unsupML/3_pca at master · cpenaloza/unsupML · GitHub

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# Perform scaled PCA: pr.out
pr.out <- prcomp(pokemon, scale = T)

# Inspect model output
summary(pr.out)
#
#
biplot(pr.out)
#
#
# Variability of each principal component: pr.var
pr.var <- pr.out$sdev ^ 2

# Variance explained by each principal component: pve
pve <- pr.var / sum(pr.var)
#
#
# Plot variance explained for each principal component
plot(pve, xlab = "Principal Component",
     ylab = "Proportion of Variance Explained",
     ylim = c(0, 1), type = "b")

# Plot cumulative proportion of variance explained
plot(cumsum(pve), xlab = "Principal Component",
     ylab = "Cumulative Proportion of Variance Explained",
     ylim = c(0, 1), type = "b")
#
#
# Mean of each variable
colMeans(pokemon)

# Standard deviation of each variable
apply(pokemon, 2, sd)

# PCA model with scaling: pr.with.scaling
pr.with.scaling <- prcomp(pokemon, scale = T)

# PCA model without scaling: pr.without.scaling
pr.without.scaling <- prcomp(pokemon, scale= F)

# Create biplots of both for comparison
biplot(pr.with.scaling)
biplot(pr.without.scaling)