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Panel of plots to detect influential observations using DFBETAs.


ols_plot_dfbetas(model, print_plot = TRUE)



An object of class lm.


logical; if TRUE, prints the plot else returns a plot object.


list; ols_plot_dfbetas returns a list of data.frame (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.


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.


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.


model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)