> x2=rnorm(100)
> x3=rnorm(100)
> data=data.frame(x1,x2,x3)
> data.pr=princomp(data,cor=TRUE)
> data.pr
Call:
princomp(x = data, cor = TRUE)
Standard deviations:
1.0919785 1.0120505 0.8850631
> summary(data.pr,loadings=TRUE)
Importance of components:
Standard deviation 1.0919785 1.0120505 0.8850631#主成分的标准差
Proportion of Variance 0.3974724 0.3414154 0.2611122#方差的贡献率
Cumulative Proportion 0.3974724 0.7388878 1.0000000#方差的累积贡献率
Loadings:
x1 0.718 -0.152 -0.679
x2 -0.686 -0.317 -0.655
x3 -0.116 0.936 -0.332
三个主成分的是:
Comp.1=0.718×1-0.686×2-0.116×3
Comp.2=-0.152×1-0.317×20.936×3
Comp.3=-0.679×1-0.655×2-0.332×3
若是前面几个的贡献率很高比如0.99,则可以用这几个主成分降维;
> biplot(data.pr)
R主成分分析