This function extracts the raw data points (i.e. the data
that was used to fit the model) and "averages" (i.e. "collapses") the
response variable over the levels of the grouping factor given in
collapse_by. Only works with mixed models.
collapse_by_group(grid, model, collapse_by = NULL, residuals = FALSE)A data frame representing the data grid, or an object of class
ggeffects, as returned by predict_response().
The model for which to compute partial residuals. The data grid
grid should match to predictors in the model.
Name of the (random effects) grouping factor. Data is collapsed by the levels of this factor.
Logical, if TRUE, collapsed partial residuals instead
of raw data by the levels of the grouping factor.
A data frame with raw data points, averaged over the levels of
the given grouping factor from the random effects. The group level of
the random effect is saved in the column "random".
library(ggeffects)
data(efc, package = "ggeffects")
efc$e15relat <- as.factor(efc$e15relat)
efc$c161sex <- as.factor(efc$c161sex)
levels(efc$c161sex) <- c("male", "female")
model <- lme4::lmer(neg_c_7 ~ c161sex + (1 | e15relat), data = efc)
me <- predict_response(model, terms = "c161sex")
head(attributes(me)$rawdata)
#> response x group facet panel rowname
#> 1 12 2 1 1 1 1
#> 2 20 2 1 1 1 2
#> 3 11 1 1 1 1 3
#> 4 10 1 1 1 1 4
#> 5 12 2 1 1 1 5
#> 6 19 1 1 1 1 6
collapse_by_group(me, model, "e15relat")
#> x group_col facet panel random response
#> 1 1 1 1 1 1 12.297872
#> 2 2 1 1 1 1 13.347107
#> 3 1 1 1 1 2 11.585586
#> 4 2 1 1 1 2 12.118310
#> 5 1 1 1 1 3 12.166667
#> 6 2 1 1 1 3 10.545455
#> 7 1 1 1 1 4 10.750000
#> 8 2 1 1 1 4 11.726027
#> 9 1 1 1 1 5 11.333333
#> 10 2 1 1 1 5 10.235294
#> 11 1 1 1 1 6 8.200000
#> 12 2 1 1 1 6 9.235294
#> 13 1 1 1 1 7 13.000000
#> 14 2 1 1 1 7 10.400000
#> 15 1 1 1 1 8 9.666667
#> 16 2 1 1 1 8 10.955882