modelbased (version 0.7.0)

estimate_means: Estimate Marginal Means (Model-based average at each factor level)

Description

Estimate average value of response variable at each factor levels. For plotting, check the examples in visualisation_recipe. See also other related functions such as estimate_contrasts and estimate_slopes.

Usage

estimate_means(
  model,
  levels = NULL,
  fixed = NULL,
  modulate = NULL,
  transform = "response",
  ci = 0.95,
  ...
)

Arguments

model

A statistical model.

levels

A character vector or formula specifying the names of the predicting factors 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. Other arguments from visualisation_matrix, such as length to adjust the number of data points.

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.

ci

Uncertainty Interval (CI) level. Default to 95% (0.95).

...

Other arguments passed for instance to visualisation_matrix.

Value

A dataframe of estimated marginal means.

Examples

# NOT RUN {
library(modelbased)

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

estimate_means(model)
estimate_means(model, fixed = "Sepal.Width")
estimate_means(model, levels = c("Species", "Sepal.Width"), length = 2)
estimate_means(model, levels = "Species=c('versicolor', 'setosa')")
estimate_means(model, levels = "Sepal.Width=c(2, 4)")
estimate_means(model, levels = c("Species", "Sepal.Width=0"))
estimate_means(model, modulate = "Sepal.Width", length = 5)
estimate_means(model, modulate = "Sepal.Width=c(2, 4)")

# Methods that can be applied to it:
means <- estimate_means(model, fixed = "Sepal.Width")
plot(means) # which runs visualisation_recipe()
effectsize::standardize(means)
# }
# NOT RUN {
if (require("lme4")) {
  data <- iris
  data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B")

  model <- lmer(Petal.Length ~ Sepal.Width + Species + (1 | Petal.Length_factor), data = data)
  estimate_means(model)
  estimate_means(model, modulate = "Sepal.Width", length = 3)
}

# Bayesian models
# -------------------
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")
}
# }
# NOT RUN {
# }
# NOT RUN {
if (require("brms")) {
  model <- brm(mpg ~ cyl * am, data = data, refresh = 0)
  estimate_means(model)
}
# }
# NOT RUN {
# }