RoBMA (version 4.0.0)

plot_pet_peese: Plot PET-PEESE Fit of brma Object

Description

plot_pet_peese visualizes posterior (and prior) PET-PEESE fit of a brma object.

Usage

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

Value

plot_pet_peese returns either NULL invisibly if plot_type = "base" or a ggplot2 object if plot_type = "ggplot".

Arguments

x

a fitted brma object with a PET or PEESE component.

...

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, pch, bg, cex, and size to adjust the line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, y-axis range, and observed data point style respectively.

show_data

whether the observed effect sizes should be added to figure. 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".

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.

Details

The plot shows observed yi values against sei. PET regression uses \(\mu + PET \cdot se_i\); PEESE regression uses \(\mu + PEESE \cdot se_i^2\), with the fitted effect direction.

See Also

RoBMA()

Examples

if (FALSE) {
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")

  fit <- bPET(
    yi      = yi,
    vi      = vi,
    data    = dat.lehmann2018,
    measure = "SMD",
    seed    = 1,
    silent  = TRUE
  )

  plot_pet_peese(fit)
}
}