Panel of plots to detect influential observations using DFBETAs.

blr_plot_dfbetas_panel(model, print_plot = TRUE)

## Arguments

model An object of class glm. logical; if TRUE, prints the plot else returns a plot object.

## Value

list; 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 outlier/influential observation.

## 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.

## Examples

if (FALSE) {
model <- glm(honcomp ~ female + read + science, data = hsb2,