visreg2d: Visualization of regression functions for two variables

Description

A function used to visualize how two variables interact to affect the response in regression models.

Usage

visreg2d(fit, xvar, yvar, type=c("conditional","effect"),
nn=ifelse(plot.type=="persp",49,99), plot.type=c("image","persp","rgl"),
trans=as.numeric, scale=c("linear","response"), cond=list(),
whitespace=0.2, ...)

Arguments

fit

This is the fitted model object you wish to visualize.

xvar

This is the variable to be put on the x-axis of your plot. Both continuous variables and factors are supported.

yvar

This is the variable to be put on the y-axis of your plot. Both continuous variables and factors are supported.

type

The type of plot to be produced. The following options are supported:

If 'conditional' is selected, the plot returned shows the value of the variable on the x-axis and the change in response on the y-axis, holding all other variables constant

Resolution of the three dimensional plot. Higher values will results in a smoother looking plot.

plot.type

The style of plot to be produced. The following three options are supported:

image, a filled contour plot.
persp, a 3 dimensional perspective plot.
rgl, a version of the perspective plot that can be rotated. Note: requires the r

trans

Allows transformations to the response. A function should be passed.

scale

If response is selected and a glm is passed as the fit parameter, the response will automatically be transformed.

cond

Used to set variables to specific values. Can also be used to select the reference point for an effect plot. Named lists should be passed. All variables left unspecified will be filled in with the median/most common category (see examples).

whitespace

When x (or y) is a factor, whitespace determines the ammount of space in between factors on the x (or y) axis.

...

Graphical parameters can be passed to the function to customize the plots.

Value

In addition to providing plots, the visreg function also invisibly returns the data frames and estimates used in the creation of its plots.

References

Breheny, P. and Burchett, W. (2012), Visualizing regression models using visreg. http://web.as.uky.edu/statistics/users/pbreheny/publications/visreg.pdf

Examples

Run this code

ozone <- airquality[complete.cases(airquality),]

fit <- lm(Ozone ~ Solar.R + Wind + Temp + I(Wind^2) + I(Temp^2) + I(Wind*Temp)+I(Wind*Temp^2) + I(Temp*Wind^2) + I(Temp^2*Wind^2),data=ozone)

visreg2d(fit,x="Wind",y="Temp",plot.type="image")
visreg2d(fit,x="Wind",y="Temp",plot.type="persp")

## Requires the rgl package
visreg2d(fit,x="Wind",y="Temp",plot.type="rgl")