Monna,
The way i do it is to re-create the biplot for the PCA ..... I am attaching my
code (i am sure this can be done even easier ..... but this works as well)
where i am using pca() function from labdsv and my data is called veg1.
library (labdsv)
pca.1<-pca(veg1,cor=TRUE)
# The scores are what are typically plotted in a PCA "ordination", but we will
scale them between -1 and 1 so you can plot together with loadings
scores <- pca.1$scores
nrows <- nrow(scores)
ncols <- ncol(scores)
#re-scaling
for (i in 1:nrows) {
for (j in 1:ncols){
if (pca.1$scores[i,j] < 0) scores[i,j] <-
(-1)*(pca.1$scores[i,j])/min(pca.1$scores) else scores[i,j] <-
pca.1$scores[i,j]/max(pca.1$scores)
}}
pc1 <- scores[,1]
pc2 <- scores[,2]
plot(pc1, pc2, pch=16, cex=2, col ="paleturquoise", xlim = c(-1, 1), xlab =
"PC1", ylab = "PC2",main = "Principal Component Analysis, Region A")
## here you can change pch as a function of your values so the points will have
different sizes - if you wish - but the sizes need to be a vector of numbers ##
and not characters and .... your numbers are too big so maybe you should scale
them somehow .... let's say between 1 and 3 or whatever .....
abline(v=0, lty=2, col="green")
abline(h=0, lty=2, col="green")
# add the loadings
loadings <- pca.1$loadings
load1 <- loadings[,1]
load2 <- loadings[,2]
c0 <- rep(0,length(load1))
d0 <- rep(0,length(load1))
c1 <- load1
d1 <- load2
segments(c0,d0,c1,d1, col = "grey")
load <- cbind(load1, load2)
points(load, pch=17, cex = 2, col = "darkblue")
identify(load[,1],load[,2], dimnames(load)[[1]], col = "deeppink3", font = 2)
I hope this helps,
Monica
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