R/olstest.R

In musto101/wilcox_R: What the package does (short line)

olstest <-
function(x,y,nboot=500,SEED=TRUE,RAD=TRUE,xout=FALSE,outfun=outpro,...){
#
# Test the hypothesis that all OLS slopes are zero.
# Heteroscedasticity is allowed.
#
# RAD=T: use Rademacher function to generate wild bootstrap values.
# RAD=F, use standardized uniform distribution.
#
if(SEED)set.seed(2)
m<-elimna(cbind(x,y))
x<-as.matrix(x)
p<-ncol(x)
pp<-p+1
x<-m[,1:p]
y<-m[,pp]
if(xout){
m<-cbind(x,y)
flag<-outfun(x,plotit=FALSE,...)$keep
m<-m[flag,]
x<-m[,1:p]
y<-m[,pp]
}
x<-as.matrix(x)
temp<-lsfit(x,y)
yhat<-mean(y)
res<-y-yhat
test<-sum(temp$coef[2:pp]^2)
if(RAD)data<-matrix(ifelse(rbinom(length(y)*nboot,1,0.5)==1,-1,1),nrow=nboot)
if(!RAD){
data<-matrix(runif(length(y)*nboot),nrow=nboot)#
data<-(data-.5)*sqrt(12) # standardize the random numbers.
}
rvalb<-apply(data,1,olstests1,yhat,res,x)
p.val<-sum(rvalb>=test)/nboot
list(p.value=p.val)
}