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MCMC plots implemented in bayesplot

mcmc_plot.jsdmStanFit.Rd

An interface for calling the MCMC plotting functions implemented in the bayesplot package.

Usage

# S3 method for class 'jsdmStanFit'
mcmc_plot(
  x,
  pars = NULL,
  plotfun = "intervals",
  sample_n = 10,
  inc_warmup = FALSE,
  include = TRUE,
  regexp = FALSE,
  ...
)

mcmc_plot(x, ...)

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

plotfun

The MCMC plot function from bayesplot to be used

sample_n

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

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)

regexp

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

...

Other arguments to be passed to the MCMC plotting function

Value

A ggplot object that can be customised using the ggplot2 package

Details

This acts as an interface to the plotting functions prefaced with mcmc_ within the bayesplot package. The default is to plot an interval plot for the parameters specified, for a full list of available plot options run bayesplot::available_mcmc() or the error message when you set plotfun to an unrecognised plot function will give you a list of options. 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

plot.jsdmStanFit()

Examples

if (FALSE) { # \dontrun{
# First simulate data and fit the model:
gllvm_data <- jsdm_sim_data(
  method = "gllvm", N = 100, S = 6, D = 2,
  family = "bernoulli"
)
gllvm_fit <- stan_jsdm(
  dat_list = gllvm_data, method = "gllvm",
  family = "bernoulli"
)

# Default is to plot the intervals:
mcmc_plot(gllvm_fit)

# Other plot types include options to see parameter recovery (if the
# jsdm_sim_data functions are used the original parameters are saved within the
# data list)
mcmc_plot(gllvm_fit,
  plotfun = "recover_intervals",
  pars = c("LV[2,20]", "sigmas_preds[1]", "sigma_L"),
  true = c(
    gllvm_data$pars$LV[2, 20],
    gllvm_data$pars$beta_sds,
    gllvm_data$pars$L_sigma
  )
)
} # }