wilcox_R: What the package does (short line)

adtestv2 <-
function(x,y,est=tmean,nboot=500,alpha=.05,fr=NA,xout=TRUE,outfun=outpro,com.pval=FALSE,SEED=TRUE,qval=.5,...){
#
# For two predictors, test the hypothesis that the regression model is additive. That is, there is no interaction.
#  In essence, for the model Y=g_1(X_1)+g_2(X_2)+g_3(X_1X_2), test H_0: g_3(X_1X_2)=0
#
# The method fits an additive model using running interval smoother and the backfitting
# algorithm and then tests the hypothesis that the median of X_1X_2, given the residuals,
# is a straight horizontal line.
#
if(ncol(x)!=2)stop("There should be two predictors")
temp<-cbind(x,y)
p<-ncol(x)
p1<-p+1
temp<-elimna(temp)
x<-temp[,1:p]
x<-as.matrix(x)
y<-temp[,p1]
if(xout){
keepit<-rep(T,nrow(x))
flag<-outfun(x,plotit=FALSE,...)$out.id
keepit[flag]<-FALSE
x<-x[keepit,]
y<-y[keepit]
}
if(alpha<.05 && nboot<=100)warning("You used alpha<.05 and nboot<=100")
if(is.na(fr)){
fr<-.8
if(ncol(x)==2){
nval<-c(20,30,50,80,100,200,300,400)
fval<-c(0.40,0.36,0.3,0.25,0.23,.12,.08,.015)
if(length(y)<=400)fr<-approx(nval,fval,length(y))$y
if(length(y)>400)fr<-.01
}
}
if(SEED)set.seed(2)
x<-as.matrix(x)
mflag<-matrix(NA,nrow=length(y),ncol=length(y))
for (j in 1:length(y)){
for (k in 1:length(y)){
mflag[j,k]<-(sum(x[j,]<=x[k,])==ncol(x))
}
}
yhat<-adrun(x,y,est=est,plotit=FALSE,fr=fr,pyhat=T)
regres<-y-yhat
test2=medind(regres,x[,1]*x[,2],qval=qval,nboot=nboot,com.pval=com.pval,SEED=SEED,alpha=alpha,
pr=TRUE,xout=xout,outfun=outfun,...)
test2
}