Plot for detecting influential observations using DFFITs.

## 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.

## Examples

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