# pairs.stanfit

##### Create a matrix of output plots from a `stanfit`

object

A `pairs`

method that is customized for MCMC output

##### Usage

```
# S3 method for 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, 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 too many to visualize on a small computer screen. If`include = FALSE`

, then the named parameters are excluded from the plot.- 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 the rest in the upper panel. A list of two integer vectors can be passed, each specifying a subset of the chains to be plotted in the lower and upper panels respectively.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

`"accept_stat__", "stepsize__"`

,`"treedepth__", "n_leapfrog__"`

,`"divergent__"`

,,`"energy__"`

, or`"lp__"`

, which are the variables produced by`get_sampler_params`

and`get_logposterior`

. In that case the lower panel will plot realizations that are below the median of the indicated variable (or are zero in the case of`"divergent__"`

) and the upper panel will plot realizations that are greater than or equal to the median of the indicated variable (or are one in the case of`"divergent__"`

). Finally, any logical vector whose length is equal to the product of the number of iterations and the number of chains can be passed, in which case realizations corresponding to`FALSE`

and`TRUE`

will be plotted in the lower and upper panel respectively. The default is`"accept_stat__"`

.- 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
`"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

```
# NOT RUN {
example(read_stan_csv)
pairs(fit, pars = c("mu", "sigma", "alpha", "lp__"), log = TRUE, las = 1)
# sigma and alpha will have logarithmic axes
# }
```

*Documentation reproduced from package rstan, version 2.17.3, License: GPL (>= 3)*