ggeffects (version 1.2.0)

hypothesis_test: (Pairwise) comparisons between predictions

Description

Function to test differences of adjusted predictions for statistical significance. This is usually called contrasts or (pairwise) comparisons.

Usage

hypothesis_test(model, ...)
# S3 method for default
hypothesis_test(
  model,
  terms = NULL,
  test = "pairwise",
  p_adjust = NULL,
  df = NULL,
  verbose = TRUE,
  ...
)
# S3 method for ggeffects
hypothesis_test(
  model,
  test = "pairwise",
  p_adjust = NULL,
  df = NULL,
  verbose = TRUE,
  ...
)

Value

A data frame containing predictions (e.g. for test = NULL), contrasts or pairwise comparisons of adjusted predictions or estimated marginal means.

Arguments

model

A fitted model object, or an object of class ggeffects.

...

Arguments passed down to data_grid() when creating the reference grid.

terms

Character vector with the names of the focal terms from model, for which contrasts or comparisons should be displayed. At least one term is required, maximum length is three terms. If the first focal term is numeric, contrasts or comparisons for the slopes of this numeric predictor are computed (possibly grouped by the levels of further categorical focal predictors).

test

Hypothesis to test. By default, pairwise-comparisons are conducted. See section Introduction into contrasts and pairwise comparisons.

p_adjust

Character vector, if not NULL, indicates the method to adjust p-values. See stats::p.adjust() for details. Further possible adjustment methods are "tukey" or "sidak". Some caution is necessary when adjusting p-value for multiple comparisons. See also section P-value adjustment below.

df

Degrees of freedom that will be used to compute the p-values and confidence intervals. If NULL, degrees of freedom will be extracted from the model using insight::get_df() with type = "wald".

verbose

Toggle messages and warnings.

Introduction into contrasts and pairwise comparisons

There are many ways to test contrasts or pairwise comparisons. A detailed introduction with many (visual) examples is shown in this vignette.

P-value adjustment for multiple comparisons

Note that p-value adjustment for methods supported by p.adjust() (see also p.adjust.methods), each row is considered as one set of comparisons, no matter which test was specified. That is, for instance, when hypothesis_test() returns eight rows of predictions (when test = NULL), and p_adjust = "bonferroni", the p-values are adjusted in the same way as if we had a test of pairwise comparisons (test = "pairwise") where eight rows of comparisons are returned. For methods "tukey" or "sidak", a rank adjustment is done based on the number of combinations of levels from the focal predictors in terms. Thus, the latter two methods may be useful for certain tests only, in particular pairwise comparisons.

Examples

if (FALSE) {
if (requireNamespace("marginaleffects") && interactive()) {
  data(efc)
  efc$c172code <- as.factor(efc$c172code)
  efc$c161sex <- as.factor(efc$c161sex)
  levels(efc$c161sex) <- c("male", "female")
  m <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)

  # direct computation of comparisons
  hypothesis_test(m, "c172code")

  # passing a `ggeffects` object
  pred <- ggpredict(m, "c172code")
  hypothesis_test(pred)

  # test for slope
  hypothesis_test(m, "c12hour")

  # interaction - contrasts by groups
  m <- lm(barthtot ~ c12hour + c161sex * c172code + neg_c_7, data = efc)
  hypothesis_test(m, c("c161sex", "c172code"), test = NULL)

  # interaction - pairwise comparisons by groups
  hypothesis_test(m, c("c161sex", "c172code"))

  # p-value adjustment
  hypothesis_test(m, c("c161sex", "c172code"), p_adjust = "tukey")

  # specific comparisons
  hypothesis_test(m, c("c161sex", "c172code"), test = "b2 = b1")

  # interaction - slope by groups
  m <- lm(barthtot ~ c12hour + neg_c_7 * c172code + c161sex, data = efc)
  hypothesis_test(m, c("neg_c_7", "c172code"))
}
}