This function is designed to facilitate the creation of partial
residual plots, in which you can plot observed data alongside model
predictions. The difference is instead of the *actual* observed data, the
outcome variable is adjusted for the effects of the covariates.

## Usage

```
partialize(model, ...)
# S3 method for default
partialize(
model,
vars = NULL,
data = NULL,
at = NULL,
center = TRUE,
scale = c("response", "link"),
set.offset = 1,
...
)
```

## Arguments

- model
A regression model.

- ...
Ignored.

- vars
The variable(s) to

*not*adjust for, as a string (or vector of strings). If I want to show the effect of`x`

adjusting for the effect of`z`

, then I would make`"x"`

the`vars`

argument.- data
Optionally, provide the data used to fit the model (or some other data frame with the same variables). Otherwise, it will be retrieved from the model or the global environment.

- at
If you want to manually set the values of other variables in the model, do so by providing a named list where the names are the variables and the list values are vectors of the values. This can be useful especially when you are exploring interactions or other conditional predictions.

- center
Set numeric covariates to their mean? Default is TRUE. You may also just provide a vector of names (as strings) of covariates to center. Note that for

`svyglm`

models, the survey-weighted means are used. For models with weights, these are weighted means.- scale
For GLMs, should the outcome variable be returned on the link scale or response scale? Default is

`"response"`

.- set.offset
For models with an offset (e.g., Poisson models), sets an offset for the predicted values. All predicted values will have the same offset. By default, this is set to 1, which makes the predicted values a proportion. See details for more about offset support.

## Details

The main use for working with partial residuals rather than the observed values is to explore patterns in the model fit with respect to one or more variables while "controlling out" the effects of others. Plotting a predicted line along with observed data may make a very well-fitting model look as if it is a poor fit if a lot of variation is accounted for by variables other than the one on the x-axis.

I advise consulting Fox and Weisberg (available free) for more details
on what partial residuals are. This function is designed to produce
data in a similar format to `effects::Effect()`

when that function has
`residuals`

set to `TRUE`

and is plotted. I wanted a more modular function
to produce the data separately. To be clear, the developers of the `effects`

package have nothing to do with this function; `partialize`` is merely
designed to replicate some of that functionality.