Plot the posterior density from object of class JointAI
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 class '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 |
object inheriting from class 'JointAI' |
... |
additional parameters passed to |
start |
the first iteration of interest
(see |
end |
the last iteration of interest
(see |
thin |
thinning interval (integer; see |
subset |
subset of parameters/variables/nodes (columns in the MCMC
sample). Follows the same principle as the argument
|
outcome |
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) |
exclude_chains |
optional vector of the index numbers of chains that should be excluded |
vlines |
list, where each element is a named list of parameters that
can be passed to |
nrow |
optional; number of rows in the plot layout; automatically chosen if unspecified |
ncol |
optional; number of columns in the plot layout; automatically chosen if unspecified |
joined |
logical; should the chains be combined before plotting? |
use_ggplot |
logical; Should ggplot be used instead of the base graphics? |
warn |
logical; should warnings be given? Default is
|
mess |
logical; should messages be given? Default is
|
The vignette
Parameter Selection
contains some examples how to specify the argument subset
.
# fit a JointAI object: mod <- lm_imp(y ~ C1 + C2 + M1, data = wideDF, n.iter = 100) # Example 1: basic densityplot densplot(mod) 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 library(ggplot2) densplot(mod, use_ggplot = TRUE) + xlab("value") + theme(legend.position = 'bottom') + scale_color_brewer(palette = 'Dark2', name = 'chain')
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