Plot the traceplots and density plots for parameters within a jsdmStanFit object

This function takes parameters from the jsdmStanfit object and plots them using the bayesplot::mcmc_combo()function from the bayesplot package.

Usage

# S3 method for class 'jsdmStanFit'
plot(
  x,
  pars = NULL,
  combo = c("dens", "trace"),
  N = 5L,
  ask = TRUE,
  inc_warmup = FALSE,
  include = TRUE,
  sample_n = 10,
  regexp = FALSE,
  plot = TRUE,
  newpage = TRUE,
  ...
)

Arguments

x

The jsdmStanFit model object

pars

The parameters to plot, by default a random sample of twenty of the parameters fit within the model

combo

Which combination of plot types within bayesplot::mcmc_combo() to use, by default c("dens", "trace")

N

The number of plots per page, default 5

ask

Whether to ask before plotting a new page, default TRUE

inc_warmup

Whether to include the warmup period in the plots, by default FALSE

include

Whether to include or exclude the parameters specified by pars, by default TRUE (i.e. include)

sample_n

If pars = NULL then the number of random non-sigma parameters to include (details in description)

regexp

If pars should be treated as a regular expression for matching to parnames, by default FALSE

plot

Whether to plot the plots, default TRUE

newpage

Whether the first plot should be plotted on a new page, by default TRUE

...

Arguments passed to bayesplot::mcmc_combo()

Value

An invisible list of the plots#

Details

This acts as an interface to the bayesplot::mcmc_combo() function, with the default being to plot a density plot and a trace plot for each parameter specified, although this can be changed by setting the combo argument. These jSDM models have a lot of parameters so selecting a subset is recommended. If pars is set to NULL (the default) all parameters with either sigma or kappa in their name will be plotted along with a random selection of the other parameters (total number of other parameters set by sample_n). To see the name of the parameters within the model use get_parnames() - and if you want to plot all parameters (there will be hundreds in any reasonably sized model) set pars = get_parnames(x).

See also

mcmc_plot.jsdmStanFit() for more plotting options.

Examples

if (FALSE) { # \dontrun{
# First simulate data and get model fit:
mglmm_data <- mglmm_sim_data(
  N = 100, S = 10, K = 3,
  family = "gaussian"
)
mglmm_fit <- stan_mglmm(
  Y = mglmm_data$Y, X = mglmm_data$X,
  family = "gaussian"
)

# The default plot:
plot(mglmm_fit)

# Plotting specifically the cor_species parameters:
plot(mglmm_fit, pars = "cor_species", regexp = TRUE)

# Increasing the number of randomly sampled parameters to plot:
plot(mglmm_fit, sample_n = 20)
} # }