Plot for detecting influential observations using DFFITs.

ols_plot_dffits(model, print_plot = TRUE)

## Arguments

model |
An object of class `lm` . |

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

## Value

`ols_plot_dffits`

returns a list containing the
following components:

outliersa `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.

## See also

## Examples