Plot for detecting influential observations using DFFITs.

ols_plot_dffits(model, print_plot = TRUE)

## Arguments

model An object of class lm. logical; if TRUE, prints the plot else returns a plot object.

## Value

ols_plot_dffits returns a list containing the following components:

outliers

a data.frame with observation number and DFFITs that exceed threshold

threshold

threshold for classifying an observation as an outlier

## Details

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 $$n - 1$$ observations

• 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: $$2\sqrt(p + 1) / (n - p -1)$$

where n is the number of observations and p is the number of predictors including intercept.

## Deprecated Function

ols_dffits_plot() has been deprecated. Instead use ols_plot_dffits().

## 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. ISBN 0-471-05856-4.

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