modelbased (version 0.7.0)

estimate_contrasts: Estimate Marginal Contrasts

Description

Run a contrast analysis by estimating the differences between each level of a factor. See also other related functions such as estimate_means and estimate_slopes.

Usage

estimate_contrasts(
  model,
  levels = NULL,
  fixed = NULL,
  modulate = NULL,
  transform = "none",
  ci = 0.95,
  adjust = "holm",
  ...
)

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).

adjust

The p-values adjustment method for frequentist multiple comparisons. Can be one of "holm" (default), "tukey", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr" or "none". See the p-value adjustment section in the emmeans::test documentation.

...

Other arguments passed for instance to visualisation_matrix.

Value

A data frame of estimated contrasts.

Examples

# NOT RUN {
library(modelbased)

# Basic usage
model <- lm(Sepal.Width ~ Species, data = iris)
estimate_contrasts(model)

# Dealing with interactions
model <- lm(Sepal.Width ~ Species * Petal.Width, data = iris)
estimate_contrasts(model)
estimate_contrasts(model, fixed = "Petal.Width")
estimate_contrasts(model, modulate = "Petal.Width", length = 4)
estimate_contrasts(model, levels = "Petal.Width", length = 4)

# Standardized differences
estimated <- estimate_contrasts(lm(Sepal.Width ~ Species, data = iris))
effectsize::standardize(estimated)

# Other models (mixed, Bayesian, ...)
if (require("lme4")) {
  data <- iris
  data$Petal.Length_factor <- ifelse(data$Petal.Length < 4.2, "A", "B")

  model <- lmer(Sepal.Width ~ Species + (1 | Petal.Length_factor), data = data)
  estimate_contrasts(model)
}

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)
}
# }
# NOT RUN {
# }