Panel of plots to detect influential observations using DFBETAs.
Usage
blr_plot_dfbetas_panel(model, print_plot = TRUE)
Arguments
model
An object of class glm.
print_plot
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.
# NOT RUN {model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
blr_plot_dfbetas_panel(model)
# }# NOT RUN {# }