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.
Arguments
- grid
A data frame representing the data grid, or an object of class
ggeffects, as returned bypredict_response().- model
The model for which to compute partial residuals. The data grid
gridshould match to predictors in the model.- collapse_by
Name of the (random effects) grouping factor. Data is collapsed by the levels of this factor.
- residuals
Logical, if
TRUE, collapsed partial residuals instead of raw data by the levels of the grouping factor.
Value
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".
Examples
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 rowname
#> 1 12 2 1 1 1
#> 2 20 2 1 1 2
#> 3 11 1 1 1 3
#> 4 10 1 1 1 4
#> 5 12 2 1 1 5
#> 6 19 1 1 1 6
collapse_by_group(me, model, "e15relat")
#> x group_col facet random response
#> 1 1 1 1 1 12.297872
#> 2 2 1 1 1 13.347107
#> 3 1 1 1 2 11.585586
#> 4 2 1 1 2 12.118310
#> 5 1 1 1 3 12.166667
#> 6 2 1 1 3 10.545455
#> 7 1 1 1 4 10.750000
#> 8 2 1 1 4 11.726027
#> 9 1 1 1 5 11.333333
#> 10 2 1 1 5 10.235294
#> 11 1 1 1 6 8.200000
#> 12 2 1 1 6 9.235294
#> 13 1 1 1 7 13.000000
#> 14 2 1 1 7 10.400000
#> 15 1 1 1 8 9.666667
#> 16 2 1 1 8 10.955882