
moncomb<-function(z,lambda,alpha1,n1,n2,b)
{
#this function yields the table2 for CV, chesher, 2sqr 2spq estimates.
#scale-location model is used in moncom(.).
coef<-matrix(0,35,4)
tau<-c(0.1,0.3,0.5,0.7,0.9)
coefa<- moncom(z,.1,lambda,alpha1,n1,n2,b)
coefb<- moncom(z,.3,lambda,alpha1,n1,n2,b)
coefc<- moncom(z,.5,lambda,alpha1,n1,n2,b)
coefd<- moncom(z,.7,lambda,alpha1,n1,n2,b)
coefe<- moncom(z,.9,lambda,alpha1,n1,n2,b)
coef<-rbind(coefa, coefb,coefc,coefd,coefe)

methods1<-rbind("Theoretical Value","CV", "Chesher","2SQQ","2SMQ", "2SLQ", "QR")
methods<-rbind(methods1, methods1, methods1, methods1, methods1)

dimnames(coef)<-list (methods, c("Coeffcient", "Bias", "Std. Error", "MSE"))

#dump("coef", "bbb")
# put into latex.table.
#return(coef)
latex.table(coef,file="Table2",  rowlabel="", dec=3, rgroup =paste("t = ", tau), n.rgroup=c(7,7,7,7,7),label="", caption="Simulation Results")

}
