modelbased (version 0.6.0)

estimate_contrasts.stanreg: Estimate contrasts

Description

Estimate contrasts

Usage

# S3 method for stanreg
estimate_contrasts(
  model,
  levels = NULL,
  fixed = NULL,
  modulate = NULL,
  transform = "none",
  length = 10,
  standardize = TRUE,
  standardize_robust = FALSE,
  centrality = "median",
  ci = 0.95,
  ci_method = "hdi",
  test = c("pd", "rope"),
  rope_range = "default",
  rope_ci = 1,
  ...
)

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.

standardize

If TRUE, adds standardized differences or coefficients.

standardize_robust

Robust standardization through MAD (Median Absolute Deviation, a robust estimate of SD) instead of regular SD.

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).

test

The indices of effect existence to compute. Character (vector) or list with one or more of these options: "p_direction" (or "pd"), "rope", "p_map", "equivalence_test" (or "equitest"), "bayesfactor" (or "bf") or "all" to compute all tests. For each "test", the corresponding bayestestR function is called (e.g. rope or p_direction) and its results included in the summary output.

rope_range

ROPE's lower and higher bounds. Should be a list of two values (e.g., c(-0.1, 0.1)) or "default". If "default", the bounds are set to x +- 0.1*SD(response).

rope_ci

The Credible Interval (CI) probability, corresponding to the proportion of HDI, to use for the percentage in ROPE.

...

Arguments passed to or from other methods.

Value

A data frame of estimated contrasts.

Examples

# NOT RUN {
library(modelbased)

data <- mtcars
data$cyl <- as.factor(data$cyl)
data$am <- as.factor(data$am)
# }
# NOT RUN {
if (require("rstanarm")) {
  model <- stan_glm(mpg ~ cyl * am, data = data, refresh = 0)
  estimate_contrasts(model)
  estimate_contrasts(model, fixed = "am")

  model <- stan_glm(mpg ~ cyl * wt, data = data, refresh = 0)
  estimate_contrasts(model)
  estimate_contrasts(model, fixed = "wt")
  estimate_contrasts(model, modulate = "wt", length = 4)
  estimate_contrasts(model, levels = "wt", length = 4)

  model <- stan_glm(Sepal.Width ~ Species + Petal.Width + Petal.Length, data = iris, refresh = 0)
  estimate_contrasts(model, fixed = "Petal.Width", modulate = "Petal.Length", test = "bf")
}

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