
Plot for detecting influential observations using DFFITs.
ols_plot_dffits(model, size_adj_threshold = TRUE, print_plot = TRUE)
ols_plot_dffits
returns a list containing the
following components:
a data.frame
with observation number and DFFITs
that exceed threshold
threshold
for classifying an observation as an outlier
An object of class lm
.
logical; if TRUE
(the default), size
adjusted threshold is used to determine influential observations.
logical; if TRUE
, prints the plot else returns a
plot object.
DFFIT - difference in fits, is used to identify influential data points. It quantifies the number of standard deviations that the fitted value changes when the ith data point is omitted.
Steps to compute DFFITs:
Delete observations one at a time.
Refit the regression model on remaining
examine how much all of the fitted values change when the ith observation is deleted.
An observation is deemed influential if the absolute value of its DFFITS value is greater than:
A size-adjusted cutoff recommended by Belsley, Kuh, and Welsch is
where n
is the number of observations and p
is the number of predictors including intercept.
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. ISBN 0-471-05856-4.
ols_plot_dfbetas()
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_plot_dffits(model)
ols_plot_dffits(model, size_adj_threshold = FALSE)
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