Plots a fitted RoBSA object

plot.RoBSA allows to visualize posterior distribution of different "RoBSA" object parameters. See plot_survival for plotting the survival ways. See type for the different model types.

# S3 method for RoBSA
plot(
  x,
  parameter = NULL,
  conditional = FALSE,
  plot_type = "base",
  prior = FALSE,
  dots_prior = NULL,
  ...
)

Arguments

x

a fitted RoBSA object

parameter

a name of parameter to be plotted. Defaults to the first regression parameter if left unspecified. Use "intercept" and "aux" to plot the intercepts and auxiliary parameters of each distribution family.

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".

prior

whether prior distribution should be added to figure. Defaults to FALSE.

dots_prior

list of additional graphical arguments to be passed to the plotting function of the prior distribution. Supported arguments are lwd, lty, col, and col.fill, to adjust the line thickness, line type, line color, and fill color of the prior distribution respectively.

...

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.RoBSA returns either NULL if plot_type = "base"

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

See also

RoBSA()

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
plot(fit, parameter = "treatment")

}