I'm using R for PCA and? factor analysis. I want to create biplots of
varimax rotated factors that color-code points by their classification.
My research is on streams that are urban and rural. So, I want to color
code them by this classification. If you just do a biplot from prcomp or
princomp, you cannot add this color. So, I have used some code developed
by a graduate student in our statistics department here at NC State
University. However, when you compare the two biplots, the observed
points are not in the same location. The variable vectors match up, but
not the points. I'm not sure why. The code is below. Please help.
label=data[,"Urban.Rural"]
indexU<-which(label=="U")
indexR<-which(label=="R")
collab<-rep(0,length(data[,1]))
collab[indexU]<-"Blue"
collab[indexR]<-"Green"
library(psych)
fit <- principal(mydata, nfactors = num.fac, rotate="varimax", scores =
TRUE)
z1 <- sum(fit2$loadings[,1]^2) ### need to scale scores and loadings by
these factors
z2 <- sum(fit2$loadings[,2]^2)
biplot(fit$scores[,c(1,2)]/c(z1, z2), loadings(fit)[,c(1,2)]*c(z1, z2),
xlabs=rep("", length(collab)), col = c("black", "orange"))
legend(x="bottomright", legend=c("Urban","Rural"),
text.col=c("Blue","Green"), bg="Grey90")
### scale the plot parameters
rrr<-apply(fit$scores[,1:2],2, range)
(abs(rrr)+.1)*sign(rrr)
par(usr=as.vector(rrr))
### now include the colored points
points(fit$scores[,c(1,2)], col=collab, pch=20)
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