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.

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

model | A regression model. |
---|---|

... | Ignored. |

vars | The variable(s) to |

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 |

scale | For GLMs, should the outcome variable be returned on the link
scale or response scale? Default is |

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. |

`data`

plus the residualized outcome variable.

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.

Fox, J., & Weisberg, S. (2018). Visualizing fit and lack of fit in complex
regression models with predictor effect plots and partial residuals.
*Journal of Statistical Software*, *87*(9), 1–27.
https://doi.org/10.18637/jss.v087.i09