The function plots a set of densities (per chain and coefficient) from the MCMC sample of an object of class "JointAI".

densplot(object, ...)

# S3 method for JointAI
densplot(object, start = NULL, end = NULL, thin = NULL,
  subset = c(analysis_main = TRUE), outcome = NULL,
  exclude_chains = NULL, vlines = NULL, nrow = NULL, ncol = NULL,
  joined = FALSE, use_ggplot = FALSE, warn = TRUE, mess = TRUE, ...)



object inheriting from class 'JointAI'


additional parameters passed to plot()


the first iteration of interest (see window.mcmc)


the last iteration of interest (see window.mcmc)


thinning interval (integer; see window.mcmc). For example, thin = 1 (default) will keep the MCMC samples from all iterations; thin = 5 would only keep every 5th iteration.


subset of parameters/variables/nodes (columns in the MCMC sample). Follows the same principle as the argument monitor_params in *_imp.


optional; vector identifying a subset of sub-models included in the output, either by specifying their indices (using the order used in the list of model formulas), or their names (LHS of the respective model formula as character string)


optional vector of the index numbers of chains that should be excluded


list, where each element is a named list of parameters that can be passed to graphics::abline() to create vertical lines. Each of the list elements needs to contain at least v = <x location>, where <x location> is a vector of the same length as the number of plots (see examples).


optional; number of rows in the plot layout; automatically chosen if unspecified


optional; number of columns in the plot layout; automatically chosen if unspecified


logical; should the chains be combined before plotting?


logical; Should ggplot be used instead of the base graphics?


logical; should warnings be given? Default is TRUE.


logical; should messages be given? Default is TRUE.

See also

The vignette Parameter Selection contains some examples how to specify the argument subset.


if (FALSE) {
# fit a JointAI object:
mod <- lm_imp(y ~ C1 + C2 + M1, data = wideDF, n.iter = 100)

# Example 1: basic densityplot
densplot(mod, exclude_chains = 2)

# Example 2: use vlines to mark zero
densplot(mod, col = c("darkred", "darkblue", "darkgreen"),
         vlines = list(list(v = rep(0, nrow(summary(mod)$res$y$regcoef)),
                            col = grey(0.8))))

# Example 3: use vlines to visualize posterior mean and 2.5%/97.5% quantiles
res <- rbind(summary(mod)$res$y$regcoef[, c('Mean', '2.5%', '97.5%')],
             summary(mod)$res$y$sigma[, c('Mean', '2.5%', '97.5%'),
             drop = FALSE]
densplot(mod, vlines = list(list(v = res[, "Mean"], lty = 1, lwd = 2),
                            list(v = res[, "2.5%"], lty = 2),
                            list(v = res[, "97.5%"], lty = 2)))

# Example 4: ggplot version
densplot(mod, use_ggplot = TRUE)

# Example 5: change how the ggplot version looks

densplot(mod, use_ggplot = TRUE) +
  xlab("value") +
  theme(legend.position = 'bottom') +
  scale_color_brewer(palette = 'Dark2', name = 'chain')