Nothing
#############################################################################
# Collect files from step 1 to create variance lookup object
#############################################################################
library(data.table)
# Note that these are exactly the same values that were defined in step1
f_alleles <- c(seq(0.001, 0.01, by = 0.001), seq(0.02,0.1, by = 0.01), seq(0.15,0.5, by = 0.05))
# Read all the results from step1
.variance.simulation <- rbindlist(lapply(1:length(f_alleles), function(x){
out_dat <- fread(paste0("./job_", x, ".txt"))
return(out_dat)
}))
# We save this in a place for package internal data
usethis::use_data(.variance.simulation, internal = TRUE, overwrite = TRUE)
library(ggplot2)
library(ggsci)
# One way to visualise this
g_p <- ggplot(.variance.simulation, aes(x = beta_hat, y = alpha_hat, color = factor(f_par), group = factor(f_par))) +
geom_line() + theme_bw() + scale_color_viridis_d() + xlab("Estimated beta after inv norm") + ylab("Estimated alpha after inv norm") +
theme(legend.position = "top") + labs(color="allele frequency") +
ggtitle("Beta hat and Alpha hat")
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.