Models plot for a RoBSA object — plot

plot_models plots individual models' estimates for a "RoBSA" object.

plot_models(
  x,
  parameter = NULL,
  conditional = FALSE,
  plot_type = "base",
  order = "decreasing",
  order_by = "model",
  ...
)

Arguments

x: a fitted RoBSA object
parameter: a name of parameter to be plotted. Defaults to the first regression parameter if left unspecified.
conditional: whether conditional estimates should be plotted. Defaults to FALSE which plots the model-averaged estimates.
plot_type: whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".
order: how the models should be ordered. Defaults to "decreasing" which orders them in decreasing order in accordance to order_by argument. The alternative is "increasing".
order_by: what feature should be use to order the models. Defaults to "model" which orders the models according to their number. The alternatives are "estimate" (for the effect size estimates), "probability" (for the posterior model probability), and "BF" (for the inclusion Bayes factor).
...: list of additional graphical arguments to be passed to the plotting function. Supported arguments are lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim to adjust the line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, and y-axis range respectively.

Value

plot_models returns either NULL if plot_type = "base"

or an object object of class 'ggplot2' if plot_type = "ggplot2".

Examples

if (FALSE) {
# (execution of the example takes several minutes)
# example from the README (more details and explanation therein)
data(cancer, package = "survival")
priors <- calibrate_quartiles(median_t = 5, iq_range_t = 10, prior_sd = 0.5)
df <- data.frame(
  time         = veteran$time / 12,
  status       = veteran$status,
  treatment    = factor(ifelse(veteran$trt == 1, "standard", "new"), levels = c("standard", "new")),
  karno_scaled = veteran$karno / 100
)
RoBSA.options(check_scaling = FALSE)
fit <- RoBSA(
  Surv(time, status) ~ treatment + karno_scaled,
  data   = df,
  priors = list(
    treatment    = prior_factor("normal", parameters = list(mean = 0.30, sd = 0.15),
                                truncation = list(0, Inf), contrast = "treatment"),
    karno_scaled = prior("normal", parameters = list(mean = 0, sd = 1))
  ),
  test_predictors = "treatment",
  prior_intercept = priors[["intercept"]],
  prior_aux       = priors[["aux"]],
  parallel = TRUE, seed = 1
)

# plot posterior distribution of the treatment effect from each model
plot_models(fit, parameter = "treatment")

}