R/olsMUL.R

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

olsMUL <-
function(x,y,xout=FALSE,outfun=out){
#
# y is assumed to be a matrix. For each
#  outcome variable (for each column of  y), this function returns
# confidence intervals for each slope parameter and intercept.
#
# Performs OLS regression calling built-in R or S+ funtions.
#
# xout=T will eliminate any leverage points (outliers among x values)
#
m<-elimna(cbind(x,y))
x<-as.matrix(x)
p<-ncol(x)
pp<-ncol(m)
p1=p+1
x<-m[,1:p]
y<-m[,p:pp]
if(xout){
m<-cbind(x,y)
flag<-outfun(x,plotit=FALSE)$keep
m<-m[flag,]
x<-m[,1:p]
y<-m[,pp]
}
x<-as.matrix(x)
y<-as.matrix(y)
py=ncol(y)
for(j in 1:py){
print(paste("Response", j))
temp<-summary(lm(y[,j]~x))
coef<-temp[4]$coefficients
Ftest<-temp[10]$fstatistic
Ftest.p.value<-1-pf(Ftest[1],Ftest[2],Ftest[3])
Rval=Rsq(x,y[,j])
print(list(coef=coef,Ftest.p.value=Ftest.p.value,R.squared=Rval))
}
}