rstan (version 2.9.0-3)

pairs.stanfit: Create a matrix of output plots from a stanfit object

Description

A pairs method that is customized for MCMC output

Usage

## S3 method for class 'stanfit':
pairs(x, labels = NULL, panel = NULL, \dots, 
    lower.panel = NULL, 
    upper.panel = NULL, diag.panel = NULL, text.panel = NULL, 
    label.pos = 0.5 + 1/3, cex.labels = NULL, font.labels = 1, 
    row1attop = TRUE, gap = 1, log = "", pars = NULL, condition = "accept_stat__", 
    include = TRUE)

Arguments

x

An object of S4 class stanfit

labels, panel, ..., lower.panel, upper.panel, diag.panel

Same as in pairs syntactically but see the Details section for different default arguments

text.panel, label.pos, cex.labels, font.labels, row1attop, gap

Same as in pairs.default

log

Same as in pairs.default, which makes it possible to utilize logarithmic axes and additionally accepts log = TRUE. See the Details section.

pars

If not NULL, a character vector indicating which quantities to include in the plots, which is passed to extract. Thus, by default, all unknown quantities are included, which may be far

condition

If NULL, it will plot roughly half of the chains in the lower panel and the rest in the upper panel. An integer vector can be passed to select some subset of the chains, of which roughly half will be plotted in the lower panel and

include

Logical scalar indicating whether to include (the default) or excldue the parameters named in the pars argument from the plot.

Details

This method differs from the default pairs method in the following ways. If unspecified, the smoothScatter function is used for the off-diagonal plots, rather than points, since the former is more appropriate for visualizing thousands of draws from a posterior distribution. Also, if unspecified, histograms of the marginal distribution of each quantity are placed on the diagonal of the plot, after pooling all of the chains specified by the chain_id argument. The draws from the warmup phase are always discarded before plotting. By default, the lower (upper) triangle of the plot contains draws with below (above) median acceptance probability. Also, if condition is not "n_divergent__", red points will be superimposed onto the smoothed density plots indicating which (if any) iterations encountered a divergent transition. Otherwise, yellow points indicate a transition that hit the maximum treedepth rather than terminated its evolution normally. You may very well want to specify the log argument for non-negative parameters. However, the pairs function will drop (with a message) parameters that are either constant or duplicative with previous parameters. For example, if a correlation matrix is included among pars, then neither its diagonal elements (which are always 1) nor its upper triangular elements (which are the same as the corresponding lower triangular elements) will be included. Thus, if log is an integer vector, it needs to pertain to the parameters after constant and duplicative ones are dropped. It is perhaps easiest to specify log = TRUE, which will utilize logarithmic axes for all non-negative parameters, except lp__ and any integer valued quantities.

See Also

S4 class stanfit and its method extract as well as the pairs generic function. Also, see get_sampler_params and get_logposterior.

Examples

example(read_stan_csv)
pairs(fit, pars = c("mu", "sigma", "alpha", "lp__"), log = TRUE, las = 1)
# sigma and alpha will have logarithmic axes