R/residualize_over_grid.R
residualize_over_grid.RdThis function computes partial residuals based on a data grid,
where the data grid is usually a data frame from all combinations of factor
variables or certain values of numeric vectors. This data grid is usually used
as newdata argument in predict(), and can be created with
new_data().
residualize_over_grid(grid, model, ...)
# S3 method for class 'data.frame'
residualize_over_grid(grid, model, predictor_name, ...)
# S3 method for class 'ggeffects'
residualize_over_grid(grid, model, protect_names = TRUE, ...)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.
Currently not used.
The name of the focal predictor, for which partial residuals are computed.
Logical, if TRUE, preserves column names from the
ggeffects objects that is used as grid.
A data frame with residuals for the focal predictor.
For generalized linear models (glms), residualized scores are
computed as inv.link(link(Y) + r) where Y are the predicted
values on the response scale, and r are the working residuals.
For (generalized) linear mixed models, the random effect are also
partialled out.
Fox J, Weisberg S. Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals. Journal of Statistical Software 2018;87.
library(ggeffects)
set.seed(1234)
x <- rnorm(200)
z <- rnorm(200)
# quadratic relationship
y <- 2 * x + x^2 + 4 * z + rnorm(200)
d <- data.frame(x, y, z)
model <- lm(y ~ x + z, data = d)
pr <- predict_response(model, c("x [all]", "z"))
head(residualize_over_grid(pr, model))
#> x group predicted
#> 53 -1.207 0.07 -1.797239
#> 402 0.277 1.08 4.888712
#> 518 1.084 0.07 3.232202
#> 9 -2.346 1.08 4.133561
#> 428 0.429 0.07 1.801594
#> 441 0.506 1.08 5.659527