R/test_predictions.R
test_predictions.RdFunction to test differences of adjusted predictions for
statistical significance. This is usually called contrasts or (pairwise)
comparisons, or "marginal effects". hypothesis_test() is an alias.
test_predictions(object, ...)
hypothesis_test(object, ...)
# Default S3 method
test_predictions(
object,
terms = NULL,
by = NULL,
test = "pairwise",
test_args = NULL,
p_adjust = NULL,
df = NULL,
ci_level = 0.95,
margin = "mean_reference",
condition = NULL,
collapse_levels = FALSE,
verbose = TRUE,
...
)
# S3 method for class 'ggeffects'
test_predictions(
object,
by = NULL,
test = "pairwise",
p_adjust = NULL,
df = NULL,
collapse_levels = FALSE,
engine = "emmeans",
verbose = TRUE,
...
)A fitted model object, or an object of class ggeffects. If
object is of class ggeffects, arguments terms, margin and ci_level
are taken from the ggeffects object and don't need to be specified.
Arguments passed down to data_grid() when creating the reference
grid.
If object is an object of class ggeffects, the same terms
argument is used as for the predictions, i.e. terms can be ignored. Else,
if object is a model object, terms must be a character vector with the
names of the focal terms from object, 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).
Character vector specifying the names of predictors to condition on.
Hypothesis test is then carried out for focal terms by each level of by
variables. This is useful especially for interaction terms, where we want
to test the interaction within "groups". by is only relevant for
categorical predictors.
Hypothesis to test, defined as character string, formula, or data frame. Can be one of:
String:
"pairwise" (default), to test pairwise comparisons.
"trend" (or "slope") to test for the linear trend/slope of (usually)
continuous predictors. These options are just aliases for setting
trend = NULL.
"contrast" to test simple contrasts (i.e. each level is tested against
the average over all levels).
"exclude" to test simple contrasts (i.e. each level is tested against
the average over all other levels, excluding the contrast that is being
tested).
"interaction" to test interaction contrasts (difference-in-difference
contrasts). More flexible interaction contrasts can be calcualted using
the test_args argument.
"consecutive" to test contrasts between consecutive levels of a predictor.
"polynomial" to test orthogonal polynomial contrasts, assuming
equally-spaced factor levels.
A data frame with custom contrasts. See 'Examples'.
NULL, in which case simple contrasts are computed.
Optional arguments passed to test, typically provided as
named list. Only applies to those options that use the emmeans package
as backend, e.g. if test = "interaction", test_args will be passed to
emmeans::contrast(interaction = test_args). For other emmeans options
(like "contrast", "exclude", "consecutive" and so on), test_args will
be passed to the option argument in emmeans::contrast().
Character vector, if not NULL, indicates the method to
adjust p-values. See stats::p.adjust() or stats::p.adjust.methods 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.
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".
Numeric, the level of the confidence intervals. If object
is an object of class ggeffects, the same ci_level argument is used as
for the predictions, i.e. ci_level can be ignored.
Character string, indicates the method how to marginalize over
non-focal terms. See predict_response() for details. If object is an
object of class ggeffects, the same margin argument is used as for the
predictions, i.e. margin can be ignored.
Named character vector, which indicates covariates that
should be held constant at specific values, for instance
condition = c(covariate1 = 20, covariate2 = 5).
Logical, if TRUE, term labels that refer to identical
levels are no longer separated by "-", but instead collapsed into a unique
term label (e.g., "level a-level a" becomes "level a"). See 'Examples'.
Toggle messages and warnings.
Character string, indicates the package to use for computing
contrasts and comparisons. Setting engine = "emmeans" (default) provides
some additional test options: "interaction" to calculate interaction
contrasts, "consecutive" to calculate contrasts between consecutive levels
of a predictor, or a data frame with custom contrasts (see also test). The
second option, engine = "ggeffects". However, this option offers less
features as the default engine, "emmeans". It can be faster in some cases,
though, and works for comparing predicted random effects in mixed models, or
predicted probabilities of the zero-inflation component.
A data frame containing predictions (e.g. for test = NULL),
contrasts or pairwise comparisons of adjusted predictions or estimated
marginal means.
A simple workflow includes calculating adjusted predictions and passing the
results directly to test_predictions(), e.g.:
# 1. fit your model
model <- lm(mpg ~ hp + wt + am, data = mtcars)
# 2. calculate adjusted predictions
pr <- predict_response(model, "am")
pr
# 3. test pairwise comparisons
test_predictions(pr)See also this vignette.
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 test_predictions()
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.
The verbose argument can be used to display or silence messages and
warnings. Furthermore, options() can be used to set defaults for the
print() and print_html() method. The following options are available,
which can simply be run in the console:
ggeffects_ci_brackets: Define a character vector of length two, indicating
the opening and closing parentheses that encompass the confidence intervals
values, e.g. options(ggeffects_ci_brackets = c("[", "]")).
ggeffects_collapse_ci: Logical, if TRUE, the columns with predicted
values (or contrasts) and confidence intervals are collapsed into one
column, e.g. options(ggeffects_collapse_ci = TRUE).
ggeffects_collapse_p: Logical, if TRUE, the columns with predicted
values (or contrasts) and p-values are collapsed into one column, e.g.
options(ggeffects_collapse_p = TRUE). Note that p-values are replaced
by asterisk-symbols (stars) or empty strings when ggeffects_collapse_p = TRUE,
depending on the significance level.
ggeffects_collapse_tables: Logical, if TRUE, multiple tables for
subgroups are combined into one table. Only works when there is more than
one focal term, e.g. options(ggeffects_collapse_tables = TRUE).
ggeffects_output_format: String, either "text", "markdown" or "html".
Defines the default output format from predict_response(). If "html", a
formatted HTML table is created and printed to the view pane. "markdown"
creates a markdown-formatted table inside Rmarkdown documents, and prints
a text-format table to the console when used interactively. If "text" or
NULL, a formatted table is printed to the console, e.g.
options(ggeffects_output_format = "html").
ggeffects_html_engine: String, either "tt" or "gt". Defines the default
engine to use for printing HTML tables. If "tt", the tinytable package
is used, if "gt", the gt package is used, e.g.
options(ggeffects_html_engine = "gt").
Use options(<option_name> = NULL) to remove the option.
Esarey, J., & Sumner, J. L. (2017). Marginal effects in interaction models: Determining and controlling the false positive rate. Comparative Political Studies, 1–33. Advance online publication. doi: 10.1177/0010414017730080
There is also an equivalence_test() method in the parameters
package (parameters::equivalence_test.lm()), which can be used to
test contrasts or comparisons for practical equivalence. This method also
has a plot() method, hence it is possible to do something like:
library(parameters)
predict_response(model, focal_terms) |>
equivalence_test() |>
plot()if (FALSE) { # all(insight::check_if_installed(c("parameters", "emmeans"), quietly = TRUE)) && interactive()
# \donttest{
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
test_predictions(m, "c172code")
# passing a `ggeffects` object
pred <- predict_response(m, "c172code")
test_predictions(pred)
# test for slope
test_predictions(m, "c12hour")
# interaction - contrasts by groups
m <- lm(barthtot ~ c12hour + c161sex * c172code + neg_c_7, data = efc)
test_predictions(m, c("c161sex", "c172code"), test = NULL)
# interaction - pairwise comparisons by groups
test_predictions(m, c("c161sex", "c172code"))
# interaction - collapse unique levels
test_predictions(m, c("c161sex", "c172code"), collapse_levels = TRUE)
# p-value adjustment
test_predictions(m, c("c161sex", "c172code"), p_adjust = "tukey")
# not all comparisons, only by specific group levels
test_predictions(m, "c172code", by = "c161sex")
# interaction - slope by groups
m <- lm(barthtot ~ c12hour + neg_c_7 * c172code + c161sex, data = efc)
test_predictions(m, c("neg_c_7", "c172code"))
# Interaction and consecutive contrasts -----------------
# -------------------------------------------------------
data(coffee_data, package = "ggeffects")
m <- lm(alertness ~ time * coffee + sex, data = coffee_data)
# consecutive contrasts
test_predictions(m, "time", by = "coffee", test = "consecutive")
# same as (using formula):
pr <- predict_response(m, c("time", "coffee"))
test_predictions(pr, test = difference ~ sequential | coffee)
# interaction contrasts - difference-in-difference comparisons
pr <- predict_response(m, c("time", "coffee"), margin = "marginalmeans")
test_predictions(pr, test = "interaction")
# Ratio contrasts ---------------------------------------
# -------------------------------------------------------
test_predictions(test = ratio ~ reference | coffee)
# Custom contrasts --------------------------------------
# -------------------------------------------------------
wakeup_time <- data.frame(
"wakeup vs later" = c(-2, 1, 1) / 2, # make sure each "side" sums to (+/-)1!
"start vs end of day" = c(-1, 0, 1)
)
test_predictions(m, "time", by = "coffee", test = wakeup_time)
# }
}