged in 1000 iterations."
DIC: 673.35 D_mean: 487.88 D_post: 458.21 logLik_var: 53.78
[1] "Converged in 1000 iterations."
DIC: 491.34 D_mean: 357.15 D_post: 335.24 logLik_var: 39.02
[1] "Converged in 1000 iterations."
DIC: 506.77 D_mean: 294.82 D_post: 268.15 logLik_var: 59.66
[1] "Converged in 1000 iterations."
DIC: 441.63 D_mean: 271.02 D_post: 252.53 logLik_var: 47.27
[1] "Converged in 1000 iterations."
DIC: 855.84 D_mean: 675.46 D_post: 644.05 logLik_var: 52.95
[1] "Converged in 1000 iterations."
DIC: 383.81 D_mean: 199.85 D_post: 180.5 logLik_var: 50.83
[1] "Converged in 1000 iterations."
DIC: 421.28 D_mean: 304.42 D_post: 280.13 logLik_var: 35.29
[1] "Converged in 1000 iterations."
DIC: 544.09 D_mean: 261.49 D_post: 230.51 logLik_var: 78.39
[1] "Converged in 1000 iterations."
DIC: 414.86 D_mean: 270.46 D_post: 244.69 logLik_var: 42.54
[1] "Converged in 1000 iterations."
DIC: 653.6 D_mean: 438.09 D_post: 404.18 logLik_var: 62.36
[1] "Converged in 1000 iterations."
DIC: 404.27 D_mean: 202.13 D_post: 183.54 logLik_var: 55.18
[1] "Converged in 1000 iterations."
DIC: 654 D_mean: 406.5 D_post: 367.37 logLik_var: 71.66
[1] "Converged in 1000 iterations."
DIC: 403.04 D_mean: 315.33 D_post: 295.32 logLik_var: 26.93
[1] "Converged in 1000 iterations."
DIC: 349 D_mean: 185.9 D_post: 173.04 logLik_var: 43.99
[1] "Converged in 1000 iterations."
DIC: 158.36 D_mean: 87.76 D_post: 79.21 logLik_var: 19.79
[1] "Converged in 1000 iterations."
DIC: 851.26 D_mean: 540.68 D_post: 516.86 logLik_var: 83.6
[1] "Converged in 1000 iterations."
DIC: 516.68 D_mean: 287.05 D_post: 254.37 logLik_var: 65.58
[1] "Converged in 1000 iterations."
DIC: 633.26 D_mean: 440.23 D_post: 417.36 logLik_var: 53.97
[1] "Converged in 1000 iterations."
DIC: 1434.4 D_mean: 1199.33 D_post: 1148.76 logLik_var: 71.41
[1] "Converged in 1000 iterations."
DIC: 750.14 D_mean: 459.73 D_post: 417.18 logLik_var: 83.24
[1] "Converged in 1000 iterations."
DIC: 618.16 D_mean: 368.2 D_post: 332.35 logLik_var: 71.45
[1] "Converged in 1000 iterations."
DIC: 1338.37 D_mean: 1166.04 D_post: 1118.09 logLik_var: 55.07
[1] "Converged in 1000 iterations."
DIC: 858.12 D_mean: 707.29 D_post: 665.86 logLik_var: 48.07
[1] "Converged in 1000 iterations."
DIC: 822.37 D_mean: 630.35 D_post: 605.63 logLik_var: 54.18
[1] "Converged in 1000 iterations."
DIC: 765.54 D_mean: 471.75 D_post: 421.29 logLik_var: 86.06
[1] "Converged in 1000 iterations."
DIC: 853.66 D_mean: 611.45 D_post: 562.62 logLik_var: 72.76
[1] "Converged in 1000 iterations."
DIC: 1014.81 D_mean: 730.46 D_post: 675.58 logLik_var: 84.81
[1] "Converged in 1000 iterations."
DIC: 868.75 D_mean: 607.01 D_post: 560.32 logLik_var: 77.11
[1] "Converged in 1000 iterations."
DIC: 881.18 D_mean: 687.15 D_post: 644.22 logLik_var: 59.24
[1] "Converged in 1000 iterations."
DIC: 789.61 D_mean: 657.59 D_post: 624.09 logLik_var: 41.38
[1] "Converged in 1000 iterations."
DIC: 821.42 D_mean: 510.84 D_post: 484.79 logLik_var: 84.16
[1] "Converged in 1000 iterations."
DIC: 883.63 D_mean: 492.15 D_post: 462.38 logLik_var: 105.31
[1] "Converged in 1000 iterations."
DIC: 1011.42 D_mean: 782.03 D_post: 733.1 logLik_var: 69.58
[1] "Converged in 1000 iterations."
DIC: 924.66 D_mean: 728.64 D_post: 685.58 logLik_var: 59.77
[1] "Converged in 1000 iterations."
DIC: 694.75 D_mean: 470.44 D_post: 427.56 logLik_var: 66.8
[1] "Converged in 1000 iterations."
DIC: 595.66 D_mean: 387.95 D_post: 336.21 logLik_var: 64.86
[1] "Converged in 1000 iterations."
DIC: 1068.64 D_mean: 848.7 D_post: 806.56 logLik_var: 65.52
[1] "Converged in 1000 iterations."
DIC: 961.92 D_mean: 829.43 D_post: 786.69 logLik_var: 43.81
[1] "Converged in 1000 iterations."
DIC: 995.46 D_mean: 828.68 D_post: 783.37 logLik_var: 53.02
[1] "Converged in 1000 iterations."
DIC: 627.31 D_mean: 400.17 D_post: 359.4 logLik_var: 66.98
[1] "Converged in 1000 iterations."
DIC: 1846.08 D_mean: 1611.9 D_post: 1555.15 logLik_var: 72.73
[1] "Converged in 1000 iterations."
DIC: 1089.6 D_mean: 776.96 D_post: 730.01 logLik_var: 89.9
[1] "Converged in 1000 iterations."
DIC: 755.44 D_mean: 414.78 D_post: 357.17 logLik_var: 99.57
[1] "Converged in 1000 iterations."
DIC: 1305.62 D_mean: 1119.32 D_post: 1078.47 logLik_var: 56.79
[1] "Converged in 1000 iterations."
DIC: 1371.41 D_mean: 1181.07 D_post: 1134.68 logLik_var: 59.18
[1] "Converged in 1000 iterations."
DIC: 764.73 D_mean: 586 D_post: 553.63 logLik_var: 52.77
[1] "Converged in 1000 iterations."
DIC: 1188.78 D_mean: 964.07 D_post: 903.2 logLik_var: 71.39
[1] "Converged in 1000 iterations."
DIC: 1613.55 D_mean: 1522.35 D_post: 1480.25 logLik_var: 33.32
[1] "Converged in 1000 iterations."
DIC: 670.68 D_mean: 495.23 D_post: 455.63 logLik_var: 53.76
[1] "Converged in 1000 iterations."
DIC: 1000.66 D_mean: 674.39 D_post: 623.3 logLik_var: 94.34
[1] "Converged in 1700 iterations."
DIC: 720.74 D_mean: 589.99 D_post: 561.98 logLik_var: 39.69
[1] "Converged in 1000 iterations."
DIC: 978.08 D_mean: 795.13 D_post: 742.77 logLik_var: 58.83
[1] "Converged in 1000 iterations."
DIC: 1072.77 D_mean: 823.02 D_post: 800.54 logLik_var: 68.06
[1] "Converged in 1000 iterations."
DIC: 882.38 D_mean: 676.82 D_post: 600.38 logLik_var: 70.5
[1] "Converged in 1000 iterations."
DIC: 1208.71 D_mean: 989.73 D_post: 918.1 logLik_var: 72.65
[1] "Converged in 1000 iterations."
DIC: 1304.5 D_mean: 1159.06 D_post: 1113.86 logLik_var: 47.66
[1] "Converged in 1000 iterations."
DIC: 1008.4 D_mean: 785.01 D_post: 729.61 logLik_var: 69.7
[1] "Converged in 1000 iterations."
DIC: 1028.66 D_mean: 811.32 D_post: 745.39 logLik_var: 70.82
[1] "Converged in 1000 iterations."
DIC: 1267.35 D_mean: 1096.34 D_post: 1039.52 logLik_var: 56.96
[1] "Converged in 1000 iterations."
DIC: 1399.09 D_mean: 1056.88 D_post: 996.3 logLik_var: 100.7
[1] "Converged in 1000 iterations."
DIC: 1096.72 D_mean: 867.47 D_post: 818.27 logLik_var: 69.61
[1] "Converged in 1000 iterations."
DIC: 1534.37 D_mean: 1285.11 D_post: 1214.35 logLik_var: 80
[1] "Converged in 1000 iterations."
DIC: 1266.53 D_mean: 1034.62 D_post: 977.53 logLik_var: 72.25
[1] "Converged in 1000 iterations."
DIC: 1428.53 D_mean: 1197.46 D_post: 1136.84 logLik_var: 72.92
[1] "Converged in 1000 iterations."
DIC: 1525.55 D_mean: 1355.44 D_post: 1316.01 logLik_var: 52.38
[1] "Converged in 1000 iterations."
DIC: 1604.09 D_mean: 1445.12 D_post: 1379.82 logLik_var: 56.07
[1] "Converged in 1000 iterations."
DIC: 1054.87 D_mean: 873.98 D_post: 831.06 logLik_var: 55.95
[1] "Converged in 1000 iterations."
DIC: 1892.46 D_mean: 1616.68 D_post: 1525.31 logLik_var: 91.79
[1] "Converged in 1000 iterations."
DIC: 1947.57 D_mean: 1786.08 D_post: 1735.74 logLik_var: 52.96
[1] "Converged in 1000 iterations."
DIC: 1137.15 D_mean: 1004.87 D_post: 955.45 logLik_var: 45.43
[1] "Converged in 1000 iterations."
DIC: 1051.39 D_mean: 829.53 D_post: 761.46 logLik_var: 72.48
[1] "Converged in 1000 iterations."
DIC: 1429.25 D_mean: 1158.49 D_post: 1087.09 logLik_var: 85.54
[1] "Converged in 1000 iterations."
DIC: 1974.69 D_mean: 1830.55 D_post: 1780.38 logLik_var: 48.58
[1] "Converged in 1000 iterations."
DIC: 1550.4 D_mean: 1241.09 D_post: 1163.67 logLik_var: 96.68
[1] "Converged in 1000 iterations."
DIC: 1361.38 D_mean: 926 D_post: 832.08 logLik_var: 132.32
[1] "Converged in 1000 iterations."
DIC: 1072.58 D_mean: 711.15 D_post: 632.51 logLik_var: 110.02
[1] "Converged in 1000 iterations."
DIC: 2306.06 D_mean: 2119.21 D_post: 2073.22 logLik_var: 58.21
[1] "Converged in 1000 iterations."
DIC: 1880.41 D_mean: 1771.84 D_post: 1715.44 logLik_var: 41.24
[1] "Converged in 1000 iterations."
DIC: 1504.74 D_mean: 1270.54 D_post: 1185.37 logLik_var: 79.84
[1] "Converged in 1000 iterations."
DIC: 1893.12 D_mean: 1595.62 D_post: 1515.8 logLik_var: 94.33
[1] "Converged in 1000 iterations."
DIC: 1417.21 D_mean: 1235.32 D_post: 1176.02 logLik_var: 60.3
[1] "Converged in 1000 iterations."
DIC: 2007.01 D_mean: 1749.77 D_post: 1666.47 logLik_var: 85.13
[1] "Converged in 1000 iterations."
DIC: 2993.19 D_mean: 2859.06 D_post: 2795.89 logLik_var: 49.32
[1] "Converged in 1000 iterations."
DIC: 1801.76 D_mean: 1569.94 D_post: 1494.58 logLik_var: 76.79
[1] "Converged in 1000 iterations."
DIC: 1694.05 D_mean: 1467.89 D_post: 1391.5 logLik_var: 75.64
[1] "Converged in 1000 iterations."
DIC: 1174.48 D_mean: 1044.03 D_post: 990.18 logLik_var: 46.08
[1] "Converged in 1000 iterations."
DIC: 2257.21 D_mean: 2186.39 D_post: 2149.45 logLik_var: 26.94
[1] "Converged in 1000 iterations."
DIC: 2051.16 D_mean: 1836.61 D_post: 1778.9 logLik_var: 68.07
[1] "Converged in 1000 iterations."
DIC: 1548.36 D_mean: 1322.55 D_post: 1259.83 logLik_var: 72.13
[1] "Converged in 1000 iterations."
DIC: 1673.7 D_mean: 1476.54 D_post: 1405.3 logLik_var: 67.1
[1] "Converged in 1000 iterations."
DIC: 1698.54 D_mean: 1585.65 D_post: 1529.8 logLik_var: 42.18
[1] "Converged in 1000 iterations."
DIC: 1212.82 D_mean: 1043.55 D_post: 979.14 logLik_var: 58.42
[1] "Converged in 1000 iterations."
DIC: 2957.64 D_mean: 2879.33 D_post: 2819.62 logLik_var: 34.5
[1] "Converged in 1000 iterations."
DIC: 2108.76 D_mean: 1894.03 D_post: 1822.7 logLik_var: 71.52
[1] "Converged in 1000 iterations."
DIC: 2596.78 D_mean: 2450.36 D_post: 2398.95 logLik_var: 49.46
[1] "Converged in 1000 iterations."
DIC: 1598.33 D_mean: 1422.45 D_post: 1338.61 logLik_var: 64.93
[1] "Converged in 1000 iterations."
DIC: 4024.37 D_mean: 3869.15 D_post: 3809.91 logLik_var: 53.61
[1] "Converged in 1000 iterations."
DIC: 2667.58 D_mean: 2594.28 D_post: 2530.76 logLik_var: 34.2
[1] "Converged in 1000 iterations."
DIC: 2344.79 D_mean: 2210.46 D_post: 2150.93 logLik_var: 48.46
[1] "Converged in 1000 iterations."
DIC: 2174.74 D_mean: 1995.4 D_post: 1940.74 logLik_var: 58.5
[1] "Converged in 1000 iterations."
DIC: 3027.94 D_mean: 2900.63 D_post: 2816.43 logLik_var: 52.88
[1] "Converged in 1000 iterations."
DIC: 2881.02 D_mean: 2756.29 D_post: 2688.95 logLik_var: 48.02
[1] "Converged in 1000 iterations."
DIC: 2559.2 D_mean: 2387.45 D_post: 2324.27 logLik_var: 58.73
[1] "Converged in 1000 iterations."
DIC: 2193.38 D_mean: 1950.17 D_post: 1883.78 logLik_var: 77.4
[1] "Converged in 1000 iterations."
DIC: 2888.3 D_mean: 2749.65 D_post: 2679.26 logLik_var: 52.26
[1] "Converged in 1000 iterations."
DIC: 2785.52 D_mean: 2669.48 D_post: 2594.25 logLik_var: 47.82
[1] "Converged in 1000 iterations."
DIC: 2113.83 D_mean: 1923.85 D_post: 1838.83 logLik_var: 68.75
[1] "Converged in 1000 iterations."
DIC: 4876.5 D_mean: 4734.07 D_post: 4650.45 logLik_var: 56.51
[1] "Converged in 1000 iterations."
DIC: 3548.87 D_mean: 3449.4 D_post: 3381.3 logLik_var: 41.89
[1] "Converged in 1000 iterations."
DIC: 2399.23 D_mean: 2292.21 D_post: 2229.82 logLik_var: 42.35
[1] "Converged in 1000 iterations."
DIC: 4070.21 D_mean: 3978.66 D_post: 3902.01 logLik_var: 42.05
[1] "Converged in 1000 iterations."
DIC: 2703.02 D_mean: 2560.41 D_post: 2493.72 logLik_var: 52.33
[1] "Converged in 1000 iterations."
DIC: 3305.21 D_mean: 3206.64 D_post: 3124.36 logLik_var: 45.21
[1] "Converged in 1000 iterations."
DIC: 2251.04 D_mean: 2055.46 D_post: 1966.46 logLik_var: 71.14
[1] "Converged in 1000 iterations."
DIC: 3446.09 D_mean: 3352.96 D_post: 3297.06 logLik_var: 37.26
[1] "Converged in 1000 iterations."
DIC: 2379.23 D_mean: 2260.18 D_post: 2190.31 logLik_var: 47.23
[1] "Converged in 1000 iterations."
DIC: 712.48 D_mean: 495.61 D_post: 457.34 logLik_var: 63.79
[1] "Converged in 1000 iterations."
DIC: 1024.08 D_mean: 741.56 D_post: 689.87 logLik_var: 83.55
[1] "Converged in 1000 iterations."
DIC: 1024.12 D_mean: 889.27 D_post: 852.05 logLik_var: 43.02
[1] "Converged in 1000 iterations."
DIC: 580.52 D_mean: 339.64 D_post: 297.58 logLik_var: 70.73
[1] "Converged in 1000 iterations."
DIC: 1288.96 D_mean: 1124.93 D_post: 1097.72 logLik_var: 47.81
[1] "Converged in 1000 iterations."
DIC: 1274.16 D_mean: 1130.72 D_post: 1088.34 logLik_var: 46.45
[1] "Converged in 1000 iterations."
DIC: 791.4 D_mean: 581.06 D_post: 547.15 logLik_var: 61.06
[1] "Converged in 1000 iterations."
DIC: 893.38 D_mean: 728.68 D_post: 684.14 logLik_var: 52.31
[1] "Converged in 1000 iterations."
DIC: 1084.74 D_mean: 896.75 D_post: 846.61 logLik_var: 59.53
[1] "Converged in 1000 iterations."
DIC: 1180.41 D_mean: 1069.78 D_post: 1037.47 logLik_var: 35.73
[1] "Converged in 1000 iterations."
DIC: 1386.44 D_mean: 1202.71 D_post: 1159.13 logLik_var: 56.83
[1] "Converged in 1000 iterations."
DIC: 1085.05 D_mean: 868.72 D_post: 811.58 logLik_var: 68.37
[1] "Converged in 1000 iterations."
DIC: 969.87 D_mean: 809.84 D_post: 747.78 logLik_var: 55.52
[1] "Converged in 1000 iterations."
DIC: 1200.7 D_mean: 1004.7 D_post: 953.74 logLik_var: 61.74
[1] "Converged in 1000 iterations."
DIC: 898.09 D_mean: 604.55 D_post: 563.81 logLik_var: 83.57
[1] "Converged in 1000 iterations."
DIC: 1390.79 D_mean: 1242.32 D_post: 1191.46 logLik_var: 49.83
[1] "Converged in 1000 iterations."
DIC: 1739.12 D_mean: 1686.99 D_post: 1644.85 logLik_var: 23.57
[1] "Converged in 1000 iterations."
DIC: 1504.33 D_mean: 1391.81 D_post: 1343.97 logLik_var: 40.09
[1] "Converged in 1000 iterations."
DIC: 847.55 D_mean: 694.74 D_post: 655.8 logLik_var: 47.94
[1] "Converged in 1000 iterations."
DIC: 973.96 D_mean: 782.23 D_post: 741.85 logLik_var: 58.03
[1] "Converged in 1000 iterations."
DIC: 1753.89 D_mean: 1514.04 D_post: 1453.93 logLik_var: 74.99
[1] "Converged in 1000 iterations."
DIC: 2415.36 D_mean: 2297.18 D_post: 2247.13 logLik_var: 42.06
[1] "Converged in 1000 iterations."
DIC: 1205.16 D_mean: 943.51 D_post: 870.39 logLik_var: 83.69
[1] "Converged in 1000 iterations."
DIC: 1274.49 D_mean: 1038.89 D_post: 961.68 logLik_var: 78.2
[1] "Converged in 1000 iterations."
DIC: 1097.3 D_mean: 882.45 D_post: 808.14 logLik_var: 72.29
[1] "Converged in 1000 iterations."
DIC: 1376.98 D_mean: 1139.72 D_post: 1079.8 logLik_var: 74.3
[1] "Converged in 1000 iterations."
DIC: 1139.49 D_mean: 832.25 D_post: 776.07 logLik_var: 90.86
[1] "Converged in 1000 iterations."
DIC: 985.77 D_mean: 703.55 D_post: 636.26 logLik_var: 87.38
[1] "Converged in 1000 iterations."
DIC: 1473.93 D_mean: 1218.28 D_post: 1162.83 logLik_var: 77.78
[1] "Converged in 1000 iterations."
DIC: 717.84 D_mean: 526.31 D_post: 501.27 logLik_var: 54.14
[1] "Converged in 1000 iterations."
DIC: 1263.33 D_mean: 1087.52 D_post: 1022.94 logLik_var: 60.1
[1] "Converged in 1000 iterations."
DIC: 1026.01 D_mean: 772.66 D_post: 706.81 logLik_var: 79.8
[1] "Converged in 1000 iterations."
DIC: 914.27 D_mean: 622.36 D_post: 561.79 logLik_var: 88.12
[1] "Converged in 1000 iterations."
DIC: 1597.13 D_mean: 1356.55 D_post: 1300.1 logLik_var: 74.26
[1] "Converged in 1000 iterations."
DIC: 1707.02 D_mean: 1377.25 D_post: 1320.44 logLik_var: 96.64
[1] "Converged in 1000 iterations."
DIC: 1609.74 D_mean: 1391.65 D_post: 1322.05 logLik_var: 71.92
[1] "Converged in 1000 iterations."
DIC: 1510.03 D_mean: 1386.04 D_post: 1327.26 logLik_var: 45.69
[1] "Converged in 1000 iterations."
DIC: 1553.94 D_mean: 1381.96 D_post: 1315.04 logLik_var: 59.72
[1] "Converged in 1000 iterations."
DIC: 998.7 D_mean: 763.03 D_post: 684.1 logLik_var: 78.65
[1] "Converged in 1000 iterations."
DIC: 1798.41 D_mean: 1613.19 D_post: 1545.59 logLik_var: 63.21
[1] "Converged in 1000 iterations."
DIC: 1010.02 D_mean: 740.2 D_post: 664.06 logLik_var: 86.49
[1] "Converged in 1000 iterations."
DIC: 1462.9 D_mean: 1251.26 D_post: 1182.37 logLik_var: 70.13
[1] "Converged in 1000 iterations."
DIC: 1526.81 D_mean: 1274.25 D_post: 1196.64 logLik_var: 82.54
[1] "Converged in 1000 iterations."
DIC: 2079.51 D_mean: 1903.87 D_post: 1787.29 logLik_var: 73.05
[1] "Converged in 1000 iterations."
DIC: 1669.66 D_mean: 1456.6 D_post: 1379.93 logLik_var: 72.43
[1] "Converged in 1000 iterations."
DIC: 2000.52 D_mean: 1834.15 D_post: 1778.27 logLik_var: 55.56
[1] "Converged in 1000 iterations."
DIC: 2163.46 D_mean: 1941.2 D_post: 1856.51 logLik_var: 76.74
[1] "Converged in 1000 iterations."
DIC: 1359.1 D_mean: 1138.6 D_post: 1066.11 logLik_var: 73.25
[1] "Converged in 1000 iterations."
DIC: 1513.01 D_mean: 1248.46 D_post: 1176.03 logLik_var: 84.24
[1] "Converged in 1000 iterations."
DIC: 2299.99 D_mean: 2082.2 D_post: 2018.54 logLik_var: 70.36
[1] "Converged in 1000 iterations."
DIC: 1400.9 D_mean: 1228.2 D_post: 1175.43 logLik_var: 56.37
[1] "Converged in 1000 iterations."
DIC: 1594.01 D_mean: 1357.45 D_post: 1295.49 logLik_var: 74.63
[1] "Converged in 1000 iterations."
DIC: 1861.19 D_mean: 1634.94 D_post: 1493.3 logLik_var: 91.97
[1] "Converged in 1000 iterations."
DIC: 1586.92 D_mean: 1348.19 D_post: 1282.66 logLik_var: 76.07
[1] "Converged in 1000 iterations."
DIC: 904.16 D_mean: 609.19 D_post: 548.46 logLik_var: 88.93
[1] "Converged in 1000 iterations."
DIC: 2345.23 D_mean: 2101.11 D_post: 2039.77 logLik_var: 76.37
[1] "Converged in 1000 iterations."
DIC: 1515.96 D_mean: 1289.05 D_post: 1241.94 logLik_var: 68.5
[1] "Converged in 1000 iterations."
DIC: 1280.85 D_mean: 998.44 D_post: 915.98 logLik_var: 91.22
[1] "Converged in 1000 iterations."
DIC: 1485.46 D_mean: 1175.71 D_post: 1107.3 logLik_var: 94.54
[1] "Converged in 1000 iterations."
DIC: 1312.21 D_mean: 1072.07 D_post: 980.22 logLik_var: 83
[1] "Converged in 1000 iterations."
DIC: 2072.63 D_mean: 1786.97 D_post: 1699.21 logLik_var: 93.36
[1] "Converged in 1000 iterations."
DIC: 2107.16 D_mean: 1898.09 D_post: 1810.63 logLik_var: 74.13
[1] "Converged in 1000 iterations."
DIC: 1904.83 D_mean: 1520.67 D_post: 1457.08 logLik_var: 111.94
[1] "Converged in 1000 iterations."
DIC: 1365.34 D_mean: 1082.37 D_post: 1005.7 logLik_var: 89.91
[1] "Converged in 1000 iterations."
DIC: 2375.63 D_mean: 2150.26 D_post: 2082.32 logLik_var: 73.33
[1] "Converged in 1000 iterations."
DIC: 2674.23 D_mean: 2490.86 D_post: 2412 logLik_var: 65.56
[1] "Converged in 1000 iterations."
DIC: 1376.2 D_mean: 1123.36 D_post: 1033.71 logLik_var: 85.62
[1] "Converged in 1000 iterations."
DIC: 1761.31 D_mean: 1549.47 D_post: 1481.1 logLik_var: 70.05
[1] "Converged in 1000 iterations."
DIC: 2064.53 D_mean: 1747.23 D_post: 1667.82 logLik_var: 99.18
[1] "Converged in 1000 iterations."
DIC: 2956.54 D_mean: 2733.37 D_post: 2679.79 logLik_var: 69.19
[1] "Converged in 1000 iterations."
DIC: 1826 D_mean: 1472.8 D_post: 1376.27 logLik_var: 112.43
[1] "Converged in 1000 iterations."
DIC: 1811.22 D_mean: 1561.44 D_post: 1491.93 logLik_var: 79.82
[1] "Converged in 1000 iterations."
DIC: 1322.05 D_mean: 953.4 D_post: 851.81 logLik_var: 117.56
[1] "Converged in 1000 iterations."
DIC: 1921.2 D_mean: 1651.56 D_post: 1578.02 logLik_var: 85.79
[1] "Converged in 1000 iterations."
DIC: 1975.68 D_mean: 1689.04 D_post: 1617.44 logLik_var: 89.56
[1] "Converged in 1000 iterations."
DIC: 1136.71 D_mean: 839.09 D_post: 755.54 logLik_var: 95.29
[1] "Converged in 1000 iterations."
DIC: 1404.59 D_mean: 1114.23 D_post: 1018.32 logLik_var: 96.57
[1] "Converged in 1000 iterations."
DIC: 2393.05 D_mean: 2068.68 D_post: 1973.89 logLik_var: 104.79
[1] "Converged in 1000 iterations."
DIC: 1854.45 D_mean: 1553.33 D_post: 1456.65 logLik_var: 99.45
[1] "Converged in 1000 iterations."
DIC: 1621.13 D_mean: 1315.58 D_post: 1238.82 logLik_var: 95.58
[1] "Converged in 1000 iterations."
DIC: 2144.12 D_mean: 1929.95 D_post: 1851.95 logLik_var: 73.04
[1] "Converged in 1000 iterations."
DIC: 2051.32 D_mean: 1768.9 D_post: 1673.81 logLik_var: 94.38
[1] "Converged in 1000 iterations."
DIC: 2582.14 D_mean: 2403.93 D_post: 2341.84 logLik_var: 60.07
[1] "Converged in 1000 iterations."
DIC: 2722.57 D_mean: 2416.89 D_post: 2330.87 logLik_var: 97.92
[1] "Converged in 1000 iterations."
DIC: 1664.11 D_mean: 1374.39 D_post: 1287.8 logLik_var: 94.08
[1] "Converged in 1000 iterations."
DIC: 2651.45 D_mean: 2384.94 D_post: 2306.34 logLik_var: 86.28
[1] "Converged in 1000 iterations."
DIC: 1962.54 D_mean: 1657.47 D_post: 1554.74 logLik_var: 101.95
[1] "Converged in 1000 iterations."
DIC: 1831.01 D_mean: 1550.4 D_post: 1492.48 logLik_var: 84.63
[1] "Converged in 1000 iterations."
DIC: 3330.21 D_mean: 3072.26 D_post: 2976.18 logLik_var: 88.51
[1] "Converged in 2100 iterations."
DIC: 2384.15 D_mean: 2047.76 D_post: 1952.52 logLik_var: 107.91
[1] "Converged in 1000 iterations."
DIC: 3057.46 D_mean: 2782.08 D_post: 2708 logLik_var: 87.37
[1] "Converged in 1000 iterations."
DIC: 2496.36 D_mean: 2189.78 D_post: 2103.17 logLik_var: 98.3
[1] "Converged in 1000 iterations."
DIC: 1839.11 D_mean: 1490.37 D_post: 1393.54 logLik_var: 111.39
[1] "Converged in 1000 iterations."
DIC: 1500.22 D_mean: 1254.34 D_post: 1170.45 logLik_var: 82.44
[1] "Converged in 1000 iterations."
DIC: 2595.21 D_mean: 2374.1 D_post: 2313.85 logLik_var: 70.34
[1] "Converged in 1000 iterations."
DIC: 1695.3 D_mean: 1381.06 D_post: 1304.08 logLik_var: 97.8
[1] "Converged in 1000 iterations."
DIC: 2436.78 D_mean: 2177.6 D_post: 2086.58 logLik_var: 87.55
[1] "Converged in 1000 iterations."
DIC: 3164.83 D_mean: 2936.69 D_post: 2851.41 logLik_var: 78.35
[1] "Converged in 1000 iterations."
DIC: 1771.68 D_mean: 1484.35 D_post: 1400.67 logLik_var: 92.75
[1] "Converged in 1000 iterations."
DIC: 1731.16 D_mean: 1439.26 D_post: 1339.95 logLik_var: 97.8
[1] "Converged in 1000 iterations."
DIC: 1964.87 D_mean: 1387.54 D_post: 1274 logLik_var: 172.72
[1] "Converged in 1000 iterations."
DIC: 3339.34 D_mean: 2994.66 D_post: 2900.4 logLik_var: 109.73
[1] "Converged in 1000 iterations."
DIC: 3366.93 D_mean: 2980.08 D_post: 2888.33 logLik_var: 119.65
[1] "Converged in 1000 iterations."
DIC: 3416.29 D_mean: 3158.86 D_post: 3073.77 logLik_var: 85.63
[1] "Converged in 1000 iterations."
DIC: 2427.91 D_mean: 2034.85 D_post: 1930.93 logLik_var: 124.25
[1] "Converged in 1000 iterations."
DIC: 2456.93 D_mean: 2129.19 D_post: 2028.84 logLik_var: 107.02
[1] "Converged in 1000 iterations."
DIC: 2243.46 D_mean: 1842.37 D_post: 1735.67 logLik_var: 126.95
[1] "Converged in 1000 iterations."
DIC: 4152.04 D_mean: 3924.72 D_post: 3840.05 logLik_var: 78
[1] "Converged in 1000 iterations."
DIC: 2256.93 D_mean: 1901.98 D_post: 1793.55 logLik_var: 115.84
[1] "Converged in 1000 iterations."
DIC: 4282.69 D_mean: 3956.57 D_post: 3844.95 logLik_var: 109.44
[1] "Converged in 1000 iterations."
DIC: 3030.88 D_mean: 2393.35 D_post: 2298.19 logLik_var: 183.17
[1] "Converged in 1000 iterations."
DIC: 2818.47 D_mean: 2408.35 D_post: 2316.84 logLik_var: 125.41
[1] "Converged in 1000 iterations."
DIC: 2015.07 D_mean: 1680.95 D_post: 1584.88 logLik_var: 107.55
[1] "Converged in 1000 iterations."
DIC: 2676.89 D_mean: 2320.86 D_post: 2210.08 logLik_var: 116.7
[1] "Converged in 1000 iterations."
DIC: 3275.41 D_mean: 3137.15 D_post: 3006.62 logLik_var: 67.2
[1] "Converged in 1000 iterations."
DIC: 2633.53 D_mean: 2289.32 D_post: 2184.97 logLik_var: 112.14
[1] "Converged in 1000 iterations."
DIC: 2084.73 D_mean: 1709.4 D_post: 1613.39 logLik_var: 117.83
[1] "Converged in 1000 iterations."
DIC: 2583.67 D_mean: 2128.56 D_post: 2030.01 logLik_var: 138.42
[1] "Converged in 1000 iterations."
DIC: 2642.03 D_mean: 2340.34 D_post: 2246.02 logLik_var: 99
[1] "Converged in 1000 iterations."
DIC: 1762.98 D_mean: 1418.23 D_post: 1326.92 logLik_var: 109.01
[1] "Converged in 1000 iterations."
DIC: 1840.14 D_mean: 1651.77 D_post: 1591.21 logLik_var: 62.23
[1] "Converged in 1000 iterations."
DIC: 1438.07 D_mean: 1105.73 D_post: 1012.65 logLik_var: 106.36
[1] "Converged in 1000 iterations."
DIC: 2575.81 D_mean: 2379.08 D_post: 2307.67 logLik_var: 67.04
[1] "Converged in 1000 iterations."
DIC: 1564.5 D_mean: 1384.34 D_post: 1320.83 logLik_var: 60.92
[1] "Converged in 1000 iterations."
DIC: 1165.2 D_mean: 935.57 D_post: 859.83 logLik_var: 76.34
[1] "Converged in 1000 iterations."
DIC: 1737.58 D_mean: 1490.66 D_post: 1423.82 logLik_var: 78.44
[1] "Converged in 1000 iterations."
DIC: 2388.9 D_mean: 2193.27 D_post: 2128.98 logLik_var: 64.98
[1] "Converged in 1000 iterations."
DIC: 1457.5 D_mean: 1237.89 D_post: 1184.84 logLik_var: 68.16
[1] "Converged in 1000 iterations."
DIC: 1925.65 D_mean: 1707.92 D_post: 1626.5 logLik_var: 74.79
[1] "Converged in 1000 iterations."
DIC: 1990.87 D_mean: 1768.28 D_post: 1707.35 logLik_var: 70.88
[1] "Converged in 1000 iterations."
DIC: 1457.26 D_mean: 1265.3 D_post: 1196.46 logLik_var: 65.2
[1] "Converged in 1000 iterations."
DIC: 1602.15 D_mean: 1346.77 D_post: 1260.12 logLik_var: 85.51
[1] "Converged in 1000 iterations."
DIC: 1663.56 D_mean: 1475.81 D_post: 1392.37 logLik_var: 67.8
[1] "Converged in 1000 iterations."
DIC: 1357.27 D_mean: 1084.14 D_post: 984.09 logLik_var: 93.3
[1] "Converged in 1000 iterations."
DIC: 2067.03 D_mean: 1874.48 D_post: 1799.2 logLik_var: 66.96
[1] "Converged in 1000 iterations."
DIC: 1866.31 D_mean: 1631.18 D_post: 1564.66 logLik_var: 75.41
[1] "Converged in 1000 iterations."
DIC: 1671.72 D_mean: 1531.61 D_post: 1470.07 logLik_var: 50.41
[1] "Converged in 1000 iterations."
DIC: 2112.67 D_mean: 1950.17 D_post: 1867.06 logLik_var: 61.4
[1] "Converged in 1000 iterations."
DIC: 1662.14 D_mean: 1373.08 D_post: 1295.11 logLik_var: 91.76
[1] "Converged in 1000 iterations."
DIC: 2091.61 D_mean: 1895.66 D_post: 1795.1 logLik_var: 74.13
[1] "Converged in 1000 iterations."
DIC: 1754.73 D_mean: 1561.86 D_post: 1517.72 logLik_var: 59.25
[1] "Converged in 1000 iterations."
DIC: 2102.99 D_mean: 1906.64 D_post: 1843.55 logLik_var: 64.86
[1] "Converged in 1000 iterations."
DIC: 1091.23 D_mean: 939.1 D_post: 874.86 logLik_var: 54.09
[1] "Converged in 1000 iterations."
DIC: 1569.88 D_mean: 1368.17 D_post: 1308.06 logLik_var: 65.46
[1] "Converged in 1000 iterations."
DIC: 1592.32 D_mean: 1308.8 D_post: 1239.93 logLik_var: 88.1
[1] "Converged in 1000 iterations."
DIC: 1767.28 D_mean: 1576.73 D_post: 1534.67 logLik_var: 58.15
[1] "Converged in 1000 iterations."
DIC: 1503.91 D_mean: 1231.63 D_post: 1176.85 logLik_var: 81.77
[1] "Converged in 1000 iterations."
DIC: 1477.15 D_mean: 1244.12 D_post: 1181.2 logLik_var: 73.99
[1] "Converged in 1000 iterations."
DIC: 1062.31 D_mean: 816.28 D_post: 755.14 logLik_var: 76.79
[1] "Converged in 1000 iterations."
DIC: 2421.73 D_mean: 2246.31 D_post: 2185.56 logLik_var: 59.04
[1] "Converged in 1000 iterations."
DIC: 1603.65 D_mean: 1382.51 D_post: 1309.39 logLik_var: 73.56
[1] "Converged in 1000 iterations."
DIC: 1625.61 D_mean: 1446.86 D_post: 1387.17 logLik_var: 59.61
[1] "Converged in 1000 iterations."
DIC: 1680.81 D_mean: 1387.2 D_post: 1312.79 logLik_var: 92.01
[1] "Converged in 1000 iterations."
DIC: 2209.75 D_mean: 1954.95 D_post: 1887.11 logLik_var: 80.66
[1] "Converged in 1000 iterations."
DIC: 1753.81 D_mean: 1491.7 D_post: 1438.81 logLik_var: 78.75
[1] "Converged in 1000 iterations."
DIC: 2277.28 D_mean: 2044.13 D_post: 1981.87 logLik_var: 73.85
[1] "Converged in 1000 iterations."
DIC: 1565.32 D_mean: 1258.11 D_post: 1190.12 logLik_var: 93.8
[1] "Converged in 1000 iterations."
DIC: 1666.87 D_mean: 1428.29 D_post: 1362.45 logLik_var: 76.11
[1] "Converged in 1000 iterations."
DIC: 1567.57 D_mean: 1442.69 D_post: 1389.5 logLik_var: 44.52
[1] "Converged in 1000 iterations."
DIC: 2049.42 D_mean: 1861.13 D_post: 1804.69 logLik_var: 61.18
[1] "Converged in 1000 iterations."
DIC: 1579.6 D_mean: 1288.02 D_post: 1232.11 logLik_var: 86.87
[1] "Converged in 1000 iterations."
DIC: 935.01 D_mean: 726.05 D_post: 673.64 logLik_var: 65.34
[1] "Converged in 1000 iterations."
DIC: 1673.8 D_mean: 1326.29 D_post: 1256.22 logLik_var: 104.39
[1] "Converged in 1000 iterations."
DIC: 1094.63 D_mean: 874 D_post: 796.8 logLik_var: 74.46
[1] "Converged in 1000 iterations."
DIC: 1531.3 D_mean: 1368.37 D_post: 1317.52 logLik_var: 53.45
[1] "Converged in 1000 iterations."
DIC: 1139.59 D_mean: 937.13 D_post: 879.62 logLik_var: 64.99
[1] "Converged in 1000 iterations."
DIC: 1702.91 D_mean: 1452.81 D_post: 1385.73 logLik_var: 79.29
[1] "Converged in 1000 iterations."
DIC: 1066.56 D_mean: 846.19 D_post: 783 logLik_var: 70.89
[1] "Converged in 1000 iterations."
DIC: 1374.05 D_mean: 1167.32 D_post: 1091.17 logLik_var: 70.72
[1] "Converged in 1000 iterations."
DIC: 1180.02 D_mean: 930.73 D_post: 853.99 logLik_var: 81.51
[1] "Converged in 1000 iterations."
DIC: 1534.9 D_mean: 1285.68 D_post: 1236.69 logLik_var: 74.55
[1] "Converged in 1000 iterations."
DIC: 910.63 D_mean: 666.03 D_post: 617.44 logLik_var: 73.3
[1] "Converged in 1000 iterations."
DIC: 1312.13 D_mean: 1137.1 D_post: 1081.96 logLik_var: 57.54
[1] "Converged in 1000 iterations."
DIC: 1054.42 D_mean: 850.91 D_post: 803.93 logLik_var: 62.62
[1] "Converged in 1000 iterations."
DIC: 1547.93 D_mean: 1379.33 D_post: 1311.59 logLik_var: 59.09
[1] "Converged in 1000 iterations."
DIC: 1290.75 D_mean: 992.92 D_post: 935.65 logLik_var: 88.78
[1] "Converged in 1000 iterations."
DIC: 1773.51 D_mean: 1639.63 D_post: 1563.41 logLik_var: 52.53
[1] "Converged in 1000 iterations."
DIC: 1641.54 D_mean: 1417.56 D_post: 1368.66 logLik_var: 68.22
[1] "Converged in 1600 iterations."
DIC: 1265.26 D_mean: 1144.39 D_post: 1108.96 logLik_var: 39.07
[1] "Converged in 1000 iterations."
DIC: 2252.68 D_mean: 2109.71 D_post: 2061.81 logLik_var: 47.72
[1] "Converged in 1000 iterations."
DIC: 835.34 D_mean: 615.66 D_post: 571.85 logLik_var: 65.87
[1] "Converged in 1000 iterations."
DIC: 1229.05 D_mean: 1002.12 D_post: 957.7 logLik_var: 67.84
[1] "Converged in 1000 iterations."
DIC: 1472.44 D_mean: 1303.53 D_post: 1257 logLik_var: 53.86
[1] "Converged in 1000 iterations."
DIC: 1399.39 D_mean: 1226.02 D_post: 1184.46 logLik_var: 53.73
[1] "Converged in 1000 iterations."
DIC: 1223.19 D_mean: 1009.4 D_post: 953.84 logLik_var: 67.34
[1] "Converged in 1000 iterations."
DIC: 747.33 D_mean: 534.01 D_post: 496.85 logLik_var: 62.62
[1] "Converged in 1000 iterations."
DIC: 1802.15 D_mean: 1586.45 D_post: 1523.76 logLik_var: 69.6
[1] "Converged in 1000 iterations."
DIC: 713.06 D_mean: 485.04 D_post: 430.12 logLik_var: 70.73
[1] "Converged in 1000 iterations."
DIC: 1327.6 D_mean: 1128.22 D_post: 1075.09 logLik_var: 63.13
[1] "Converged in 1000 iterations."
DIC: 905.43 D_mean: 652.15 D_post: 600.54 logLik_var: 76.22
[1] "Converged in 1000 iterations."
DIC: 595.33 D_mean: 409.72 D_post: 374.28 logLik_var: 55.26
[1] "Converged in 1000 iterations."
DIC: 843.73 D_mean: 633.05 D_post: 572.25 logLik_var: 67.87
[1] "Converged in 1000 iterations."
DIC: 792.72 D_mean: 527.85 D_post: 465.59 logLik_var: 81.78
[1] "Converged in 1000 iterations."
DIC: 1207.11 D_mean: 1021.57 D_post: 981.29 logLik_var: 56.46
[1] "Converged in 1000 iterations."
DIC: 1082.58 D_mean: 856.69 D_post: 810.87 logLik_var: 67.93
[1] "Converged in 1000 iterations."
DIC: 779.58 D_mean: 600.78 D_post: 559.46 logLik_var: 55.03
[1] "Converged in 1000 iterations."
DIC: 1000.56 D_mean: 796.54 D_post: 757.35 logLik_var: 60.8
[1] "Converged in 1000 iterations."
DIC: 944.79 D_mean: 796.31 D_post: 754.93 logLik_var: 47.47
[1] "Converged in 1000 iterations."
DIC: 1419.62 D_mean: 1095.54 D_post: 1026.36 logLik_var: 98.32
[1] "Converged in 1000 iterations."
DIC: 1184.79 D_mean: 960.44 D_post: 914.18 logLik_var: 67.65
[1] "Converged in 1000 iterations."
DIC: 680.31 D_mean: 412.16 D_post: 360.03 logLik_var: 80.07
[1] "Converged in 1000 iterations."
DIC: 774.75 D_mean: 540.82 D_post: 492.38 logLik_var: 70.59
[1] "Converged in 1000 iterations."
DIC: 813.93 D_mean: 613.76 D_post: 582.75 logLik_var: 57.8
[1] "Converged in 1000 iterations."
DIC: 1257.02 D_mean: 941.68 D_post: 887.71 logLik_var: 92.33
[1] "Converged in 1000 iterations."
DIC: 841.24 D_mean: 586.79 D_post: 535.9 logLik_var: 76.33
[1] "Converged in 1000 iterations."
DIC: 733.01 D_mean: 527.27 D_post: 488 logLik_var: 61.25
[1] "Converged in 1000 iterations."
DIC: 781.57 D_mean: 604.63 D_post: 562.72 logLik_var: 54.71
[1] "Converged in 1000 iterations."
DIC: 872.63 D_mean: 616.68 D_post: 566.85 logLik_var: 76.45
[1] "Converged in 1000 iterations."
DIC: 600.06 D_mean: 423.94 D_post: 400.78 logLik_var: 49.82
[1] "Converged in 1000 iterations."
DIC: 761.25 D_mean: 496.02 D_post: 440.68 logLik_var: 80.14
[1] "Converged in 1000 iterations."
DIC: 771.22 D_mean: 547.85 D_post: 485.54 logLik_var: 71.42
[1] "Converged in 1000 iterations."
DIC: 761.39 D_mean: 510.14 D_post: 464.74 logLik_var: 74.16
[1] "Converged in 1000 iterations."
DIC: 833.43 D_mean: 626.59 D_post: 596.84 logLik_var: 59.15
[1] "Converged in 1000 iterations."
DIC: 879.11 D_mean: 611.61 D_post: 563.84 logLik_var: 78.82
[1] "Converged in 1000 iterations."
DIC: 868.79 D_mean: 692.2 D_post: 650.75 logLik_var: 54.51
[1] "Converged in 1000 iterations."
DIC: 625.31 D_mean: 398.74 D_post: 366.71 logLik_var: 64.65
[1] "Converged in 1000 iterations."
DIC: 762.28 D_mean: 562.73 D_post: 518.16 logLik_var: 61.03
[1] "Converged in 1000 iterations."
DIC: 266.66 D_mean: 131.02 D_post: 125.24 logLik_var: 35.35
[1] "Converged in 1000 iterations."
DIC: 727.83 D_mean: 493.33 D_post: 435.85 logLik_var: 72.99
[1] "Converged in 1000 iterations."
DIC: 797.51 D_mean: 623.06 D_post: 585.51 logLik_var: 53
[1] "Converged in 1000 iterations."
DIC: 402.21 D_mean: 223.91 D_post: 208.57 logLik_var: 48.41
[1] "Converged in 1000 iterations."
DIC: 378.87 D_mean: 206.78 D_post: 192.25 logLik_var: 46.65
[1] "Converged in 1000 iterations."
DIC: 502.18 D_mean: 214.97 D_post: 208.17 logLik_var: 73.5
[1] "Converged in 1800 iterations."
DIC: 443.8 D_mean: 300.02 D_post: 271.92 logLik_var: 42.97
[1] "Converged in 1000 iterations."
DIC: 404.63 D_mean: 298.2 D_post: 282.12 logLik_var: 30.63
[1] "Converged in 1000 iterations."
DIC: 363.64 D_mean: 212.56 D_post: 201.58 logLik_var: 40.51
[1] "Converged in 1000 iterations."
DIC: 483.37 D_mean: 265.81 D_post: 233.8 logLik_var: 62.39
[1] "Converged in 1000 iterations."
DIC: 526.15 D_mean: 322.83 D_post: 293.7 logLik_var: 58.11
[1] "Converged in 1000 iterations."
DIC: 521.07 D_mean: 327.66 D_post: 304.84 logLik_var: 54.06
[1] "Converged in 1000 iterations."
DIC: 419.65 D_mean: 249.31 D_post: 225.83 logLik_var: 48.46
[1] "Converged in 1000 iterations."
DIC: 359.49 D_mean: 182.02 D_post: 155.29 logLik_var: 51.05
[1] "Converged in 1000 iterations."
DIC: 301.84 D_mean: 180.15 D_post: 165.64 logLik_var: 34.05
[1] "Converged in 1000 iterations."
DIC: 321.04 D_mean: 189.59 D_post: 174.31 logLik_var: 36.68
[1] "Converged in 1000 iterations."
DIC: 447.63 D_mean: 271.66 D_post: 251.26 logLik_var: 49.09
[1] "Converged in 1000 iterations."
DIC: 379.41 D_mean: 292.45 D_post: 275.12 logLik_var: 26.07
[1] "Converged in 1000 iterations."
DIC: 385.47 D_mean: 229.7 D_post: 220.3 logLik_var: 41.29
[1] "Converged in 1000 iterations."
DIC: 480.32 D_mean: 279.43 D_post: 257.05 logLik_var: 55.82
[1] "Converged in 1000 iterations."
DIC: 440.97 D_mean: 272.67 D_post: 244.25 logLik_var: 49.18
[1] "Converged in 1000 iterations."
DIC: 449.64 D_mean: 256.65 D_post: 230.58 logLik_var: 54.77
[1] "Converged in 1000 iterations."
DIC: 758.64 D_mean: 624.8 D_post: 595.12 logLik_var: 40.88
[1] "Converged in 1000 iterations."
DIC: 1063.91 D_mean: 830.29 D_post: 780.37 logLik_var: 70.88
[1] "Converged in 1000 iterations."
DIC: 938.99 D_mean: 658.01 D_post: 599.42 logLik_var: 84.89
[1] "Converged in 1000 iterations."
DIC: 1566.89 D_mean: 1191.22 D_post: 1125.91 logLik_var: 110.25
[1] "Converged in 1000 iterations."
DIC: 1655.82 D_mean: 1341.4 D_post: 1275.45 logLik_var: 95.09
[1] "Converged in 1000 iterations."
DIC: 1343.59 D_mean: 1045.55 D_post: 973.05 logLik_var: 92.64
[1] "Converged in 1000 iterations."
DIC: 1662.13 D_mean: 1433.22 D_post: 1375.99 logLik_var: 71.53
[1] "Converged in 1000 iterations."
DIC: 1196.97 D_mean: 883.67 D_post: 815.41 logLik_var: 95.39
[1] "Converged in 1000 iterations."
DIC: 1077.63 D_mean: 825.29 D_post: 775.29 logLik_var: 75.58
[1] "Converged in 1000 iterations."
DIC: 1270.59 D_mean: 884.07 D_post: 821.04 logLik_var: 112.39
[1] "Converged in 1000 iterations."
DIC: 1104.35 D_mean: 856.56 D_post: 793.37 logLik_var: 77.74
[1] "Converged in 1000 iterations."
DIC: 1210.59 D_mean: 876.63 D_post: 810.67 logLik_var: 99.98
[1] "Converged in 1700 iterations."
DIC: 1086.74 D_mean: 755.94 D_post: 708.98 logLik_var: 94.44
[1] "Converged in 1000 iterations."
DIC: 2140.1 D_mean: 1918.59 D_post: 1875.79 logLik_var: 66.08
[1] "Converged in 1000 iterations."
DIC: 826.76 D_mean: 543.76 D_post: 498.36 logLik_var: 82.1
[1] "Converged in 1000 iterations."
DIC: 558.74 D_mean: 308.09 D_post: 289.87 logLik_var: 67.22
[1] "Converged in 1000 iterations."
DIC: 1661.34 D_mean: 1391.83 D_post: 1329.33 logLik_var: 83
[1] "Converged in 1000 iterations."
DIC: 1165.3 D_mean: 928.41 D_post: 861 logLik_var: 76.07
[1] "Converged in 1000 iterations."
DIC: 1048.08 D_mean: 664.12 D_post: 603.09 logLik_var: 111.25
[1] "Converged in 1000 iterations."
DIC: 1407.26 D_mean: 1208.39 D_post: 1158.08 logLik_var: 62.29
[1] "Converged in 1000 iterations."
DIC: 1060.52 D_mean: 742.93 D_post: 664.89 logLik_var: 98.91
[1] "Converged in 1000 iterations."
DIC: 1127 D_mean: 853.88 D_post: 781.93 logLik_var: 86.27
[1] "Converged in 1000 iterations."
DIC: 1452.09 D_mean: 1217.13 D_post: 1159.96 logLik_var: 73.03
[1] "Converged in 1000 iterations."
DIC: 899.48 D_mean: 651.31 D_post: 599.02 logLik_var: 75.12
[1] "Converged in 1000 iterations."
DIC: 1486.14 D_mean: 1280.06 D_post: 1234.8 logLik_var: 62.83
[1] "Converged in 1000 iterations."
DIC: 1770.5 D_mean: 1594.27 D_post: 1546.13 logLik_var: 56.09
[1] "Converged in 1000 iterations."
DIC: 733.13 D_mean: 532.28 D_post: 487.34 logLik_var: 61.45
[1] "Converged in 1000 iterations."
DIC: 1465.49 D_mean: 1241.32 D_post: 1166.83 logLik_var: 74.67
[1] "Converged in 1000 iterations."
DIC: 1227.11 D_mean: 1042.83 D_post: 983.48 logLik_var: 60.91
[1] "Converged in 1000 iterations."
DIC: 966.7 D_mean: 732.51 D_post: 681.83 logLik_var: 71.22
[1] "Converged in 1000 iterations."
DIC: 1786.97 D_mean: 1602.75 D_post: 1548.85 logLik_var: 59.53
[1] "Converged in 1000 iterations."
DIC: 1072.25 D_mean: 727.88 D_post: 668.53 logLik_var: 100.93
[1] "Converged in 1000 iterations."
DIC: 1700.54 D_mean: 1434.24 D_post: 1383.74 logLik_var: 79.2
[1] "Converged in 1000 iterations."
DIC: 1438.35 D_mean: 1178.12 D_post: 1096.12 logLik_var: 85.56
[1] "Converged in 1000 iterations."
DIC: 596.93 D_mean: 348.71 D_post: 313.14 logLik_var: 70.95
[1] "Converged in 1000 iterations."
DIC: 1331.13 D_mean: 1021.45 D_post: 958.84 logLik_var: 93.07
[1] "Converged in 1000 iterations."
DIC: 1410.46 D_mean: 1243.92 D_post: 1196.49 logLik_var: 53.49
[1] "Converged in 1000 iterations."
DIC: 1638.81 D_mean: 1344.46 D_post: 1283.53 logLik_var: 88.82
[1] "Converged in 1000 iterations."
DIC: 714.11 D_mean: 436.8 D_post: 385.91 logLik_var: 82.05
[1] "Converged in 1000 iterations."
DIC: 1892.61 D_mean: 1713.31 D_post: 1657.78 logLik_var: 58.71
[1] "Converged in 1000 iterations."
DIC: 1378.33 D_mean: 1071.7 D_post: 985.59 logLik_var: 98.18
[1] "Converged in 1000 iterations."
DIC: 1161.65 D_mean: 977.14 D_post: 914.26 logLik_var: 61.85
[1] "Converged in 1000 iterations."
DIC: 1153.55 D_mean: 921.28 D_post: 860.25 logLik_var: 73.32
[1] "Converged in 1000 iterations."
DIC: 1388.37 D_mean: 1099.56 D_post: 1035.93 logLik_var: 88.11
[1] "Converged in 1000 iterations."
DIC: 1104.49 D_mean: 923.28 D_post: 870.54 logLik_var: 58.49
[1] "Converged in 1000 iterations."
DIC: 1131.27 D_mean: 966.6 D_post: 918.24 logLik_var: 53.26
[1] "Converged in 1000 iterations."
DIC: 1603.43 D_mean: 1390.68 D_post: 1343.16 logLik_var: 65.07
[1] "Converged in 1000 iterations."
DIC: 1566.87 D_mean: 1458.29 D_post: 1408.53 logLik_var: 39.59
[1] "Converged in 1000 iterations."
DIC: 1137.37 D_mean: 932.24 D_post: 891.24 logLik_var: 61.53
[1] "Converged in 1000 iterations."
DIC: 2341.18 D_mean: 2173.29 D_post: 2131.39 logLik_var: 52.45
[1] "Converged in 1000 iterations."
DIC: 806.68 D_mean: 610.93 D_post: 557.7 logLik_var: 62.25
[1] "Converged in 1000 iterations."
DIC: 1440.28 D_mean: 1236.96 D_post: 1182.59 logLik_var: 64.42
[1] "Converged in 1000 iterations."
DIC: 1586.02 D_mean: 1431.24 D_post: 1368.93 logLik_var: 54.27
[1] "Converged in 1000 iterations."
DIC: 1723.81 D_mean: 1534.76 D_post: 1463.25 logLik_var: 65.14
[1] "Converged in 1000 iterations."
DIC: 1692.41 D_mean: 1500.35 D_post: 1453.45 logLik_var: 59.74
[1] "Converged in 1000 iterations."
DIC: 1599.98 D_mean: 1437.54 D_post: 1395.71 logLik_var: 51.07
[1] "Converged in 1000 iterations."
DIC: 1417.31 D_mean: 1241.03 D_post: 1169.83 logLik_var: 61.87
[1] "Converged in 1000 iterations."
DIC: 1437.77 D_mean: 1270.04 D_post: 1208.32 logLik_var: 57.36
[1] "Converged in 1000 iterations."
DIC: 1490.23 D_mean: 1286.42 D_post: 1217.38 logLik_var: 68.21
[1] "Converged in 1000 iterations."
DIC: 1046.97 D_mean: 890.12 D_post: 826.12 logLik_var: 55.21
[1] "Converged in 1000 iterations."
DIC: 1452.52 D_mean: 1281.84 D_post: 1206.31 logLik_var: 61.55
[1] "Converged in 1000 iterations."
DIC: 1120.82 D_mean: 918.21 D_post: 871.05 logLik_var: 62.44
[1] "Converged in 1000 iterations."
DIC: 1969.27 D_mean: 1867.79 D_post: 1815.36 logLik_var: 38.48
[1] "Converged in 1000 iterations."
DIC: 1950.37 D_mean: 1815.35 D_post: 1763.35 logLik_var: 46.76
[1] "Converged in 1000 iterations."
DIC: 1631.41 D_mean: 1455.72 D_post: 1401.48 logLik_var: 57.48
[1] "Converged in 1000 iterations."
DIC: 870.35 D_mean: 678.28 D_post: 624.1 logLik_var: 61.56
[1] "Converged in 1000 iterations."
DIC: 960.28 D_mean: 773.56 D_post: 704.29 logLik_var: 64
[1] "Converged in 1000 iterations."
DIC: 1104.29 D_mean: 674.52 D_post: 600.5 logLik_var: 125.95
[1] "Converged in 1000 iterations."
DIC: 1251.63 D_mean: 1144.12 D_post: 1096.42 logLik_var: 38.8
[1] "Converged in 1000 iterations."
DIC: 989.67 D_mean: 794.48 D_post: 743.01 logLik_var: 61.67
[1] "Converged in 1000 iterations."
DIC: 1423.44 D_mean: 1136.9 D_post: 1073.24 logLik_var: 87.55
[1] "Converged in 1000 iterations."
DIC: 988.74 D_mean: 744.07 D_post: 674.44 logLik_var: 78.58
[1] "Converged in 1000 iterations."
DIC: 975.66 D_mean: 748.21 D_post: 699.68 logLik_var: 68.99
[1] "Converged in 1000 iterations."
DIC: 1217.35 D_mean: 1065.77 D_post: 1009.98 logLik_var: 51.84
[1] "Converged in 1000 iterations."
DIC: 935.87 D_mean: 691.38 D_post: 636.18 logLik_var: 74.92
[1] "Converged in 1000 iterations."
DIC: 1300 D_mean: 1136.68 D_post: 1083.24 logLik_var: 54.19
[1] "Converged in 1000 iterations."
DIC: 1673.52 D_mean: 1465.49 D_post: 1393.34 logLik_var: 70.05
[1] "Converged in 1000 iterations."
DIC: 1353.9 D_mean: 1172.09 D_post: 1127.96 logLik_var: 56.48
[1] "Converged in 1000 iterations."
DIC: 1646.33 D_mean: 1472.46 D_post: 1414.94 logLik_var: 57.85
[1] "Converged in 1000 iterations."
DIC: 779.96 D_mean: 509.65 D_post: 450.28 logLik_var: 82.42
[1] "Converged in 1000 iterations."
DIC: 918.19 D_mean: 696.62 D_post: 645.19 logLik_var: 68.25
[1] "Converged in 1000 iterations."
DIC: 1169.98 D_mean: 1015.88 D_post: 978.42 logLik_var: 47.89
[1] "Converged in 1000 iterations."
DIC: 2451.99 D_mean: 2303.75 D_post: 2267.99 logLik_var: 46
[1] "Converged in 1000 iterations."
DIC: 1314.12 D_mean: 1090.88 D_post: 1022.43 logLik_var: 72.92
[1] "Converged in 1000 iterations."
DIC: 2181.05 D_mean: 2049.97 D_post: 2016.66 logLik_var: 41.1
[1] "Converged in 1000 iterations."
DIC: 1080.69 D_mean: 853.25 D_post: 797.31 logLik_var: 70.84
[1] "Converged in 1000 iterations."
DIC: 2754.83 D_mean: 2667.17 D_post: 2627.34 logLik_var: 31.87
[1] "Converged in 1000 iterations."
DIC: 1214.07 D_mean: 999.06 D_post: 955.88 logLik_var: 64.55
[1] "Converged in 1000 iterations."
DIC: 963.78 D_mean: 776.69 D_post: 739.85 logLik_var: 55.98
[1] "Converged in 1000 iterations."
DIC: 1149.46 D_mean: 930.76 D_post: 880.97 logLik_var: 67.12
[1] "Converged in 1000 iterations."
DIC: 2227.78 D_mean: 2081.02 D_post: 2035.39 logLik_var: 48.1
[1] "Converged in 1000 iterations."
DIC: 864.48 D_mean: 627.7 D_post: 563.86 logLik_var: 75.16
[1] "Converged in 1000 iterations."
DIC: 1286.73 D_mean: 1108.39 D_post: 1050.02 logLik_var: 59.18
[1] "Converged in 1000 iterations."
DIC: 1676.74 D_mean: 1539.72 D_post: 1495.69 logLik_var: 45.26
[1] "Converged in 1000 iterations."
DIC: 1490.11 D_mean: 1227.62 D_post: 1173.54 logLik_var: 79.14
[1] "Converged in 1000 iterations."
DIC: 1431.21 D_mean: 1171.97 D_post: 1104.25 logLik_var: 81.74
[1] "Converged in 1000 iterations."
DIC: 1327.59 D_mean: 1162.74 D_post: 1102.03 logLik_var: 56.39
[1] "Converged in 1000 iterations."
DIC: 1198.04 D_mean: 988.16 D_post: 937.73 logLik_var: 65.08
[1] "Converged in 1000 iterations."
DIC: 1624.85 D_mean: 1463.43 D_post: 1415.09 logLik_var: 52.44
[1] "Converged in 1000 iterations."
DIC: 787.78 D_mean: 603.04 D_post: 569.09 logLik_var: 54.67
[1] "Converged in 1000 iterations."
DIC: 1318.24 D_mean: 1195.77 D_post: 1158.27 logLik_var: 39.99
[1] "Converged in 1000 iterations."
DIC: 1130.64 D_mean: 964.15 D_post: 932.99 logLik_var: 49.41
[1] "Converged in 1000 iterations."
DIC: 1120.76 D_mean: 889.99 D_post: 849.37 logLik_var: 67.85
[1] "Converged in 1000 iterations."
DIC: 1126.43 D_mean: 954.74 D_post: 901.18 logLik_var: 56.31
[1] "Converged in 1000 iterations."
DIC: 1301.34 D_mean: 1108.26 D_post: 1057.98 logLik_var: 60.84
[1] "Converged in 1000 iterations."
DIC: 929.66 D_mean: 791.88 D_post: 745.94 logLik_var: 45.93
[1] "Converged in 1000 iterations."
DIC: 1642.32 D_mean: 1498.98 D_post: 1439.9 logLik_var: 50.61
[1] "Converged in 1000 iterations."
DIC: 1171.71 D_mean: 1016.05 D_post: 969.91 logLik_var: 50.45
[1] "Converged in 1000 iterations."
DIC: 1052.97 D_mean: 886.87 D_post: 837.38 logLik_var: 53.9
[1] "Converged in 1000 iterations."
DIC: 896.81 D_mean: 672.7 D_post: 622.36 logLik_var: 68.61
[1] "Converged in 1000 iterations."
DIC: 1919.07 D_mean: 1807.48 D_post: 1775.08 logLik_var: 36
[1] "Converged in 1000 iterations."
DIC: 1170.17 D_mean: 845.17 D_post: 772.72 logLik_var: 99.36
[1] "Converged in 1000 iterations."
DIC: 1637.76 D_mean: 1415.68 D_post: 1354.64 logLik_var: 70.78
[1] "Converged in 1000 iterations."
DIC: 1595.7 D_mean: 1460.67 D_post: 1425.74 logLik_var: 42.49
[1] "Converged in 1000 iterations."
DIC: 1335.44 D_mean: 1236.2 D_post: 1205.98 logLik_var: 32.36
[1] "Converged in 1000 iterations."
DIC: 1324.54 D_mean: 1181.82 D_post: 1121.74 logLik_var: 50.7
[1] "Converged in 1000 iterations."
DIC: 1188.96 D_mean: 976.91 D_post: 923.92 logLik_var: 66.26
[1] "Converged in 1000 iterations."
DIC: 1190.58 D_mean: 1043 D_post: 998.94 logLik_var: 47.91
[1] "Converged in 1000 iterations."
DIC: 1441.88 D_mean: 1326.25 D_post: 1278.97 logLik_var: 40.73
[1] "Converged in 1000 iterations."
DIC: 838.52 D_mean: 600.06 D_post: 556.02 logLik_var: 70.63
[1] "Converged in 1000 iterations."
DIC: 873.54 D_mean: 599.58 D_post: 544.01 logLik_var: 82.38
[1] "Converged in 1000 iterations."
DIC: 1140.42 D_mean: 785.09 D_post: 753.47 logLik_var: 96.74
[1] "Converged in 1000 iterations."
DIC: 1022.09 D_mean: 789.35 D_post: 777.38 logLik_var: 61.18
[1] "Converged in 1000 iterations."
DIC: 1014.24 D_mean: 736.83 D_post: 733.1 logLik_var: 70.28
[1] "Converged in 1000 iterations."
DIC: 2078.58 D_mean: 1926.35 D_post: 1894.61 logLik_var: 45.99
[1] "Converged in 1000 iterations."
DIC: 666.37 D_mean: 432.98 D_post: 423.71 logLik_var: 60.66
[1] "Converged in 1000 iterations."
DIC: 799.54 D_mean: 615.61 D_post: 604.6 logLik_var: 48.74
[1] "Converged in 1000 iterations."
DIC: 660.36 D_mean: 436.62 D_post: 412.91 logLik_var: 61.86
[1] "Converged in 1000 iterations."
DIC: 1189.88 D_mean: 696.57 D_post: 703.89 logLik_var: 121.5
[1] "Converged in 1000 iterations."
DIC: 735.05 D_mean: 414.36 D_post: 416.08 logLik_var: 79.74
[1] "Converged in 1000 iterations."
DIC: 726.59 D_mean: 508.82 D_post: 488.26 logLik_var: 59.58
[1] "Converged in 1000 iterations."
DIC: 1156.12 D_mean: 932.78 D_post: 893.96 logLik_var: 65.54
[1] "Converged in 1000 iterations."
DIC: 1150.65 D_mean: 682.89 D_post: 651.03 logLik_var: 124.9
[1] "Converged in 1000 iterations."
DIC: 624.31 D_mean: 351.01 D_post: 330.23 logLik_var: 73.52
[1] "Converged in 1000 iterations."
DIC: 788.85 D_mean: 462.89 D_post: 420.48 logLik_var: 92.09
[1] "Converged in 1000 iterations."
DIC: 662.69 D_mean: 401.54 D_post: 384.56 logLik_var: 69.53
[1] "Converged in 1000 iterations."
DIC: 946.87 D_mean: 685.68 D_post: 646.35 logLik_var: 75.13
[1] "Converged in 1000 iterations."
DIC: 869.16 D_mean: 702.38 D_post: 697.14 logLik_var: 43.01
[1] "Converged in 1000 iterations."
DIC: 989.89 D_mean: 706.93 D_post: 671.88 logLik_var: 79.5
[1] "Converged in 1000 iterations."
DIC: 1183.23 D_mean: 857.64 D_post: 814.32 logLik_var: 92.23
[1] "Converged in 1000 iterations."
DIC: 802.72 D_mean: 559 D_post: 512.41 logLik_var: 72.58
[1] "Converged in 1000 iterations."
DIC: 1051.72 D_mean: 785.48 D_post: 766.82 logLik_var: 71.23
[1] "Converged in 1000 iterations."
DIC: 662.87 D_mean: 467.24 D_post: 438.61 logLik_var: 56.06
[1] "Converged in 1000 iterations."
DIC: 832.89 D_mean: 470.79 D_post: 444.84 logLik_var: 97.01
[1] "Converged in 1000 iterations."
DIC: 1198.84 D_mean: 921.5 D_post: 903.71 logLik_var: 73.78
[1] "Converged in 1000 iterations."
DIC: 2036.48 D_mean: 1885.66 D_post: 1852.09 logLik_var: 46.1
[1] "Converged in 1000 iterations."
DIC: 963.74 D_mean: 720.25 D_post: 682.67 logLik_var: 70.27
[1] "Converged in 1000 iterations."
DIC: 864.13 D_mean: 526.98 D_post: 486.57 logLik_var: 94.39
[1] "Converged in 1000 iterations."
DIC: 1188.61 D_mean: 943.51 D_post: 915.17 logLik_var: 68.36
[1] "Converged in 1000 iterations."
DIC: 911.31 D_mean: 544.23 D_post: 505.77 logLik_var: 101.39
[1] "Converged in 1000 iterations."
DIC: 959.03 D_mean: 698.76 D_post: 648.6 logLik_var: 77.61
[1] "Converged in 1000 iterations."
DIC: 1118.45 D_mean: 846.3 D_post: 838.95 logLik_var: 69.88
[1] "Converged in 1000 iterations."
DIC: 970.23 D_mean: 767.33 D_post: 736.35 logLik_var: 58.47
[1] "Converged in 1000 iterations."
DIC: 702.36 D_mean: 402.36 D_post: 385.44 logLik_var: 79.23
[1] "Converged in 1000 iterations."
DIC: 963 D_mean: 715.25 D_post: 660.86 logLik_var: 75.53
[1] "Converged in 1000 iterations."
DIC: 2258.97 D_mean: 2158.45 D_post: 2121.46 logLik_var: 34.38
[1] "Converged in 1000 iterations."
DIC: 1591.9 D_mean: 1277.53 D_post: 1279.99 logLik_var: 77.98
[1] "Converged in 1000 iterations."
DIC: 1108.82 D_mean: 747.36 D_post: 734.25 logLik_var: 93.64
[1] "Converged in 1000 iterations."
DIC: 1026.32 D_mean: 759.67 D_post: 730.58 logLik_var: 73.94
[1] "Converged in 1000 iterations."
DIC: 1344.08 D_mean: 1157.45 D_post: 1130.78 logLik_var: 53.33
[1] "Converged in 1000 iterations."
DIC: 751.22 D_mean: 539.7 D_post: 524.66 logLik_var: 56.64
[1] "Converged in 1000 iterations."
DIC: 1836.27 D_mean: 1679.25 D_post: 1615.37 logLik_var: 55.22
[1] "Converged in 1000 iterations."
DIC: 1625.75 D_mean: 1423.05 D_post: 1357.53 logLik_var: 67.06
[1] "Converged in 1000 iterations."
DIC: 787.22 D_mean: 523.53 D_post: 471.57 logLik_var: 78.91
[1] "Converged in 1000 iterations."
DIC: 936.31 D_mean: 736.68 D_post: 670.64 logLik_var: 66.42
[1] "Converged in 1000 iterations."
DIC: 1135.29 D_mean: 886.73 D_post: 828.13 logLik_var: 76.79
[1] "Converged in 1000 iterations."
DIC: 1012.33 D_mean: 838.78 D_post: 793.98 logLik_var: 54.59
[1] "Converged in 1000 iterations."
DIC: 1147.23 D_mean: 954.75 D_post: 917.09 logLik_var: 57.53
[1] "Converged in 1000 iterations."
DIC: 856.52 D_mean: 628.11 D_post: 580.44 logLik_var: 69.02
[1] "Converged in 1000 iterations."
DIC: 1563.71 D_mean: 1369 D_post: 1332.75 logLik_var: 57.74
[1] "Converged in 1000 iterations."
DIC: 1497.67 D_mean: 1284.79 D_post: 1232.73 logLik_var: 66.23
[1] "Converged in 1000 iterations."
DIC: 1824.52 D_mean: 1584.54 D_post: 1513.13 logLik_var: 77.85
[1] "Converged in 1000 iterations."
DIC: 1254.06 D_mean: 987.99 D_post: 926.2 logLik_var: 81.97
[1] "Converged in 1000 iterations."
DIC: 1296.68 D_mean: 1046.06 D_post: 975.25 logLik_var: 80.36
[1] "Converged in 1000 iterations."
DIC: 1151.55 D_mean: 750.78 D_post: 700.08 logLik_var: 112.87
[1] "Converged in 1000 iterations."
DIC: 1085.41 D_mean: 838.93 D_post: 785.52 logLik_var: 74.97
[1] "Converged in 1000 iterations."
DIC: 1571.33 D_mean: 1243.23 D_post: 1191.46 logLik_var: 94.97
[1] "Converged in 1000 iterations."
DIC: 1186.28 D_mean: 1001.41 D_post: 940.65 logLik_var: 61.41
[1] "Converged in 1000 iterations."
DIC: 940.52 D_mean: 706.24 D_post: 652.24 logLik_var: 72.07
[1] "Converged in 1000 iterations."
DIC: 725.26 D_mean: 460.8 D_post: 417.1 logLik_var: 77.04
[1] "Converged in 1000 iterations."
DIC: 1616.19 D_mean: 1339.13 D_post: 1290.68 logLik_var: 81.38
[1] "Converged in 1000 iterations."
DIC: 1199.45 D_mean: 963.53 D_post: 914.15 logLik_var: 71.32
[1] "Converged in 1000 iterations."
DIC: 1202.16 D_mean: 1009.92 D_post: 957.64 logLik_var: 61.13
[1] "Converged in 1000 iterations."
DIC: 1659.11 D_mean: 1448.85 D_post: 1395.41 logLik_var: 65.93
[1] "Converged in 1000 iterations."
DIC: 1425.52 D_mean: 1079.24 D_post: 1014.9 logLik_var: 102.66
[1] "Converged in 1000 iterations."
DIC: 1446.55 D_mean: 1308.1 D_post: 1243.92 logLik_var: 50.66
[1] "Converged in 1000 iterations."
DIC: 1173.16 D_mean: 950.74 D_post: 897.79 logLik_var: 68.84
[1] "Converged in 1000 iterations."
DIC: 1559.48 D_mean: 1327.72 D_post: 1270.67 logLik_var: 72.2
[1] "Converged in 1000 iterations."
DIC: 1405.72 D_mean: 1248.78 D_post: 1191.8 logLik_var: 53.48
[1] "Converged in 1000 iterations."
DIC: 953.4 D_mean: 759.09 D_post: 707.54 logLik_var: 61.47
[1] "Converged in 1000 iterations."
DIC: 1335.31 D_mean: 927.68 D_post: 909.07 logLik_var: 106.56
[1] "Converged in 1000 iterations."
DIC: 2076.75 D_mean: 1977.35 D_post: 1926.67 logLik_var: 37.52
[1] "Converged in 1000 iterations."
DIC: 1211.61 D_mean: 1000.22 D_post: 932.02 logLik_var: 69.9
[1] "Converged in 1000 iterations."
DIC: 1700.32 D_mean: 1523.97 D_post: 1457.82 logLik_var: 60.62
[1] "Converged in 1000 iterations."
DIC: 1549.61 D_mean: 1275.6 D_post: 1217.97 logLik_var: 82.91
[1] "Converged in 1000 iterations."
DIC: 1485.33 D_mean: 1307.65 D_post: 1265.62 logLik_var: 54.93
[1] "Converged in 1000 iterations."
DIC: 1668.15 D_mean: 1479.63 D_post: 1414.53 logLik_var: 63.41
[1] "Converged in 1000 iterations."
DIC: 1521.99 D_mean: 1256.56 D_post: 1210.32 logLik_var: 77.92
[1] "Converged in 1000 iterations."
DIC: 1463.14 D_mean: 1199.34 D_post: 1134.29 logLik_var: 82.21
[1] "Converged in 1000 iterations."
DIC: 1526.44 D_mean: 1355.73 D_post: 1298.94 logLik_var: 56.88
[1] "Converged in 1000 iterations."
DIC: 1646.9 D_mean: 1484.65 D_post: 1424.3 logLik_var: 55.65
[1] "Converged in 1000 iterations."
DIC: 1982.99 D_mean: 1803.49 D_post: 1749.46 logLik_var: 58.38
[1] "Converged in 1000 iterations."
DIC: 1618.05 D_mean: 1443.1 D_post: 1393.84 logLik_var: 56.05
[1] "Converged in 1000 iterations."
DIC: 2093.12 D_mean: 1816.83 D_post: 1750.12 logLik_var: 85.75
[1] "Converged in 1000 iterations."
DIC: 1408.46 D_mean: 1183.27 D_post: 1103.01 logLik_var: 76.36
[1] "Converged in 1000 iterations."
DIC: 1555.9 D_mean: 1356.65 D_post: 1289.13 logLik_var: 66.69
[1] "Converged in 1000 iterations."
DIC: 1534.21 D_mean: 1369.88 D_post: 1311.17 logLik_var: 55.76
[1] "Converged in 1000 iterations."
DIC: 724.88 D_mean: 464.77 D_post: 415.55 logLik_var: 77.33
[1] "Converged in 1000 iterations."
DIC: 1896.64 D_mean: 1764.84 D_post: 1719.23 logLik_var: 44.35
[1] "Converged in 1000 iterations."
DIC: 971.14 D_mean: 638.08 D_post: 581.49 logLik_var: 97.41
[1] "Converged in 1000 iterations."
DIC: 1259.71 D_mean: 1045.15 D_post: 993.34 logLik_var: 66.59
[1] "Converged in 1000 iterations."
DIC: 1687.46 D_mean: 1508.71 D_post: 1453.97 logLik_var: 58.37
[1] "Converged in 1000 iterations."
DIC: 2422.04 D_mean: 2310.39 D_post: 2259.95 logLik_var: 40.52
[1] "Converged in 1000 iterations."
DIC: 1604.02 D_mean: 1448.25 D_post: 1403.84 logLik_var: 50.04
[1] "Converged in 1000 iterations."
DIC: 989.69 D_mean: 653.23 D_post: 595.22 logLik_var: 98.62
[1] "Converged in 1000 iterations."
DIC: 2454.36 D_mean: 2293.78 D_post: 2226.65 logLik_var: 56.93
[1] "Converged in 1000 iterations."
DIC: 1937.64 D_mean: 1784.73 D_post: 1732.26 logLik_var: 51.34
[1] "Converged in 1000 iterations."
DIC: 1022.29 D_mean: 849.12 D_post: 796.1 logLik_var: 56.55
[1] "Converged in 1000 iterations."
DIC: 1277.07 D_mean: 1055.69 D_post: 982.96 logLik_var: 73.53
[1] "Converged in 1000 iterations."
DIC: 1080.17 D_mean: 910.19 D_post: 863.64 logLik_var: 54.13
[1] "Converged in 1000 iterations."
DIC: 1436.23 D_mean: 1166.29 D_post: 1117.01 logLik_var: 79.81
[1] "Converged in 1000 iterations."
DIC: 1549.72 D_mean: 1339.88 D_post: 1286.41 logLik_var: 65.83
[1] "Converged in 5200 iterations."
DIC: 1696.41 D_mean: 1501.29 D_post: 1435.66 logLik_var: 65.19
[1] "Converged in 1000 iterations."
DIC: 1505.95 D_mean: 1237.33 D_post: 1171.72 logLik_var: 83.56
[1] "Converged in 1000 iterations."
DIC: 1329.6 D_mean: 1139.74 D_post: 1081.49 logLik_var: 62.03
[1] "Converged in 1000 iterations."
DIC: 1390.97 D_mean: 1182.14 D_post: 1123.31 logLik_var: 66.92
[1] "Converged in 1000 iterations."
DIC: 1031.23 D_mean: 816.46 D_post: 762.41 logLik_var: 67.21
[1] "Converged in 1000 iterations."
DIC: 793.98 D_mean: 543.02 D_post: 493.79 logLik_var: 75.05
[1] "Converged in 1000 iterations."
DIC: 1105.41 D_mean: 867.34 D_post: 801.43 logLik_var: 76
[1] "Converged in 1000 iterations."
DIC: 1735.74 D_mean: 1566.08 D_post: 1509.61 logLik_var: 56.53
[1] "Converged in 1000 iterations."
DIC: 1403.36 D_mean: 1245.67 D_post: 1190.07 logLik_var: 53.32
[1] "Converged in 1000 iterations."
DIC: 1003.73 D_mean: 780.82 D_post: 732.48 logLik_var: 67.81
[1] "Converged in 1000 iterations."
DIC: 1336.98 D_mean: 1178.59 D_post: 1109.79 logLik_var: 56.8
[1] "Converged in 1000 iterations."
DIC: 1642.39 D_mean: 1456.79 D_post: 1399.93 logLik_var: 60.61
[1] "Converged in 1000 iterations."
DIC: 1985.51 D_mean: 1805.81 D_post: 1750.14 logLik_var: 58.84
[1] "Converged in 1000 iterations."
DIC: 1838.96 D_mean: 1632.68 D_post: 1565.11 logLik_var: 68.46
[1] "Converged in 1000 iterations."
DIC: 1870.19 D_mean: 1754.24 D_post: 1699.13 logLik_var: 42.77
[1] "Converged in 1000 iterations."
DIC: 1626.07 D_mean: 1484.85 D_post: 1412.52 logLik_var: 53.39
[1] "Converged in 1000 iterations."
DIC: 1295.19 D_mean: 1078.8 D_post: 1010.4 logLik_var: 71.2
[1] "Converged in 1000 iterations."
DIC: 1266.01 D_mean: 1082.68 D_post: 1031.23 logLik_var: 58.69
[1] "Converged in 1000 iterations."
DIC: 2256.97 D_mean: 2119.95 D_post: 2071.03 logLik_var: 46.48
set.seed(1)
PCAU_curve_list <- list()
simu_data <- simulate_joint(Config_all, simu_input$D, n_clone = 4,
mut_size = 10, missing = 0.8,
error_mean = c(0.01, 0.44), n_repeat = 20)
assign_0 <- matrix(0, nrow = 200, ncol = 20)
assign_1 <- matrix(0, nrow = 200, ncol = 20)
prob_all <- matrix(0, nrow = 200, ncol = 20)
for (i in seq_len(length(simu_data))) {
d_tmp <- simu_data[[i]]
prob_tmp <- clone_id_Gibbs(d_tmp$A_sim, d_tmp$D_sim,
d_tmp$Config, Psi = NULL,
min_iter = 1000, wise = "variant",
prior1 = c(2.11, 2.69), verbose = FALSE)$prob
assign_0[, i] <- get_prob_label(d_tmp$I_sim)
assign_1[, i] <- get_prob_label(prob_tmp)
prob_all[, i] <- get_prob_value(prob_tmp, mode = "best")
}
[1] "Converged in 1000 iterations."
DIC: 2020.8 D_mean: 1826.43 D_post: 1771.07 logLik_var: 62.43
[1] "Converged in 1000 iterations."
DIC: 802.56 D_mean: 664.85 D_post: 620.22 logLik_var: 45.59
[1] "Converged in 1000 iterations."
DIC: 1578.7 D_mean: 1453.28 D_post: 1415.22 logLik_var: 40.87
[1] "Converged in 1000 iterations."
DIC: 1218.07 D_mean: 968.77 D_post: 896.82 logLik_var: 80.31
[1] "Converged in 1000 iterations."
DIC: 1187.43 D_mean: 963.75 D_post: 906.39 logLik_var: 70.26
[1] "Converged in 1000 iterations."
DIC: 978.49 D_mean: 763.52 D_post: 713.68 logLik_var: 66.2
[1] "Converged in 1000 iterations."
DIC: 990.59 D_mean: 756.49 D_post: 693.45 logLik_var: 74.29
[1] "Converged in 1000 iterations."
DIC: 1384.21 D_mean: 1173.51 D_post: 1104.69 logLik_var: 69.88
[1] "Converged in 1000 iterations."
DIC: 1863.79 D_mean: 1593.33 D_post: 1524.09 logLik_var: 84.92
[1] "Converged in 1000 iterations."
DIC: 1503.52 D_mean: 1363.23 D_post: 1318.5 logLik_var: 46.25
[1] "Converged in 1000 iterations."
DIC: 2126.13 D_mean: 1985.34 D_post: 1938.08 logLik_var: 47.01
[1] "Converged in 1000 iterations."
DIC: 911.18 D_mean: 708.18 D_post: 644.21 logLik_var: 66.74
[1] "Converged in 1000 iterations."
DIC: 1589.46 D_mean: 1376.93 D_post: 1309.46 logLik_var: 70
[1] "Converged in 1000 iterations."
DIC: 1397.55 D_mean: 1191.66 D_post: 1151.66 logLik_var: 61.47
[1] "Converged in 1000 iterations."
DIC: 1267.35 D_mean: 1021.46 D_post: 958.24 logLik_var: 77.28
[1] "Converged in 1000 iterations."
DIC: 1465.81 D_mean: 1150.78 D_post: 1085.73 logLik_var: 95.02
[1] "Converged in 1000 iterations."
DIC: 1458.1 D_mean: 1299.93 D_post: 1242.15 logLik_var: 53.99
[1] "Converged in 1000 iterations."
DIC: 1659.78 D_mean: 1435.38 D_post: 1359.15 logLik_var: 75.16
[1] "Converged in 1000 iterations."
DIC: 1160.61 D_mean: 960.21 D_post: 911.24 logLik_var: 62.34
[1] "Converged in 1000 iterations."
DIC: 856.3 D_mean: 547.21 D_post: 479.46 logLik_var: 94.21
dat_dir = "data/simulations"
rds.tmp <- readRDS(paste0(dat_dir, "/simulate_prob_curve.rds"))
assign_0 <- rds.tmp$assign_0
assign_1 <- rds.tmp$assign_1
prob_all <- rds.tmp$prob_all
thresholds <- seq(0, 1, 0.001)
recalls <- rep(0, length(thresholds))
precision_all <- matrix(0, nrow = length(thresholds), ncol = ncol(prob_all) + 1)
for (i in seq_len(length(thresholds))) {
idx <- prob_all >= thresholds[i]
recalls[i] <- mean(idx)
precision_all[i, ncol(prob_all) + 1] <- mean((assign_0 == assign_1)[idx])
for (j in seq_len(ncol(prob_all))) {
idx <- prob_all[, j] >= sort(prob_all[, j],
decreasing = TRUE)[round(recalls[i] *
nrow(prob_all))]
precision_all[i, j] <- mean((assign_0[,j] == assign_1[,j])[idx])
}
}
order_idx <- order(colMeans(precision_all[, 1:ncol(prob_all)]))
idx1 <- order_idx[round(0.25 * length(order_idx))]
idx2 <- order_idx[round(0.75 * length(order_idx))]
df.tmp <- data.frame(cutoff = thresholds, Recall = recalls,
Presision = precision_all[, ncol(precision_all)],
ACC_low1 = precision_all[, idx1],
ACC_high1 = precision_all[, idx2])
Calculate AUC score.
nn <- ncol(precision_all)
AUC_score <- 0.0
for (i in seq_len(length(recalls) - 1)) {
AUC_score <- AUC_score + 0.5 * (recalls[i] - recalls[i + 1]) *
(precision_all[i, nn] + precision_all[i + 1, nn])
}
AUC_score <- AUC_score / (recalls[1] - recalls[length(recalls)])
print(AUC_score)
[1] 0.9468138
Plot PR curve.
fig_dir <- "figures/simulations"
idx_05 <- prob_all >= 0.5
recall_05 <- mean(idx_05)
precision_05 <- mean((assign_0 == assign_1)[idx_05])
print(c(recall_05, precision_05))
[1] 0.8450000 0.8656805
fig.curve <- ggplot(df.tmp, aes(x = Recall, y = Presision)) +
geom_ribbon(aes(ymin = ACC_low1, ymax = ACC_high1), fill = "grey80") +
scale_color_viridis(option = "B") +
geom_line(aes(color = cutoff)) + geom_point(aes(color = cutoff), size = 0.5) +
geom_point(aes(x = recall_05, y = precision_05), shape = 1, color = "black",
size = 3) +
xlab("Recall: Fraction of assignable cells") +
ylab("Precision") +
ylim(0.5, 1) +
pub.theme() +
theme(legend.position = c(0.25,0.45)) +
labs(color = 'highest P')
ggsave(paste0(fig_dir, "/fig1b_PRcurve.png"),
fig.curve, height = 2.5, width = 3.5, dpi = 300)
ggsave(paste0(fig_dir, "/fig1b_PRcurve.pdf"),
fig.curve, height = 2.5, width = 3.5, dpi = 300)
fig.curve
Version | Author | Date |
---|---|---|
9ec2a59 | davismcc | 2018-08-26 |
Assess cardelino with simulated data.
df <- readRDS(paste0("data/simulations/simulate_extra_s1_v2.rds"))
## Change method names
df$Methods <- as.character(df$Methods)
df <- df[df$Methods != "Bern_EM", ]
df$Methods[df$Methods == "demuxlet"] <- "theta_fixed"
df$Methods[df$Methods == "Binom_EM"] <- "theta_EM"
df$Methods[df$Methods == "Binom_Gibbs"] <- "cardelino"
df$Methods <- as.factor(df$Methods)
## Change variables
df$labels[df$variable == "missing"] <- 1 - df$labels[df$variable == "missing"]
df$labels[df$variable == "shapes1"] <- 1 / df$labels[df$variable == "shapes1"] #paste0("1/", df$labels[df$variable == "shapes1"])
Table of results from simulations:
Accuracy AUC_of_ACC_ASS Assignable MAE Methods labels
1 0.9310345 0.9277842 0.580 0.1658004 theta_fixed 3
2 0.9206349 0.9282246 0.630 0.1592546 theta_EM 3
3 0.8778626 0.9204117 0.655 0.1566119 cardelino 3
4 0.8305085 0.8591135 0.590 0.2019469 theta_fixed 3
5 0.8166667 0.8572444 0.600 0.2014505 theta_EM 3
6 0.8474576 0.8854308 0.590 0.1909672 cardelino 3
variable
1 mut_size
2 mut_size
3 mut_size
4 mut_size
5 mut_size
6 mut_size
Area under Precision-Recall curves for different parameter settings.
fig_dir <- "figures/simulations/"
df1 <- df[df$Methods == "cardelino", ]
type_use <- c("mut_size", "n_clone", "missing", "FNR", "shapes1")
xlabels <- c("# variants per clonal branch", "Number of clones",
"Overall variant coverage")
titles <- c("Mutations per branch", "Numbers of clones", "Variant coverages",
"Fraction of ALT allele", "Constrentration of allelic expr")
df1 <- df1[df1$labels != 40, ]
df1 <- df1[df1$labels != 0.65, ]
df1 <- df1[df1$labels != 0.60, ]
fig_list <- list()
for (mm in c(1,3)) {
fig_list[[mm]] <- ggplot(df1[df1$variable == type_use[mm], ],
aes(x = as.factor(labels), y = AUC_of_ACC_ASS)) +
ylab("AU precision-recall curve") +
geom_boxplot() + xlab(xlabels[mm]) +
ylim(0.6, 1) + pub.theme()
# if (mm == 1) {
# fig_list[[mm]] <- fig_list[[mm]] +
# scale_x_discrete(labels=c("3", "5", "7", "10*", "15", "15")) }
# if (mm == 3) {
# fig_list[[mm]] <- fig_list[[mm]] +
# scale_x_discrete(labels=c("0.05", "0.1", "0.15", "0.2*", "0.25", "0.3")) }
}
ggsave(file = paste0(fig_dir, "fig_1c_mutations.png"),
fig_list[[1]], height = 2.5, width = 3.5, dpi = 300)
ggsave(file = paste0(fig_dir, "fig_1c_mutations.pdf"),
fig_list[[1]], height = 2.5, width = 3.5, dpi = 300)
ggsave(file = paste0(fig_dir, "fig_1d_coverages.png"),
fig_list[[3]], height = 2.5, width = 3.5, dpi = 300)
ggsave(file = paste0(fig_dir, "fig_1d_coverages.pdf"),
fig_list[[3]], height = 2.5, width = 3.5, dpi = 300)
fig_list[[1]]
Version | Author | Date |
---|---|---|
9ec2a59 | davismcc | 2018-08-26 |
Version | Author | Date |
---|---|---|
9ec2a59 | davismcc | 2018-08-26 |
Boxplots comparing different models for cell-clone assignment.
type_use <- c("mut_size", "n_clone", "missing", "FNR", "shapes1")
xlabels <- c("# variants per clonal branch", "Number of clones",
"Overall variant coverage",
"Mean fraction of ALT alleles", "Variance of allelic imbalance")
df$Methods <- ordered(df$Methods, levels=c("theta_fixed", "theta_EM",
"cardelino")) #Bern_EM
xlabel_list <- list(c("3", "5", "7", "10*", "15", "15"),
c("3", "4*", "5", "6", "7", "8"),
c("0.05", "0.1", "0.15", "0.2*", "0.25", "0.3"),
c("0.35", "0.4", "* 0.45", "0.5", "0.55", "0.6"),
c("1/16", "1/8", "* 1/4", "1/2", "1", "2"))
fig_list <- list()
for (mm in seq_len(length(type_use))) {
fig_list[[mm]] <- ggplot(df[df$variable == type_use[mm], ],
aes(x = as.factor(labels), y = AUC_of_ACC_ASS,
fill=Methods)) +
geom_boxplot() + xlab(xlabels[mm]) + ylab("AUC: precision-recall curve") +
pub.theme() +
scale_fill_brewer() +
scale_x_discrete(labels=xlabel_list[[mm]])
}
fig_box <- ggarrange(fig_list[[1]], fig_list[[3]], fig_list[[2]],
fig_list[[4]], fig_list[[5]],
labels = c("A", "B", "C", "D", "E"),
nrow = 2, ncol = 3, align = "hv",
common.legend = TRUE, legend = "bottom")
ggsave(file = paste0(fig_dir, "simulation_overall_AUC.png"),
fig_box, height = 7, width = 12, dpi = 300)
ggsave(file = paste0(fig_dir, "simulation_overall_AUC.pdf"),
fig_box, height = 7, width = 12, dpi = 300)
fig_box
Version | Author | Date |
---|---|---|
9ec2a59 | davismcc | 2018-08-26 |
all_files <- paste0(lines, ".simulate.rds")
assign_0 <- matrix(0, nrow = 500, ncol = length(lines))
assign_1 <- matrix(0, nrow = 500, ncol = length(lines))
prob_all <- matrix(0, nrow = 500, ncol = length(lines))
for (i in seq_len(length(all_files))) {
afile <- all_files[i]
sim_dat <- readRDS(file.path("data", "simulations", afile))
assign_0[, i] <- get_prob_label(sim_dat$I_sim)
assign_1[, i] <- get_prob_label(sim_dat$prob_Gibbs)
prob_all[, i] <- get_prob_value(sim_dat$prob_Gibbs, mode = "best")
}
all_files <- paste0("cardelino_results.", lines,
".filt_lenient.cell_coverage_sites.rds")
n_sites <- rep(0, length(lines))
n_clone <- rep(0, length(lines))
recall_all <- rep(0, length(lines))
for (i in seq_len(length(all_files))) {
afile <- all_files[i]
carde_dat <- readRDS(file.path("data", "cell_assignment", afile))
n_sites[i] <- nrow(carde_dat$D)
n_clone[i] <- ncol(carde_dat$prob_mat)
recall_all[i] <- mean(get_prob_value(carde_dat$prob_mat, mode = "best") > 0.5)
}
all_files_carderelax <- paste0("cardelino_results_carderelax.", lines,
".filt_lenient.cell_coverage_sites.rds")
recall_all_carderelax <- rep(0, length(lines))
for (i in seq_len(length(all_files))) {
afile <- all_files_carderelax[i]
carde_dat <- readRDS(file.path("data", "cell_assignment", afile))
recall_all_carderelax[i] <- mean(get_prob_value(carde_dat$prob_mat, mode = "best") > 0.5)
}
Overall correlation in assignment rates (recall) from simulated and observed data is 0.959.
precision_simu <- rep(0, length(lines))
for (i in seq_len(length(lines))) {
idx <- prob_all[, i] > 0.5
precision_simu[i] <- mean(assign_0[idx, i] == assign_1[idx, i])
}
df <- data.frame(line = lines, n_sites = n_sites, n_clone = n_clone,
recall_real_cardeorig = recall_all,
recall_real_carderelax = recall_all_carderelax,
recall_simu = colMeans(prob_all > 0.5),
precision_simu = precision_simu)
p <- df %>%
dplyr::mutate(labs = ifelse(line == "joxm", "joxm", "")) %>%
dplyr::mutate(sites_per_clone = cut(n_sites / pmax(n_clone - 1, 1),
breaks = c(0, 3, 8, 15, 25, 60))) %>%
ggplot(
aes(x = recall_simu, y = recall_real_cardeorig,
fill = sites_per_clone)) +
geom_abline(slope = 1, intercept = 0, colour = "gray40", linetype = 2) +
geom_smooth(aes(group = 1), method = "lm", colour = "firebrick") +
geom_label_repel(aes(label = labs), fill = "gray90", size = 4,
nudge_x = 0.05, nudge_y = -0.1) +
geom_point(size = 3, shape = 21) +
xlim(0, 1) + ylim(0, 1) +
scale_fill_manual(name = "mean\n# variants\nper clonal\nbranch",
values = magma(6)[-1]) +
guides(colour = FALSE, group = FALSE) +
xlab("Assignment rate: simulated") +
ylab("Assignment rate: observed") +
theme_cowplot(font_size = 16)
ggsave("figures/simulations/assign_rate_obs_v_sim.png", plot = p,
height = 4.5, width = 5)
ggsave("figures/simulations/assign_rate_obs_v_sim.pdf", plot = p,
height = 4.5, width = 5)
ggsave("figures/simulations/assign_rate_obs_v_sim_wide.png", plot = p,
height = 4.5, width = 6.5)
ggsave("figures/simulations/assign_rate_obs_v_sim_wide.pdf", plot = p,
height = 4.5, width = 6.5)
p
Version | Author | Date |
---|---|---|
9ec2a59 | davismcc | 2018-08-26 |
ggsave("figures/simulations/assign_rate_obs_v_sim_skinny.png",
plot = p + theme(legend.position = "bottom"),
height = 5.5, width = 5)
ggsave("figures/simulations/assign_rate_obs_v_sim_skinny.pdf",
plot = p + theme(legend.position = "bottom"),
height = 5.5, width = 5)
summary(lm(recall_real_cardeorig ~ recall_simu, data = df))
Call:
lm(formula = recall_real_cardeorig ~ recall_simu, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.177532 -0.005110 0.008029 0.010426 0.160930
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.02246 0.04634 0.485 0.631
recall_simu 0.96951 0.05262 18.424 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06609 on 30 degrees of freedom
Multiple R-squared: 0.9188, Adjusted R-squared: 0.9161
F-statistic: 339.4 on 1 and 30 DF, p-value: < 2.2e-16
pp <- df %>%
dplyr::mutate(labs = ifelse(line == "joxm", "joxm", "")) %>%
dplyr::mutate(sites_per_clone = cut(n_sites / pmax(n_clone - 1, 1),
breaks = c(0, 3, 8, 15, 25, 60))) %>%
ggplot(
aes(x = recall_simu, y = recall_real_carderelax,
fill = sites_per_clone)) +
geom_abline(slope = 1, intercept = 0, colour = "gray40", linetype = 2) +
geom_smooth(aes(group = 1), method = "lm", colour = "firebrick") +
geom_label_repel(aes(label = labs), fill = "gray90", size = 4,
nudge_x = 0.05, nudge_y = -0.1) +
geom_point(size = 3, shape = 21) +
xlim(0, 1) + ylim(0, 1) +
scale_fill_manual(name = "mean\n# variants\nper clonal\nbranch",
values = magma(6)[-1]) +
guides(colour = FALSE, group = FALSE) +
xlab("Assignment rate: simulated") +
ylab("Assignment rate: observed") +
theme_cowplot(font_size = 12)
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim.png", plot = pp,
height = 4.5, width = 5)
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim.pdf", plot = pp,
height = 4.5, width = 5)
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim_wide.png", plot = pp,
height = 4.5, width = 6.5)
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim_wide.pdf", plot = pp,
height = 4.5, width = 6.5)
pp
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim_skinny.png",
plot = pp + theme(legend.position = "bottom"),
height = 6.5, width = 6)
ggsave("figures/simulations/carderelax_assign_rate_obs_v_sim_skinny.pdf",
plot = pp + theme(legend.position = "bottom"),
height = 6.5, width = 6)
summary(lm(recall_real_carderelax ~ recall_simu, data = df))
Call:
lm(formula = recall_real_carderelax ~ recall_simu, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.178635 -0.018784 -0.001869 0.008904 0.278506
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.14003 0.06186 2.264 0.031 *
recall_simu 0.86183 0.07024 12.271 3.19e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08822 on 30 degrees of freedom
Multiple R-squared: 0.8339, Adjusted R-squared: 0.8283
F-statistic: 150.6 on 1 and 30 DF, p-value: 3.19e-13
df %>%
dplyr::mutate(sites_per_clone = cut(n_sites / n_clone,
breaks = c(0, 5, 10, 20, 40))) %>%
ggplot(
aes(x = recall_simu, y = precision_simu,
fill = sites_per_clone)) +
geom_hline(yintercept = 0.85, colour = "gray40", linetype = 2) +
geom_smooth(aes(group = 1), method = "lm", colour = "firebrick") +
geom_point(size = 3, shape = 21) +
xlim(0, 1) + ylim(0, 1) +
scale_fill_manual(name = "mean\n# variants\nper clone",
values = magma(5)[-1]) +
guides(colour = FALSE, group = FALSE) +
xlab("Assignment rate (recall)") +
ylab("Precision")
Table showing the number of lines with 2, 3 and 4 clones.
2 3 4
4 24 4
Summary of the average number of mutations per clonal branch across lines.
Min. 1st Qu. Median Mean 3rd Qu. Max.
3.00 8.50 11.50 18.69 25.00 57.50
─ Session info ──────────────────────────────────────────────────────────
setting value
version R version 3.6.0 (2019-04-26)
os Ubuntu 18.04.3 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2019-10-30
─ Packages ──────────────────────────────────────────────────────────────
package * version date lib
AnnotationDbi 1.46.1 2019-08-20 [1]
ape 5.3 2019-03-17 [1]
assertthat 0.2.1 2019-03-21 [1]
backports 1.1.4 2019-04-10 [1]
Biobase 2.44.0 2019-05-02 [1]
BiocGenerics 0.30.0 2019-05-02 [1]
BiocParallel 1.18.1 2019-08-06 [1]
biomaRt 2.40.4 2019-08-19 [1]
Biostrings 2.52.0 2019-05-02 [1]
bit 1.1-14 2018-05-29 [1]
bit64 0.9-7 2017-05-08 [1]
bitops 1.0-6 2013-08-17 [1]
blob 1.2.0 2019-07-09 [1]
broom 0.5.2 2019-04-07 [1]
BSgenome 1.52.0 2019-05-02 [1]
callr 3.3.2 2019-09-22 [1]
cardelino * 0.6.4 2019-08-21 [1]
cellranger 1.1.0 2016-07-27 [1]
cli 1.1.0 2019-03-19 [1]
colorspace 1.4-1 2019-03-18 [1]
cowplot * 1.0.0 2019-07-11 [1]
crayon 1.3.4 2017-09-16 [1]
DBI 1.0.0 2018-05-02 [1]
DelayedArray 0.10.0 2019-05-02 [1]
desc 1.2.0 2018-05-01 [1]
devtools 2.2.1 2019-09-24 [1]
digest 0.6.21 2019-09-20 [1]
dplyr * 0.8.3 2019-07-04 [1]
ellipsis 0.3.0 2019-09-20 [1]
evaluate 0.14 2019-05-28 [1]
forcats * 0.4.0 2019-02-17 [1]
fs 1.3.1 2019-05-06 [1]
generics 0.0.2 2018-11-29 [1]
GenomeInfoDb 1.20.0 2019-05-02 [1]
GenomeInfoDbData 1.2.1 2019-04-30 [1]
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stringr * 1.4.0 2019-02-10 [1]
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xml2 1.2.2 2019-08-09 [1]
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zeallot 0.1.0 2018-01-28 [1]
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source
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[1] /home/AD.SVI.EDU.AU/dmccarthy/R/x86_64-pc-linux-gnu-library/3.6
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library