RoBMA (version 4.0.0)

plot_weightfunction: Plots Weight Function of brma Object

Description

plot_weightfunction.brma visualizes the posterior (and optionally prior) publication-bias weight function of a brma object.

Usage

plot_weightfunction(x, ...)
# S3 method for brma
plot_weightfunction(
  x,
  rescale_p_values = TRUE,
  prior = FALSE,
  plot_type = "base",
  show_data = TRUE,
  dots_data = NULL,
  dots_prior = NULL,
  ...
)

Value

plot_weightfunction.brma returns either NULL if plot_type = "base" or a ggplot2 object if plot_type = "ggplot". The method errors for fitted objects without a weightfunction component.

Arguments

x

a fitted brma object with a weightfunction/selection component, such as bselmodel() or a RoBMA() object with weightfunction priors.

...

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.

rescale_p_values

whether to rescale p-values to the interval shown by the weightfunction plot. Defaults to TRUE.

prior

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

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".

show_data

whether observed one-sided p-values should be shown as rug marks on the weightfunction axis. Defaults to TRUE.

dots_data

list of additional graphical arguments for observed p-value rug marks. Supported arguments include col/color, alpha, lwd/linewidth/size, side/rug_side, and height/rug_height/ticksize.

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.

See Also

RoBMA()

Examples

if (FALSE) {
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")
  fit <- bselmodel(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  plot_weightfunction(fit)
  plot_weightfunction(fit, prior = TRUE)
}
}