Bar Plot of cook's distance to detect observations that strongly influence fitted values of the model.
ols_plot_cooksd_bar(model, print_plot = TRUE)
| model | An object of class |
|---|---|
| print_plot | logical; if |
ols_plot_cooksd_bar returns a list containing the
following components:
a tibble with observation number and cooks distance that exceed threshold
threshold for classifying an observation as an outlier
Cook's distance was introduced by American statistician R Dennis Cook in 1977. It is used to identify influential data points. It depends on both the residual and leverage i.e it takes it account both the x value and y value of the observation.
Steps to compute Cook's distance:
Delete observations one at a time.
Refit the regression model on remaining \(n - 1\) observations
examine how much all of the fitted values change when the ith observation is deleted.
A data point having a large cook's d indicates that the data point strongly influences the fitted values.
ols_cooksd_barplot() has been deprecated. Instead use ols_plot_cooksd_bar().
[ols_plot_cooksd_chart()]