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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 parameters.

  • "varying" plots all varying effects. To plot a particular varying 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_varying" plots all levels of a particular varying effect, and "^cp_|^my_varying" 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

TRUE/FALSE. Plot using prior samples? Useful for mcp(..., sample = "both")

nvariables

Positive integer. Maximum number of parameters plotted per 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 as.mcmc(fit) or as_draws(fit) if you want finer control of plotting, e.g., bayesplot::mcmc_dens(as.mcmc(fit)). There are also a number of useful plots in the coda package, i.e., coda::gelman.plot(as.mcmc(fit)) and coda::crosscorr.plot(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 'adapt' 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)



if (FALSE) { # \dontrun{
# 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"))  # 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)

# Useful for varying effects:
# plot_pars(my_fit, pars = "varying", ncol = 3)  # plot all varying effects
# plot_pars(my_fit, regex_pars = "my_varying", ncol = 3)  # plot all levels of a particular varying
# pages = plot_pars(my_fit, pars = "varying", ask = FALSE)
# pages[[1]]  # Inspect or customize one page

# Customize multi-column ggplots using "*" instead of "+" (patchwork)
library(ggplot2)
plot_pars(demo_fit, type = c("trace", "dens_overlay")) * theme_bw(10)
} # }