Chart of cook's distance to detect observations that strongly influence fitted values of the model.

## Arguments

- model
An object of class

`lm`

.- type
An integer between 1 and 5 selecting one of the 6 methods for computing the threshold.

- threshold
Threshold for detecting outliers.

- print_plot
logical; if

`TRUE`

, prints the plot else returns a plot object.

## Value

`ols_plot_cooksd_chart`

returns a list containing the
following components:

- outliers
a

`data.frame`

with observation number and`cooks distance`

that exceed`threshold`

- threshold
`threshold`

for classifying an observation as an outlier

## Details

Cook's distance was introduced by American statistician R Dennis Cook in
1977. It is used to identify influential data points. It depends on both the
residual and leverage i.e it takes it account both the *x* value and
*y* value of the observation.

Steps to compute Cook's distance:

Delete observations one at a time.

Refit the regression model on remaining \(n - 1\) observations

exmine how much all of the fitted values change when the ith observation is deleted.

A data point having a large cook's d indicates that the data point strongly influences the fitted values. There are several methods/formulas to compute the threshold used for detecting or classifying observations as outliers and we list them below.

**Type 1**: 4 / n**Type 2**: 4 / (n - k - 1)**Type 3**: ~1**Type 4**: 1 / (n - k - 1)**Type 5**: 3 * mean(Vector of cook's distance values)

where **n** and **k** stand for

**n**: Number of observations**k**: Number of predictors

## Examples

```
model <- lm(mpg ~ disp + hp + wt, data = mtcars)
ols_plot_cooksd_chart(model)
ols_plot_cooksd_chart(model, type = 4)
ols_plot_cooksd_chart(model, threshold = 0.2)
```