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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
  vars = NULL,
  data = NULL,
  at = NULL,
  center = TRUE,
  scale = c("response", "link"),
  set.offset = 1,



A regression model.




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.


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.


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.


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.


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


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.