modelbased (version 0.5.0)

estimate_means.stanreg: Estimate marginal means

Description

Estimate marginal means

Usage

# S3 method for stanreg
estimate_means(
  model,
  levels = NULL,
  fixed = NULL,
  modulate = NULL,
  transform = "response",
  length = 10,
  centrality = "median",
  ci = 0.95,
  ci_method = "hdi",
  ...
)

Arguments

model

A Bayesian model.

levels

A character vector or formula specifying the names of the predictors over which to estimate means or contrasts.

fixed

A character vector indicating the names of the predictors to be "fixed" (i.e., maintained), so that the estimation is made at these values.

modulate

A character vector indicating the names of a numeric variable along which the means or the contrasts will be estimated. Adjust its length using length.

transform

Can be "none" (default for contrasts), "response" (default for means), "mu", "unlink", "log". "none" will leave the values on scale of the linear predictors. "response" will transform them on scale of the response variable. Thus for a logistic model, "none" will give estimations expressed in log-odds (probabilities on logit scale) and "response" in terms of probabilities.

length

Length of the spread numeric variables.

centrality

The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" or "all".

ci

Credible Interval (CI) level. Default to 0.89 (89%). See ci for further details.

ci_method

The type of index used for Credible Interval. Can be "HDI" (default, see hdi), "ETI" (see eti) or "SI" (see si).

...

Arguments passed to or from other methods.

Value

A dataframe of estimated marginal means.

Examples

# NOT RUN {
library(modelbased)
# }
# NOT RUN {
data <- mtcars
data$cyl <- as.factor(data$cyl)
data$am <- as.factor(data$am)

if (require("rstanarm")) {
  model <- stan_glm(mpg ~ cyl * am, data = data, refresh = 0)
  estimate_means(model)

  model <- stan_glm(mpg ~ cyl * wt, data = data, refresh = 0)
  estimate_means(model)
  estimate_means(model, modulate = "wt")
  estimate_means(model, fixed = "wt")
}

if (require("brms")) {
  model <- brm(mpg ~ cyl * am, data = data, refresh = 0)
  estimate_means(model)
}
# }