ols_plot_cooksd_bar: Cooks' D bar plot

Description

Bar Plot of cook's distance to detect observations that strongly influence fitted values of the model.

Usage

ols_plot_cooksd_bar(model, type = 1, threshold = NULL, print_plot = TRUE)

Value

ols_plot_cooksd_bar returns a list containing the following components:

outliers: a data.frame with observation number and cooks distance that exceed threshold
threshold: threshold for classifying an observation as an outlier

Arguments

model: An object of class lm.
type: An integer between 1 and 5 selecting one of the 5 methods for computing the threshold.
threshold: Threshold for detecting outliers.
print_plot: logical; if TRUE, prints the plot else returns a plot object.

Details

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. There are several methods/formulas to compute the threshold used for detecting or classifying observations as outliers and we list them below.

Type 1 : 4 / n
Type 2 : 4 / (n - k - 1)
Type 3 : ~1
Type 4 : 1 / (n - k - 1)
Type 5 : 3 * mean(Vector of cook's distance values)

where n and k stand for

n: Number of observations
k: Number of predictors

Examples

Run this code

model <- lm(mpg ~ disp + hp + wt, data = mtcars)
ols_plot_cooksd_bar(model)
ols_plot_cooksd_bar(model, type = 4)
ols_plot_cooksd_bar(model, threshold = 0.2)