# one split versus two splits
library(distbayesianmc)
library(dplyr)
list_params_model_onesplit <- list(scale = 10,
ssplits = 1,
typesplit = "random",
dataset = "pima",
mrep = 12,
n.mil = 2,
ncores = 4,
savestring = "")
list_params_model_multisplits1 <- list(scale = 10,
ssplits = 3,
typesplit = "strat_y_cluster",
dataset = "pima",#"",
mrep = 12,
n.mil = 2,
ncores = 4,
savestring = "test2")
f_single_run_rep_rjmcmc(list_params_model_multisplits1, 2)
res_onesplit <- f_full_run_rep_rjmcmc(list_params_model_onesplit)
save(res_onesplit, file="res_onesplit.RData")
res_multisplit1 <- f_full_run_rep_rjmcmc(list_params_model_multisplits1)
save(res_multisplit1, file="res_multisplit1.RData")
#head(res_onesplit$df_sim_res_all)
#head(res_twosplits$df_sim_res_all)
df1 <- res_multisplit1$df_sim_res_all
df_single_split <- res_onesplit$df_sim_res_all
#df2 <- res_twosplits$df_sim_res_all
#keys <- df1[df1$counter_sim==1,]$key_model
common_keys <- f_intersection_keys(list_params_model_multisplits1$ssplits, df1)#, df_single_split)
i_split <- 3
i_rep <- 4
index_M1 <- 1
index_M2 <- 2
key1 <- common_keys[index_M1,]
key2 <- common_keys[index_M2,]
log(mean( (df1 %>% filter((key_model == key1 ) & split == i_split) %>% dplyr::select(Post.Prob))[,] ) /
mean((df1 %>% filter((key_model == key2 ) & split == i_split) %>% dplyr::select(Post.Prob))[,] ) )
log( (df1 %>% filter((key_model == key1 ) & split == i_split) %>% dplyr::select(Post.Prob))/
(df1 %>% filter((key_model == key2 ) & split == i_split) %>% dplyr::select(Post.Prob)))
# find out what goes wrong here
bf_rjmcmc <- f_joint_bf_model_splits_rjmcmc(res_onesplit, res_multisplit1, key1, key2, list_params_model_multisplits1)
res_simple_comp <- f_simple_bf_two_models_all_splits(list_params_model_multisplits1, key1, key2)
estimate_single <- res_simple_comp$M1$vec_logsubpost - res_simple_comp$M2$vec_logsubpost + res_simple_comp$M1$model_prior - res_simple_comp$M2$model_prior
colMeans(res_simple_comp$M1$betasamples[[i_split]])
cov(res_simple_comp$M1$betasamples[[2]])
summary_stats_list1 <- f_summary_stats_per_split(key1, res_multisplit1)
summary_stats_list1[[2]]$mat_means
summary_stats_list1[[2]]$mat_cov
mcmc_output_rjmcmc <- f_filter_rows_mcmc_rjmcmc_output(res_multisplit1$results_splits[[i_split]][[i_rep]]$mcmc_ouput, res_multisplit1$results_splits[[i_split]][[i_rep]]$topmodel_res, key1)
beta_samples <- res_simple_comp$M1$betasamples[[i_split]]
plot(density(mcmc_output_rjmcmc[,2]))
lines(density(beta_samples[,2]), col="red")
pdf(paste("boxplot_m", index_M1, "_m", index_M2, ".pdf" ,sep=""))
boxplot(as.data.frame(bf_rjmcmc))
title(paste("Posterior Odds model ", index_M1, " vs. model ", index_M2, sep=""))
abline(h= estimate_single, col="red")
dev.off()
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