R语言主成分分析报告(2)

Proportion of Variance 0.8393 0.08932 0.05296 0.01844

Cumulative Proportion 0.8393 0.92860 0.98156 1.00000

plot(lh.pca, type ="l")

predict(lh.pca,

newdata =tail(na.omit(log.lh,2)))

PC1 PC2 PC3 PC4

450 -0.4271374 0.32820602 -0.36821424 0.003225707

458 -2.2396492 -0.45685837 -0.15879184 0.195294075

466 -0.3954699 0.17690207 0.11668851 -0.061863425

475 -0.9588797 -0.02340739 0.07678242 -0.186083713

484 -0.9275021 -0.32335662 0.26334138 -0.183940408

493 -1.5313185 0.46210774 -0.41899155 -0.045873086

biplot(lh.pca)

画出一个类似于下图的图，横坐标是response variable，纵坐标是Principal Com ponent。

pp=prcomp(na.omit(log.lh))

x=lh.dose[as.numeric(names(predict(lh.pca,

newdata =na.omit(log.lh,2) )[,1]))]

y=predict(lh.pca,

newdata =na.omit(log.lh,2) )[,1]

data=data.frame(x,y)

aggregate(y ~x, data = data, mean)

x y

1 1 Gy 0.46637350

2 2 Gy 0.63214810

3 4 Gy -0.05234262

4 5.

5 Gy -0.92889378

5 Sham 0.35652766

aggregate(y ~x, data = data, sd)

x y

1 1 Gy 0.2975531

2 2 Gy 0.3364273

3 4 Gy 0.5905769