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Plot interaction effects of two predictors in linear regression model

`plotInteraction(mdl,var1,var2)`

`plotInteraction(mdl,var1,var2,ptype)`

`h = plotInteraction(___)`

`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 line objects using any of the input argument combinations in the previous
syntaxes. Use `h`

= plotInteraction(___)`h`

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

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.

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.

`CompactLinearModel`

| `LinearModel`

| `plotAdjustedResponse`

| `plotEffects`