Variance explained
The second common plot type for understanding PCA models is a scree plot. A scree plot shows the variance explained as the number of principal components increases. Sometimes the cumulative variance explained is plotted as well.
In this and the next exercise, you will prepare data from the pr.out
model you created at the beginning of the chapter for use in a scree plot. Preparing the data for plotting is required because there is not a built-in function in R to create this type of plot.
This exercise is part of the course
Unsupervised Learning in R
Exercise instructions
pr.out
and the pokemon
data are still available in your workspace.
- Assign to the variable
pr.var
the square of the standard deviations of the principal components (i.e., the variance). The standard deviation of the principal components is available in thesdev
component of the PCA model object. - Assign to the variable
pve
the proportion of the variance explained, calculated by dividingpr.var
by the total variance explained by all principal components.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Variability of each principal component: pr.var
pr.var <- ___
# Variance explained by each principal component: pve
pve <- ___ / ___