using R to plot interaction plot - r

Question

I have created a model using following
age hrs charges
530.6071 792.10 3474.60
408.6071 489.70 1247.06
108.0357 463.00 1697.07
106.6071 404.15 1676.33
669.4643 384.65 1701.13
556.4643 358.15 1630.30
665.4643 343.85 2468.83
508.4643 342.35 3366.44
106.0357 335.25 2876.82
interaction_model <- rlm( charges~age+hrs+age*hrs, age_vs_hrs_charges_cleaned);
Any idea how i can plot this in 3D?
I already plotted using
library(effects);
plot(effect(term="age:hrs", mod=interaction_model,default.levels=20),multiline=TRUE);
but this is not very clear visualization.
Any help?

Answer 1

There are several ways to do this.
model <- lm( charges~age+hrs+age*hrs, df)
# set up grid of (x,y) values
age <- seq(0,1000, by=20)
hrs <- seq(0,1000, by=20)
gg <- expand.grid(age=age, hrs=hrs)
# prediction from the linear model
gg$charges <-predict(model,newdata=gg)
# contour plot
library(ggplot2)
library(colorRamps)
library(grDevices)
jet.colors <- colorRampPalette(matlab.like(9))
ggplot(gg, aes(x=age, y=hrs, z=charges))+
stat_contour(aes(color=..level..),binwidth=200, size=2)+
scale_color_gradientn(colours=jet.colors(8))
# 3D scatterplot
library(scatterplot3d)
scatterplot3d(gg$age, gg$hrs, gg$charges)
# interactive 3D scatterplot (just a screen shot here)
library(rgl)
plot3d(gg$age,gg$hrs,gg$charges)
# interactive 3D surface plot with shading (screen shot)
colorjet <- jet.colors(100)
open3d()
rgl.surface(x=age, z=hrs, y=0.05*gg$charges,
color=colorzjet[ findInterval(gg$charges, seq(min(gg$charges), max(gg$charges), length=100))] )
axes3d()

Answer 2

A little while ago I wrote a couple of functions to display the results of a (general) linear model, together with colour coded data points, in either 3D (interactive, using rgl) or 2D (using a contour plot) :
# plot predictions of a (general) linear model as a function of two explanatory variables as an image / contour plot
# together with the actual data points
# mean value is used for any other variables in the model
plotImage=function(model=NULL,plotx=NULL,ploty=NULL,plotPoints=T,plotContours=T,plotLegend=F,npp=1000,xlab=NULL,ylab=NULL,zlab=NULL,xlim=NULL,ylim=NULL,pch=16,cex=1.2,lwd=0.1,col.palette=NULL) {
n=npp
require(rockchalk)
require(aqfig)
require(colorRamps)
require(colorspace)
require(MASS)
mf=model.frame(model);emf=rockchalk::model.data(model)
if (is.null(xlab)) xlab=plotx
if (is.null(ylab)) ylab=ploty
if (is.null(zlab)) zlab=names(mf)[[1]]
if (is.null(col.palette)) col.palette=rev(rainbow_hcl(1000,c=100))
x=emf[,plotx];y=emf[,ploty];z=mf[,1]
if (is.null(xlim)) xlim=c(min(x)*0.95,max(x)*1.05)
if (is.null(ylim)) ylim=c(min(y)*0.95,max(y)*1.05)
preds=predictOMatic(model,predVals=c(plotx,ploty),n=npp,divider="seq")
zpred=matrix(preds[,"fit"],npp,npp)
zlim=c(min(c(preds$fit,z)),max(c(preds$fit,z)))
par(mai=c(1.2,1.2,0.5,1.2),fin=c(6.5,6))
graphics::image(x=seq(xlim[1],xlim[2],len=npp),y=seq(ylim[1],ylim[2],len=npp),z=zpred,xlab=xlab,ylab=ylab,col=col.palette,useRaster=T,xaxs="i",yaxs="i")
if (plotContours) graphics::contour(x=seq(xlim[1],xlim[2],len=npp),y=seq(ylim[1],ylim[2],len=npp),z=zpred,xlab=xlab,ylab=ylab,add=T,method="edge")
if (plotPoints) {cols1=col.palette[(z-zlim[1])*999/diff(zlim)+1]
pch1=rep(pch,length(n))
cols2=adjustcolor(cols1,offset=c(-0.3,-0.3,-0.3,1))
pch2=pch-15
points(c(rbind(x,x)),c(rbind(y,y)), cex=cex,col=c(rbind(cols1,cols2)),pch=c(rbind(pch1,pch2)),lwd=lwd) }
box()
if (plotLegend) vertical.image.legend(zlim=zlim,col=col.palette) # TO DO: add z axis label, maybe make legend a bit smaller?
}
# plot predictions of a (general) linear model as a function of two explanatory variables as an interactive 3D plot
# mean value is used for any other variables in the model
plotPlaneFancy=function(model=NULL,plotx1=NULL,plotx2=NULL,plotPoints=T,plotDroplines=T,npp=50,x1lab=NULL,x2lab=NULL,ylab=NULL,x1lim=NULL,x2lim=NULL,cex=1.5,col.palette=NULL,segcol="black",segalpha=0.5,interval="none",confcol="lightgrey",confalpha=0.4,pointsalpha=1,lit=T,outfile="graph.png",aspect=c(1,1,0.3),zoom=1,userMatrix=matrix(c(0.80,-0.60,0.022,0,0.23,0.34,0.91,0,-0.55,-0.72,0.41,0,0,0,0,1),ncol=4,byrow=T),windowRect=c(0,29,1920,1032)) { # or library(colorRamps);col.palette <- matlab.like(1000)
require(rockchalk)
require(rgl)
require(colorRamps)
require(colorspace)
require(MASS)
mf=model.frame(model);emf=rockchalk::model.data(model)
if (is.null(x1lab)) x1lab=plotx1
if (is.null(x2lab)) x2lab=plotx2
if (is.null(ylab)) ylab=names(mf)[[1]]
if (is.null(col.palette)) col.palette=rev(rainbow_hcl(1000,c=100))
x1=emf[,plotx1]
x2=emf[,plotx2]
y=mf[,1]
if (is.null(x1lim)) x1lim=c(min(x1),max(x1))
if (is.null(x2lim)) x2lim=c(min(x2),max(x2))
preds=predictOMatic(model,predVals=c(plotx1,plotx2),n=npp,divider="seq",interval=interval)
ylim=c(min(c(preds$fit,y)),max(c(preds$fit,y)))
open3d(zoom=zoom,userMatrix=userMatrix,windowRect=windowRect)
if (plotPoints) plot3d(x=x1,y=x2,z=y,type="s",col=col.palette[(y-min(y))*999/diff(range(y))+1],size=cex,aspect=aspect,xlab=x1lab,ylab=x2lab,zlab=ylab,lit=lit,alpha=pointsalpha)
if (!plotPoints) plot3d(x=x1,y=x2,z=y,type="n",col=col.palette[(y-min(y))*999/diff(range(y))+1],size=cex,aspect=aspect,xlab=x1lab,ylab=x2lab,zlab=ylab)
if ("lwr" %in% names(preds)) persp3d(x=unique(preds[,plotx1]),y=unique(preds[,plotx2]),z=matrix(preds[,"lwr"],npp,npp),color=confcol, alpha=confalpha, lit=lit, back="lines",add=TRUE)
ypred=matrix(preds[,"fit"],npp,npp)
cols=col.palette[(ypred-min(ypred))*999/diff(range(ypred))+1]
persp3d(x=unique(preds[,plotx1]),y=unique(preds[,plotx2]),z=ypred,color=cols, alpha=0.7, lit=lit, back="lines",add=TRUE)
if ("upr" %in% names(preds)) persp3d(x=unique(preds[,plotx1]),y=unique(preds[,plotx2]),z=matrix(preds[,"upr"],npp,npp),color=confcol, alpha=confalpha, lit=lit, back="lines",add=TRUE)
if (plotDroplines) segments3d(x=rep(x1,each=2),y=rep(x2,each=2),z=matrix(t(cbind(y,fitted(model))),nc=1),col=segcol,lty=2,alpha=segalpha)
if (!is.null(outfile)) rgl.snapshot(outfile, fmt="png", top=TRUE)
}
Here is what you get as output with your model :
data=data.frame(age=c(530.6071,408.6071,108.0357,106.6071,669.4643,556.4643,665.4643,508.4643,106.0357),
hrs=c(792.10,489.70,463.00,404.15,384.65,358.15,343.85,342.35,335.25),
charges=c(3474.60,1247.06,1697.07,1676.33,1701.13,1630.30,2468.83,3366.44,2876.82))
library(MASS)
fit1=rlm( charges~age+hrs+age*hrs, data)
plotPlaneFancy(fit1, plotx1 = "age", plotx2 = "hrs")
plotPlaneFancy(fit1, plotx1 = "age", plotx2 = "hrs",interval="confidence")
(or interval="prediction" to show 95% prediction intervals)
plotImage(fit1,plotx="age",ploty="hrs",plotContours=T,plotLegend=T)