wilcox_R: What the package does (short line)

ols.plot.inter <-
function(x,y, pyhat = FALSE, eout = FALSE, xout = FALSE, outfun = out,
    plotit = TRUE, expand = 0.5, scale = FALSE, xlab = "X",
    ylab = "Y", zlab = "", theta = 50, phi = 25, family = "gaussian",
    duplicate = "error",ticktype="simple",...){
#
# Plot regression surface based on the classic interaction model:
#  usual product term
#
#   x is assumed to be a matrix with two columns (two predictors)
library(akima)
x<-as.matrix(x)
xx<-cbind(x,y)
xx<-elimna(xx)
x<-xx[,1:ncol(x)]
x<-as.matrix(x)
y<-xx[,ncol(x)+1]
if(ncol(x)!=2)stop("x should have two columns")
if(xout){
m<-cbind(x,y)
flag<-outfun(x,plotit=FALSE,...)$keep
m<-m[flag,]
x<-m[,1:2]
y<-m[,3]
}
xx=cbind(x,x[,1]*x[,2])
temp=lsfit(xx,y)
fitr=y-temp$residuals
iout<-c(1:length(fitr))
nm1<-length(fitr)-1
for(i in 1:nm1){
ip1<-i+1
for(k in ip1:length(fitr))if(sum(x[i,]==x[k,])==2)iout[k]<-0
}
fitr<-fitr[iout>=1] # Eliminate duplicate points in the x-y plane
#                 This is necessary when doing three dimensional plots
#                 with the R function interp
mkeep<-x[iout>=1,]
fit<-interp(mkeep[,1],mkeep[,2],fitr,duplicate=duplicate)
persp(fit,theta=theta,phi=phi,expand=expand,
scale=scale,xlab=xlab,ylab=ylab,zlab=zlab,ticktype=ticktype)
}