blr_plot_dfbetas_panel {blorr} | R Documentation |
Panel of plots to detect influential observations using DFBETAs.
blr_plot_dfbetas_panel(model, print_plot = TRUE)
model |
An object of class |
print_plot |
logical; if |
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.
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.
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)
## End(Not run)