The mcmcsample package extends ggplot2 to make it easier to plot the correlation between the parameters in your mcmc samples.
library(data.table) param1 <- rnorm( 1000, 0, 1 ) param2 <- sapply(param1, function(x) x^2 + rnorm( 1, 0, 1 ) ) # Make sure to add a sample.id column samples <- data.table( data.frame( list("sample.id"=seq(1,length(param1)), "param1"=param1, "param2"=param2) ) )
library(mcmcsample) library(ggplot2) long.samples <- melt(samples,measure.vars=c("param1","param2"),id.vars = c("sample.id")) ggs <- gg.correlation(long.samples, "value", "variable")
Points that fall outside the credibility interval
library(mcmcsample) library(ggplot2) smpls <- samples smpls$ci <- inside.ci(smpls[,.(param1,param2)],0.8, method="bin") long.samples <- melt(smpls,measure.vars=c("param1","param2"),id.vars = c("sample.id","ci")) ggs <- gg.correlation(long.samples, "value", "variable", colour="ci")
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