plot.see_parameters_brms_meta: Plot method for Model Parameters from Bayesian Meta-Analysis

In see: Visualisation Toolbox for 'easystats' and Extra Geoms, Themes and Color Palettes for 'ggplot2'

Plot method for Model Parameters from Bayesian Meta-Analysis

Description

The plot() method for the parameters::model_parameters() function when used with brms-meta-analysis models.

Usage

## S3 method for class 'see_parameters_brms_meta'
plot(
  x,
  size_point = 2,
  size_line = 0.8,
  size_text = 3.5,
  posteriors_alpha = 0.7,
  rope_alpha = 0.15,
  rope_color = "cadetblue",
  normalize_height = TRUE,
  show_labels = TRUE,
  ...
)

Arguments

x	An object.
size_point	Numeric specifying size of point-geoms.
size_line	Numeric value specifying size of line geoms.
size_text	Numeric value specifying size of text labels.
posteriors_alpha	Numeric value specifying alpha for the posterior distributions.
rope_alpha	Numeric specifying transparency level of ROPE ribbon.
rope_color	Character specifying color of ROPE ribbon.
normalize_height	Logical. If TRUE, height of mcmc-areas is "normalized", to avoid overlap. In certain cases when the range of a posterior distribution is narrow for some parameters, this may result in very flat mcmc-areas. In such cases, set normalize_height = FALSE.
show_labels	Logical. If TRUE, text labels are displayed.
...	Arguments passed to or from other methods.

Details

Colors of density areas and errorbars

To change the colors of the density areas, use scale_fill_manual() with named color-values, e.g. scale_fill_manual(values = c("Study" = "blue", "Overall" = "green")). To change the color of the error bars, use scale_color_manual(values = c("Errorbar" = "red")).

Show or hide estimates and CI

Use show_labels = FALSE to hide the textual output of estimates and credible intervals.

Value

A ggplot2-object.

Examples

## Not run: 
if (require("bayestestR") && require("brms") && require("metafor")) {
  +
    # data
    data(dat.bcg)
  dat <- escalc(
    measure = "RR",
    ai = tpos,
    bi = tneg,
    ci = cpos,
    di = cneg,
    data = dat.bcg
  )
  dat$author <- make.unique(dat$author)

  # model
  set.seed(123)
  priors <- c(
    prior(normal(0, 1), class = Intercept),
    prior(cauchy(0, 0.5), class = sd)
  )
  model <- brm(yi | se(vi) ~ 1 + (1 | author), data = dat)

  # result
  mp <- model_parameters(model)
  plot(mp)
}

## End(Not run)

see documentation built on March 31, 2022, 5:07 p.m.