Panel of plots to detect influential observations using DFBETAs.

blr_plot_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
\(2/\sqrt(n)\) 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

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