Plots weighted scatterplots for meta-analytic data. Can plot effect size as a function of either continuous (numeric, integer) or categorical (factor, character) predictors.

Usage

WeightedScatter(
  data,
  yi = "yi",
  vi = "vi",
  vars = NULL,
  tau2 = NULL,
  summarize = TRUE
)

Arguments

data: A data.frame.
yi: Character. The name of the column in data that contains the meta-analysis effect sizes. Defaults to "yi".
vi: Character. The name of the column in the data that contains the variances of the effect sizes. Defaults to "vi". By default, vi is used to calculate fixed-effects weights, because fixed effects weights summarize the data set at hand, rather than generalizing to the population.
vars: Character vector containing the names of specific moderator variables to plot. When set to NULL, the default, all moderators are plotted.
tau2: Numeric. Provide an optional value for tau2. If this value is provided, random-effects weights will be used instead of fixed-effects weights.
summarize: Logical. Should summary stats be displayed? Defaults to FALSE. If TRUE, a smooth trend line is displayed for continuous variables, using [stats::loess()] for less than 1000 observations, and [mgcv::gam()] for larger datasets. For categorical variables, box-and-whiskers plots are displayed. Outliers are omitted, because the raw data fulfill this function.

Value

A gtable object.

Examples

if (FALSE) { # \dontrun{
set.seed(42)
data <- SimulateSMD(k_train = 100, model = es * x[, 1] + es * x[, 2] + es *
                      x[, 1] * x[, 2])$training
data$X2 <- cut(data$X2, breaks = 2, labels = c("Low", "High"))
data$X3 <- cut(data$X3, breaks = 2, labels = c("Small", "Big"))
WeightedScatter(data, summarize = FALSE)
WeightedScatter(data, vars = c("X3"))
WeightedScatter(data, vars = c("X1", "X3"))
} # }