Principle Component Analysis

Bhawna G. Panwar

2 minute read

Principle Component Analysis was performed using the Iris dataset.

# Load data
data(iris)
head(iris, 3)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
# log transform 
log.ir <- log(iris[, 1:4])
ir.species <- iris[, 5]

# apply PCA - scale. = TRUE is highly 
# advisable, but default is FALSE. 
ir.pca <- prcomp(log.ir,
                 center = TRUE,
                 scale. = TRUE)
# print method
print(ir.pca)
## Standard deviations:
## [1] 1.7124583 0.9523797 0.3647029 0.1656840
## 
## Rotation:
##                     PC1         PC2        PC3         PC4
## Sepal.Length  0.5038236 -0.45499872  0.7088547  0.19147575
## Sepal.Width  -0.3023682 -0.88914419 -0.3311628 -0.09125405
## Petal.Length  0.5767881 -0.03378802 -0.2192793 -0.78618732
## Petal.Width   0.5674952 -0.03545628 -0.5829003  0.58044745
# plot method
plot(ir.pca, type = "l")

# summary method
summary(ir.pca)
## Importance of components:
##                           PC1    PC2     PC3     PC4
## Standard deviation     1.7125 0.9524 0.36470 0.16568
## Proportion of Variance 0.7331 0.2268 0.03325 0.00686
## Cumulative Proportion  0.7331 0.9599 0.99314 1.00000
# Predict PCs
predict(ir.pca, 
        newdata=tail(log.ir, 2))
##           PC1         PC2        PC3         PC4
## 149 1.0809930 -1.01155751 -0.7082289 -0.06811063
## 150 0.9712116 -0.06158655 -0.5008674 -0.12411524
#if (!require(devtools)) install.packages("devtools")
if (!require(ggbiplot)) install_github("ggbiplot", "vqv")
## Loading required package: ggbiplot
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.3.3
## Loading required package: plyr
## Loading required package: scales
## Loading required package: grid
library(devtools)
## Warning: package 'devtools' was built under R version 3.3.3
library(ggbiplot)
g <- ggbiplot(ir.pca, obs.scale = 1, var.scale = 1, 
              groups = ir.species, ellipse = TRUE, 
              circle = TRUE)
g <- g + scale_color_discrete(name = '')
g <- g + theme(legend.direction = 'horizontal', 
               legend.position = 'top')
print(g)

Reference: https://www.r-bloggers.com/computing-and-visualizing-pca-in-r/

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