Plot MLE vs MCMC marginal standard deviations for each parameter

plot_uncertainties(fit, log = TRUE, plot = TRUE)

Arguments

fit	A fitted object returned by `sample_admb`
log	Whether to plot the logarithm or not.
plot	Whether to plot it or not.

Value

Invisibly returns data.frame with parameter name and estimated uncertainties.

Details

It can be helpful to compare uncertainty estimates between the two paradigms. This plots the marginal posterior standard deviation vs the frequentist standard error estimated from the .cor file. Large differences often indicate issues with one estimation method.

Examples

fit <- readRDS(system.file('examples', 'fit.RDS', package='adnuts'))
x <- plot_uncertainties(fit, plot=FALSE)
head(x)
#>   par   sd.post  sd.mle
#> a   a 0.2028936 0.15547
#> b   b 0.9112519 0.70394