ols_plot_cooksd_bar function

Cooks' D bar plot

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

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

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.

Returns

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

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

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)

See Also

ols_plot_cooksd_chart()