Panel of plots to detect influential observations using DFBETAs.
Usage
ols_plot_dfbetas(model, print_plot = TRUE)
Value
list; ols_plot_dfbetas returns a list of data.frame (for intercept and each predictor)
with the observation number and DFBETA of observations that exceed the threshold for classifying
an observation as an outlier/influential observation.
Arguments
model
An object of class lm.
print_plot
logical; if TRUE, prints the plot else returns a plot object.
Details
DFBETA measures the difference in each parameter estimate with and without
the influential point. There is a DFBETA for each data point i.e if there are
n observations and k variables, there will be \(n * k\) DFBETAs. In
general, large values of DFBETAS indicate observations that are influential
in estimating a given parameter. Belsley, Kuh, and Welsch recommend 2 as a
general cutoff value to indicate influential observations and
\(2/\sqrt(n)\) as a size-adjusted cutoff.
References
Belsley, David A.; Kuh, Edwin; Welsh, Roy E. (1980). Regression
Diagnostics: Identifying Influential Data and Sources of Collinearity.
Wiley Series in Probability and Mathematical Statistics.
New York: John Wiley & Sons. pp. ISBN 0-471-05856-4.