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...est scores > myValues <- c(70:95) > Score <- ( sample( myValues, size=1000, replace=TRUE) ) > head(Score) [1] 77 71 81 88 83 93 > > # Get frequencies & proportions > myTable <- data.frame( table(Score) ) > myTable$Prop <- prop.table( myTable$Freq ) > myTable$CumProp <- cumsum( myTable$Prop ) > > # Print result > myTable Score Freq Prop CumProp 1 70 44 0.044 0.044 2 71 42 0.042 0.086 3 72 40 0.040 0.126 4 73 40 0.040 0.166 5 74 43 0.043 0.209 6 75 45 0.045 0.254 7 76 46 0.046 0.300 8...

...I would like to compare the % variance explained by each PC before and after rotation. < code follows > data(USArrests) pca = prcomp(USArrests, scale = TRUE) # proportion variance explained by each PC prop = pca$sdev^2/sum(pca$sdev^2) # cumulative proportion variance explained by each PC cumProp = cumsum(prop) # following print statements also can be obtained # from print(summary(pca)) #print(prop) #print(cumProp) print(summary(pca)) # Rotate the PCA loadings through PCs 1 and 2 using VARIMAX rotation rot = varimax(pca$rotation[,1:2], normalize = TRUE, eps = 1e-5) < end code >...