Plot for detecting violation of assumptions about residuals such as non-linearity, constant variances and outliers. It can also be used to examine model fit.
ols_plot_resid_stud_fit(model, threshold = NULL, print_plot = TRUE)
An object of class
Threshold for detecting outliers. Default is 2.
ols_plot_resid_stud_fit returns a list containing the
data.frame with observation number, fitted values and deleted studentized
residuals that exceed the
threshold for classifying observations as
threshold for classifying an observation as an outlier/influential observation
Studentized deleted residuals (or externally studentized residuals) is the deleted residual divided by its estimated standard deviation. Studentized residuals are going to be more effective for detecting outlying Y observations than standardized residuals. If an observation has an externally studentized residual that is larger than 2 (in absolute value) we can call it an outlier.
ols_dsrvsp_plot() has been deprecated. Instead use
[ols_plot_resid_lev()], [ols_plot_resid_stand()], [ols_plot_resid_stud()]
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_plot_resid_stud_fit(model)ols_plot_resid_stud_fit(model, threshold = 3)