Nothing
Vdgraph <-
function(des)
{
#First check if des is a data frame, and if so convert it to a matrix
if(is.data.frame(des)) des<-as.matrix(des)
# next check to see if des is centered, and if not center it
chkm<-apply(des, 2, f)
if (mean(chkm) != 0) des<-scale(des, scale=FALSE)
rc<-dim(des)
ndpts<-rc[1]
if (ndpts>99) {
cat("The number of runs in the design matrix is too large","\n")
} else {
kvar1<-rc[2]
}
check=1+2*kvar1+kvar1*(kvar1-1)/2
if (ndpts<check) {
stop("The number of design points will not allow estimation of the quadratic model","\n")
} else {
kdv1<-ndpts*kvar1
}
if (kvar1>7) {
stop("The maximum number of independent variables allowed is 7","\n")
}
rdes<-des
dim(rdes)<-ndpts*kvar1
cat("number of design points=",ndpts,"\n")
cat("number of factors=",kvar1,"\n")
v<-Vardsgr(ndpts,kvar1,kdv1,rdes)
vdgr<-matrix(v,ncol=4)
colnames(vdgr)<-c("Radius","Maximum","Minimum","Average")
# Fix fortran bug
icnt<-0
for (k in 1:21) {
if (vdgr[k,3]==vdgr[k,4] & vdgr[k,2]==vdgr[k,4]) {
icnt<-icnt+1}
else { if (icnt>=4){
vdgr[k,2]<-vdgr[k,4]}}
}
#modifies plot region
par(mai=c(1.0,.75,1.0,.25))
#Creates the plot region
maxy=max(vdgr[,2])
aminy<-min(vdgr[,3])
miny=0
maxx=sqrt(kvar1)
#cat("maxy=",maxy,"\n")
#cat("aminy=",aminy,"\n")
#cat("miny=",miny,"\n")
#cat("maxx=",maxx,"\n")
plot(c(0,maxx),c(miny,maxy),type="n",ylab="Scaled Variance",xlab="Radius",
main="Variance Dispersion Graph")
#Adds the line for maximum variance
lines(vdgr[,1],vdgr[,2],lty=2,col="red")
#Adds the line for minimum variance
lines(vdgr[,1],vdgr[,3],lty=4,col="blue")
#Adds the line for average variance
lines(vdgr[,1],vdgr[,4],lty=1,col="black")
#Adds the legend
legend("topleft",inset=.02,legend=,c("Max","Min","Avg"),lty=(c(2,4,1)),col=(c("red","blue","black")))
#gets default graphic parameters
defpar<-par()
return(vdgr)
}
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