pairs.stanfit  R Documentation 
stanfit
objectA pairs
method that is customized for MCMC output
## S3 method for class 'stanfit' pairs(x, labels = NULL, panel = NULL, ..., 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, include = TRUE, condition = "accept_stat__")
x 
An object of S4 class 
labels, panel, ..., lower.panel, upper.panel, diag.panel 
Same as in

text.panel, label.pos, cex.labels, font.labels, row1attop, gap 
Same as in 
log 
Same as in 
pars 
If not 
condition 
If A single number between zero and one exclusive can be passed, which is interpreted as the proportion of realizations (among all chains) to plot in the lower panel starting with the first realization in each chain, with the complement (from the end of each chain) plotted in the upper panel. A (possibly abbreviated) character vector of length one can be passed among

include 
Logical scalar indicating whether to include (the default) or
exclude the parameters named in the 
This method differs from the default pairs
method in the following
ways. If unspecified, the smoothScatter
function is used for the
offdiagonal 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
"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 nonnegative
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 nonnegative parameters, except lp__
and
any integer valued quantities.
S4 class stanfit
and its method extract
as
well as the pairs
generic function. Also, see
get_sampler_params
and get_logposterior
.
example(read_stan_csv) pairs(fit, pars = c("mu", "sigma", "alpha", "lp__"), log = TRUE, las = 1) # sigma and alpha will have logarithmic axes
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