# Compute partial residuals from a data grid

Source:`R/residualize_over_grid.R`

`residualize_over_grid.Rd`

This 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()`

.

## Usage

```
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, ...)
```

## Arguments

- grid
A data frame representing the data grid, or an object of class

`ggeffects`

, as returned by`predict_response()`

.- model
The model for which to compute partial residuals. The data grid

`grid`

should match to predictors in the model.- ...
Currently not used.

- predictor_name
The name of the focal predictor, for which partial residuals are computed.

- protect_names
Logical, if

`TRUE`

, preserves column names from the`ggeffects`

objects that is used as`grid`

.

## Partial Residuals

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.

## References

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.

## Examples

```
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
```