Deleted studentized residual vs fitted values plot

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.

Usage

ols_plot_resid_stud_fit(model, threshold = NULL, print_plot = TRUE)

Arguments

model

An object of class lm.

threshold

Threshold for detecting outliers. Default is 2.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

Value

ols_plot_resid_stud_fit returns a list containing the following components:

outliers

a data.frame with observation number, fitted values and deleted studentized residuals that exceed the threshold for classifying observations as outliers/influential observations

threshold

threshold for classifying an observation as an outlier/influential observation

Details

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.

See also

ols_plot_resid_lev(), ols_plot_resid_stand(), ols_plot_resid_stud()

Examples

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_plot_resid_stud_fit(model)

ols_plot_resid_stud_fit(model, threshold = 3)