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Plot individual parameters

Source: R/plot_pars.R
plot_pars.Rd

Plot many types of plots of parameter estimates. See examples for typical use cases.

Usage

plot_pars(
  fit,
  pars = "population",
  regex_pars = character(0),
  type = "combo",
  ncol = 1,
  prior = FALSE,
  nvariables = 5,
  ask = TRUE
)

Arguments

fit

An mcpfit object.

pars

Character vector. One of:

  • Vector of parameter names.

  • "population" plots all population-level parameters.

  • "group" plots all group-level deviations (random effects). To plot a particular group-level effect, use regex_pars = "^name".

regex_pars

Vector of regular expressions. This will typically just be the beginning of the parameter name(s), i.e., "^cp_" plots all change points, "^my_group_effect" plots all levels of a particular group-level effect, and "^cp_|^my_group_effect" plots both.

type

String or vector of strings. Calls bayesplot::mcmc_>>type<<(). Common calls are "combo", "trace", and "dens_overlay". Current options include 'acf', 'acf_bar', 'areas', 'areas_ridges', 'combo', 'dens', 'dens_chains', 'dens_overlay', 'hist', 'intervals', 'rank_hist', 'rank_overlay', 'trace', 'trace_highlight', and 'violin".

ncol

Number of columns in plot. This is useful when you have many parameters and only one plot type.

prior

Logical. Plot prior draws (TRUE) instead of posterior draws (FALSE, default)? Useful for mcp(..., sample = "both").

nvariables

Positive integer or NULL / Inf. Maximum number of parameters plotted per page. Set to NULL or Inf to plot all parameters on a single page. The default of 5 follows brms::plot.brmsfit().

ask

Logical. In an interactive session, prompt before printing each page after the first. Only used when there are multiple pages.

Value

A ggplot2 object when all selected parameters fit on one page. For multiple pages, an invisible list of ggplot2 objects.

Details

For other type, it calls bayesplot::mcmc_type(). Use these directly on coda::as.mcmc(fit) or as_draws(fit) if you want finer control of plotting, e.g., bayesplot::mcmc_dens(coda::as.mcmc(fit)). There are also a number of useful plots in the coda package, i.e., coda::gelman.plot(coda::as.mcmc(fit)) and coda::crosscorr.plot(coda::as.mcmc(fit))

In any case, if you see a few erratic lines or parameter estimates, this is a sign that you may want to increase argument 'warmup' and 'iter' in mcp.

Up to nvariables parameters are shown on each page. Multi-page plots are printed in sequence; in interactive use, ask = TRUE pauses between pages.

See also

plot_dpar pp_check

Author

Jonas Kristoffer Lindeløv jonas@lindeloev.dk

Examples

# Typical usage. demo_fit is an mcpfit object.
plot_pars(demo_fit)



# \donttest{
# More options
plot_pars(demo_fit, regex_pars = "^cp_")  # Plot only change points

plot_pars(demo_fit, pars = c("Intercept_3", "time_3"))  # Plot these parameters

plot_pars(demo_fit, type = c("trace", "violin"), regex_pars = "^cp_")  # Combine plots

# Some plots only take pairs. hex is good to assess identifiability
plot_pars(demo_fit, type = "hex", pars = c("cp_1", "time_2"))


# Visualize the priors:
plot_pars(demo_fit, prior = TRUE, regex_pars = "^cp_")


# Useful for group-level effects:
# plot_pars(my_fit, pars = "group", ncol = 3)  # plot all group-level deviations
# plot_pars(my_fit, regex_pars = "my_group_effect", ncol = 3)  # one group-level effect
# pages = plot_pars(my_fit, pars = "group", ask = FALSE)
# pages[[1]]  # Inspect or customize one page

# Customize multi-column ggplots using "*" instead of "+" (patchwork)
library(ggplot2)
library(patchwork)
plot_pars(demo_fit, regex_pars = "cp") & theme_gray(15)

# }