Panel of plots to detect influential observations using DFBETAs.
blr_plot_dfbetas_panel(model, print_plot = TRUE)
An object of class
blr_dfbetas_panel returns a list of tibbles (for
intercept and each predictor) with the observation number and DFBETA of
observations that exceed the threshold for classifying an observation as an
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.
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.