For following code performing principal component analysis:
> prcomp(iris[1:4], scale=TRUE)
Standard deviations:
[1] 1.7083611 0.9560494 0.3830886 0.1439265
Rotation:
PC1 PC2 PC3 PC4
Sepal.Length 0.5210659 -0.37741762 0.7195664 0.2612863
Sepal.Width -0.2693474 -0.92329566 -0.2443818 -0.1235096
Petal.Length 0.5804131 -0.02449161 -0.1421264 -0.8014492
Petal.Width 0.5648565 -0.06694199 -0.6342727 0.5235971
Will the standard biplot()
in R be plotted for Rotations or for Loadings (rotation values multiplied by standard deviation for that component - also seen in above output)? (see comments by @amoeba on Conclusions from output of a principal component analysis)
For factor analysis, I presume it will be Loadings:
> factanal(mtcars,2)
Call:
factanal(x = mtcars, factors = 2)
Uniquenesses:
mpg cyl disp hp drat wt qsec vs am gear carb
0.167 0.070 0.096 0.143 0.298 0.168 0.150 0.256 0.171 0.246 0.386
Loadings:
Factor1 Factor2
mpg 0.686 -0.602
cyl -0.629 0.731
disp -0.730 0.609
hp -0.337 0.862
drat 0.807 -0.225
wt -0.810 0.420
qsec -0.162 -0.908
vs 0.291 -0.812
am 0.907
gear 0.860 0.125
carb 0.783
Factor1 Factor2
SS loadings 4.494 4.357
Proportion Var 0.409 0.396
Cumulative Var 0.409 0.805
Test of the hypothesis that 2 factors are sufficient.
The chi square statistic is 68.57 on 34 degrees of freedom.
The p-value is 0.000405