Deleted studentized residual vs fitted values plotSource:
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.
An object of class
Threshold for detecting outliers. Default is 2.
TRUE, prints the plot else returns a plot object.
ols_plot_resid_stud_fit returns a list containing the
data.framewith observation number, fitted values and deleted studentized residuals that exceed the
thresholdfor classifying observations as outliers/influential observations
thresholdfor 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.
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_plot_resid_stud_fit(model) ols_plot_resid_stud_fit(model, threshold = 3)