get_emmeans: Easy 'emmeans' and 'emtrends'

In easystats/estimate: Estimation of Model-Based Predictions, Contrasts and Means

Easy 'emmeans' and 'emtrends'

Description

The get_emmeans() function is a wrapper to facilitate the usage of emmeans::emmeans() and emmeans::emtrends(), providing a somewhat simpler and intuitive API to find the specifications and variables of interest. It is meanly made to for the developers to facilitate the organization and debugging, and end-users should rather use the ⁠estimate_*()⁠ series of functions.

Usage

get_emcontrasts(
  model,
  contrast = NULL,
  at = NULL,
  fixed = NULL,
  transform = "none",
  method = "pairwise",
  ...
)

model_emcontrasts(
  model,
  contrast = NULL,
  at = NULL,
  fixed = NULL,
  transform = "none",
  method = "pairwise",
  ...
)

get_emmeans(
  model,
  at = "auto",
  fixed = NULL,
  transform = "response",
  levels = NULL,
  modulate = NULL,
  ...
)

model_emmeans(
  model,
  at = "auto",
  fixed = NULL,
  transform = "response",
  levels = NULL,
  modulate = NULL,
  ...
)

get_emtrends(
  model,
  trend = NULL,
  at = NULL,
  fixed = NULL,
  levels = NULL,
  modulate = NULL,
  ...
)

model_emtrends(
  model,
  trend = NULL,
  at = NULL,
  fixed = NULL,
  levels = NULL,
  modulate = NULL,
  ...
)

Arguments

model	A statistical model.
contrast	A character vector indicating the name of the variable(s) for which to compute the contrasts.
at	The predictor variable(s) at which to evaluate the desired effect / mean / contrasts. Other predictors of the model that are not included here will be collapsed and "averaged" over (the effect will be estimated across them).
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.
transform	Is passed to the type argument in emmeans::emmeans(). See this vignette. 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.
method	Contrast method. See same argument in emmeans::contrast.
...	Other arguments passed for instance to insight::get_datagrid().
levels, modulate	Deprecated, use at instead.
trend	A character indicating the name of the variable for which to compute the slopes.

Examples

if (require("emmeans", quietly = TRUE)) {
  # Basic usage
  model <- lm(Sepal.Width ~ Species, data = iris)
  get_emcontrasts(model)

  # Dealing with interactions
  model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)
  # By default: selects first factor
  get_emcontrasts(model)
  # Can also run contrasts between points of numeric
  get_emcontrasts(model, contrast = "Petal.Width", length = 3)
  # Or both
  get_emcontrasts(model, contrast = c("Species", "Petal.Width"), length = 2)
  # Or with custom specifications
  estimate_contrasts(model, contrast = c("Species", "Petal.Width=c(1, 2)"))
  # Can fixate the numeric at a specific value
  get_emcontrasts(model, fixed = "Petal.Width")
  # Or modulate it
  get_emcontrasts(model, at = "Petal.Width", length = 4)
}
model <- lm(Sepal.Length ~ Species + Petal.Width, data = iris)

if (require("emmeans", quietly = TRUE)) {
  # By default, 'at' is set to "Species"
  get_emmeans(model)

  # Overall mean (close to 'mean(iris$Sepal.Length)')
  get_emmeans(model, at = NULL)

  # One can estimate marginal means at several values of a 'modulate' variable
  get_emmeans(model, at = "Petal.Width", length = 3)

  # Interactions
  model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

  get_emmeans(model)
  get_emmeans(model, at = c("Species", "Petal.Length"), length = 2)
  get_emmeans(model, at = c("Species", "Petal.Length = c(1, 3, 5)"), length = 2)
}
if (require("emmeans")) {
  model <- lm(Sepal.Width ~ Species * Petal.Length, data = iris)

  get_emtrends(model)
  get_emtrends(model, at = "Species")
  get_emtrends(model, at = "Petal.Length")
  get_emtrends(model, at = c("Species", "Petal.Length"))

  model <- lm(Petal.Length ~ poly(Sepal.Width, 4), data = iris)
  get_emtrends(model)
  get_emtrends(model, at = "Sepal.Width")
}

easystats/estimate documentation built on Sept. 18, 2023, 11:35 p.m.