First, if you expect help, you should provide a reproducible example, which includes a sample of your data. This is why your question was downvoted (not by me though). The variance due to the ith principal component is given by the ith eigenvalue of the correlation matrix. Since the PCs...

You could try library(psych) myData1 <- myData[-1] rownames(myData1) <- myData[,1] Corrt <- corr.test(t(myData1)) Corrt$r[Corrt$r >= 0.5] If you need to preserve the structure, then we change the value < 0.5 to NA is.na(Corrt$r) <- Corrt$r < 0.5 Corrt$r # gene1 gene2 gene3 gene4 #gene1 1.0000000 0.8801186 NA NA #gene2 0.8801186...

You have several problems. 1) As previously commented upon, you are treating mydata as a function, but you need to treat it as a data.frame. Thus the call should be poly.example <- cor.ci(mydata,n.iter = 10,poly = TRUE) If you are trying to just get the first 100 cases and the...

This was partly answered, but since it is my package, I will give a somewhat more complete answer. The summary table of the PCA or FA factor loadings tables is calculated in the print function. It it is returned (invisibly by print). However, it is available as the Vaccounted object....

r,pca,rotational-matrices,psych

I'd give manipulate a try - something in the veins of: library(psych) library(manipulate) l <- l_orig <- unclass(loadings(principal(Harman.5, 2, scores=TRUE))) manipulate( { if(rotateRight) l <<- factor.rotate(l, angle, 1, 2) if (rotateLeft) l <<- factor.rotate(l, -1*angle, 1, 2) plot(l, xlim = c(-1, 1), ylim = c(-1, 1), xlab = 1, ylab...