Panel of plots to detect influential observations using DFBETAs.

blr_dfbetas_panel(model)

Arguments

model	an object of class glm

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/√(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

# NOT RUN {
model <- glm(honcomp ~ female + read + science, data = blorr::hsb2,
family = binomial(link = 'logit'))

blr_dfbetas_panel(model)
# }