# plotInteraction

Plot interaction effects of two predictors in linear regression model

## Syntax

## Description

`plotInteraction(`

creates a plot of the main effects of the
two selected predictors `mdl`

,`var1`

,`var2`

)`var1`

and `var2`

and
their conditional effects
in the linear regression model `mdl`

. Horizontal lines through
the effect values indicate their 95% confidence intervals.

`plotInteraction(`

specifies the plot type `mdl`

,`var1`

,`var2`

,`ptype`

)`ptype`

. For example, if
`ptype`

is `'predictions'`

, then
`plotInteraction`

plots the adjusted response function as a
function of the second predictor, with the first predictor fixed at specific values.
For details, see Conditional Effect.

returns a vector of line objects `h`

= plotInteraction(___)`h`

. Use `h`

to modify the properties of a specific line after you create the plot. For a list of
properties, see Line Properties.

## Examples

## Input Arguments

## Output Arguments

## More About

## Tips

The data cursor displays the values of the selected plot point in a data tip (small text box located next to the data point). The data tip includes the

*x*-axis and*y*-axis values for the selected point, along with the observation name or number.

## Alternative Functionality

A

`LinearModel`

object provides multiple plotting functions.When creating a model, use

`plotAdded`

to understand the effect of adding or removing a predictor variable.When verifying a model, use

`plotDiagnostics`

to find questionable data and to understand the effect of each observation. Also, use`plotResiduals`

to analyze the residuals of the model.After fitting a model, use

`plotAdjustedResponse`

,`plotPartialDependence`

, and`plotEffects`

to understand the effect of a particular predictor. Use`plotInteraction`

to understand the interaction effect between two predictors. Also, use`plotSlice`

to plot slices through the prediction surface.

## Extended Capabilities

## Version History

**Introduced in R2012a**