Plot posterior densities of group summaries of individual parameters. The respondents can be grouped by any categorical variable and the function works whether the fitted model is of "MULTI"-type or not.
plot_by_group(
object,
par = "abs_beta",
group_id = NULL,
ascending_means = TRUE,
fill = "#2166AC",
color = "#053061",
alpha = 0.5,
ncol = max(1, round(length(unique(group_id))/10))
)A ggplot object.
An instance of class stanfit produced by hbam().
Character: Name of the parameter to be plotted. One of the following: "alpha", "beta", "abs_beta", "lambda", or "chi". Defaults to "abs_beta", which means the absolute values of the draws for beta will be used. Further individual-level parameters like "eta" can be specified if these have been passed to hbam() via the argument extra_pars when fitting the model. (Note that homoskedastic models have no "eta" parameters and "NF"-type models have no "lambda" or "kappa" parameters.)
An optional vector that will be used to split the respondents into groups. The vector must either be as long as the number of rows in the original dataset, or as long as the number of respondents included in the analysis. If a group_id was previously supplied to prep_data() or hbam() and if no group_id is supplied here, the default is to use the existing group_id. If a group_id is supplied here, it will be used instead of any previously supplied vector. The group_id supplied here does not have to coincide with the group_id used to fit a "MULTI"-type model: Any vector that can be used to group the respondents is allowed.
Logical: Should the groups be placed in ascending order based on their posterior means (TRUE) or should they be ordered based on their names (FALSE)? Defaults to TRUE.
Fill color. Passed on to ggplot2::geom_density().
Color of outer lines. Passed on to ggplot2::geom_density().
Number in [0,1]: Inverse level of transparency.
Number of columns. The default uses a formula to have approximately ten subplots per column.