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文件名:  Computing and Visualizing PCA in R.pdf
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Following my introduction to PCA, I will demonstrate how to apply and visualize PCA in R. There are many packages and functions that can apply PCA in R. In this post I will use the function prcomp from the stats package. I will also show how to visualize PCA in R using Base R graphics. However, my favorite visualization function for PCA is ggbiplot, which is implemented byVince Q. Vu and available on github. Please, let me know if you have better ways to visualize PCA in R.
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  1. # Load data
  2. data(iris)
  3. head(iris, 3)
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  1. # log transform
  2. log.ir <- log(iris[, 1:4])
  3. ir.species <- iris[, 5]

  5. # apply PCA - scale. = TRUE is highly
  6. # advisable, but default is FALSE.
  7. ir.pca <- prcomp(log.ir,
  8. center = TRUE,
  9. scale. = TRUE)
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  1. # print method
  2. print(ir.pca)
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  1. # summary method
  2. summary(ir.pca)
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  1. # Predict PCs
  2. predict(ir.pca,
  3. newdata=tail(log.ir, 2))
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  1. library(devtools)
  2. install_github("ggbiplot", "vqv")

  4. library(ggbiplot)
  5. g <- ggbiplot(ir.pca, obs.scale = 1, var.scale = 1,
  6. groups = ir.species, ellipse = TRUE,
  7. circle = TRUE)
  8. g <- g + scale_color_discrete(name = '')
  9. g <- g + theme(legend.direction = 'horizontal',
  10. legend.position = 'top')
  11. print(g)
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  1. require(caret)
  2. trans = preProcess(iris[,1:4],
  3. method=c("BoxCox", "center",
  4. "scale", "pca"))
  5. PC = predict(trans, iris[,1:4])
  6. # Retained PCs
  7. head(PC, 3)
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