see (version 0.8.2)

plot.see_parameters_brms_meta: 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 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,
  ...
)

Value

A ggplot2-object.

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.

Examples

if (FALSE) { # require("brms") && require("metafor") && require("RcppEigen")
# \donttest{
library(parameters)
library(brms)
library(metafor)
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 <- suppressWarnings(
  brm(yi | se(vi) ~ 1 + (1 | author), data = dat)
)

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