Panel of plots to detect influential observations using DFBETAs.

ols_plot_dfbetas(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

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.

## Details

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.

## Deprecated Function

`ols_dfbetas_panel()`

has been deprecated. Instead use `ols_plot_dfbetas()`

.

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

## See also

## Examples