# CookD: Calculates and plots Cook's distances for a Linear (Mixed)... In predictmeans: Calculate Predicted Means for Linear Models

## Description

This function produces Cook's distance plots for a linear model obtained from functions `aov`, `lm`, `glm`, `gls`, `lme`, or `lmer`.

## Usage

 `1` ```CookD(model, group=NULL, plot=TRUE, idn=3, newwd=TRUE) ```

## Arguments

 `model` Model object returned by `aov`, `lm`, `glm`, `gls`, `lme`, and `lmer`. `group` Name (in "quotes") for indicating how observations are deleted for Cook's distance calculation. If `group!=NULL` then deletions will be along levels of `group` variable, otherwise, will be along individual observations. `plot` A logical variable; if it is true, a plot of Cook's distance will be presented. The default is TRUE. `idn` An integer indicating the number of top Cook's distances to be labelled in the plot. The default value is 3. `newwd` A logical variable to indicate whether to print graph in a new window. The default value is TRUE.

## Author(s)

Dongwen Luo, Siva Ganesh and John Koolaard

## Examples

 ```1 2 3 4 5 6``` ```library(predictmeans) Oats\$nitro <- factor(Oats\$nitro) fm <- lme(yield ~ nitro*Variety, random=~1|Block/Variety, data=Oats) # library(lme4) # fm <- lmer(yield ~ nitro*Variety+(1|Block/Variety), data=Oats) CookD(fm) ```

### Example output

```Loading required package: lme4