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Plot for detecting influential observations using DFFITs.

## Usage

ols_plot_dffits(model, size_adj_threshold = TRUE, print_plot = TRUE)

## Arguments

model

An object of class lm.

size_adj_threshold

logical; if TRUE (the default), size adjusted threshold is used to determine influential observations.

print_plot

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: $$2\sqrt((p + 1) / (n - p -1))$$

A size-adjusted cutoff recommended by Belsley, Kuh, and Welsch is $$2\sqrt(p / n)$$ and is used by default in olsrr.

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

## 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.

## See also

ols_plot_dfbetas()

## Examples

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

ols_plot_dffits(model, size_adj_threshold = FALSE)