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Rxc density plots and parallelization cluster making #122

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Rxc density plots and parallelization cluster making #122

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c7e0048
add time out time for making clusters
hikari-murayama Sep 4, 2020
6d6343e
Change mean innerjoin lead up
hikari-murayama Sep 4, 2020
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2 changes: 1 addition & 1 deletion R/ei_iter.R
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Original file line number Diff line number Diff line change
Expand Up @@ -89,7 +89,7 @@ ei_iter <- function(
}

# Standard to use 1 less core for clusters
clust <- parallel::makeCluster(parallel::detectCores() - 1)
clust <- parallel::makeCluster(parallel::detectCores() - 1, setup_timeout = 0.5)

# Register parallel processing cluster
doSNOW::registerDoSNOW(clust)
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4 changes: 1 addition & 3 deletions R/ei_rxc.R
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Expand Up @@ -136,7 +136,7 @@ ei_rxc <- function(
if (verbose) message("Running in paralllel")

# Standard to use 1 less core for clusters
clust <- parallel::makeCluster(parallel::detectCores() - 1)
clust <- parallel::makeCluster(parallel::detectCores() - 1, setup_timeout = 0.5)

# Register parallel processing cluster
doSNOW::registerDoSNOW(clust)
Expand Down Expand Up @@ -319,8 +319,6 @@ ei_rxc <- function(
}

if (plots) {
# colnames(chains_pr) <- gsub("ccount.", "betas.", colnames(md_out$draws$Cell.counts))
colnames(chains_pr) <- paste0("betas.", colnames(chains_pr))
# Create density plots
density_plots <- overlay_density_plot(chains_pr, results_table,
race_cols, cand_cols,
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6 changes: 4 additions & 2 deletions R/overlay_density_plot.R
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Expand Up @@ -70,6 +70,7 @@ overlay_density_plot <- function(agg_betas, results_table, race_cols, cand_cols,

# Column titles
colnames(race_comb) <- gsub(paste(race[k], "_", sep = ""), "", colnames(race_comb))
colnames(race_comb) <- gsub(paste("_", race[k], sep = ""), "", colnames(race_comb))

# Set up data to create graphs
dens_data <- reshape2::melt(race_comb, id.vars = NULL)
Expand All @@ -96,9 +97,10 @@ overlay_density_plot <- function(agg_betas, results_table, race_cols, cand_cols,
} else if (ei_type == "rxc") {
dens_data <- as.data.frame(dens_data[, c("Candidate", "value")])

rt_sub <- as.data.frame(results_table[race[k]][[1]])
rt_sub$Candidate <- rownames(rt_sub)
rt_sub <- as.data.frame(results_table[results_table$race == race[k], ])
rt_sub <- dplyr::rename(rt_sub, mean_size = mean)
rt_sub$mean_size <- rt_sub$mean_size * 100
rt_sub <- dplyr::rename(rt_sub, Candidate = cand)
rt_sub <- rt_sub[, c("Candidate", "mean_size")]
}

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