pairs_admb {adnuts}  R Documentation 
Plot pairwise parameter posteriors and optionally the MLE points and confidence ellipses.
Description
Plot pairwise parameter posteriors and optionally the MLE points and confidence ellipses.
Usage
pairs_admb(
fit,
order = NULL,
diag = c("trace", "acf", "hist"),
acf.ylim = c(1, 1),
ymult = NULL,
axis.col = gray(0.5),
pars = NULL,
label.cex = 0.8,
limits = NULL,
add.mle = TRUE,
add.monitor = TRUE,
unbounded = FALSE,
...
)
Arguments
fit 
A list as returned by 
order 
The order to consider the parameters. Options are NULL (default) to use the order declared in the model, or 'slow' and 'fast' which are based on the effective sample sizes ordered by slowest or fastest mixing respectively. See example for usage. 
diag 
What type of plot to include on the diagonal,
options are 'acf' which plots the autocorrelation function

acf.ylim 
If using the acf function on the diagonal, specify the y limit. The default is c(1,1). 
ymult 
A vector of length ncol(posterior) specifying how much room to give when using the hist option for the diagonal. For use if the label is blocking part of the plot. The default is 1.3 for all parameters. 
axis.col 
Color of axes 
pars 
A vector of parameter names or integers representing which parameters to subset. Useful if the model has a larger number of parameters and you just want to show a few key ones. 
label.cex 
Control size of outer and diagonal labels (default 1) 
limits 
A list containing the ranges for each parameter to use in plotting. 
add.mle 
Boolean whether to add 95% confidence ellipses 
add.monitor 
Boolean whether to print effective sample 
unbounded 
Whether to use the bounded or unbounded version of the parameters. size (ESS) and Rhat values on the diagonal. 
... 
Arguments to be passed to plot call in lower diagonal panels 
Details
This function is modified from the base pairs
code to work specifically with fits from the
'adnuts' package using either the NUTS or RWM MCMC
algorithms. If an invertible Hessian was found (in
fit$mle
) then estimated covariances are available to
compare and added automatically (red ellipses). Likewise, a
"monitor" object from rstan::monitor
is attached as
fit$monitor
and provides effective sample sizes (ESS)
and Rhat values. The ESS are used to potentially order the
parameters via argument order
, but also printed on
the diagonal.
Value
Produces a plot, and returns nothing.
Author(s)
Cole Monnahan
Examples
fit < readRDS(system.file('examples', 'fit.RDS', package='adnuts'))
pairs_admb(fit)
pairs_admb(fit, pars=1:2)
pairs_admb(fit, pars=c('b', 'a'))
pairs_admb(fit, pars=1:2, order='slow')
pairs_admb(fit, pars=1:2, order='fast')