elbowPlot: Elbow Plot for PCA (Plot)
In mariytu/RegressionLibs: What the package does (short line)

Description Usage Arguments See Also Examples

Generate a plot of 10 first variances of Principal Components. This is useful to determinate which are the most important components.

1	elbowPlot(data.pca)

data.pca

a list with class "prcomp" containing all principal components calculated.

CalculateVariance, plotPC

#Example 1
iris.x <- iris[,1:4] # These are the independent variables
# We know that there are no missing values in the data set

# performing prcomp
ir.pca <- prcomp(iris.x, center = TRUE, scale. = TRUE)

# Generating elbow plot to detect the most important principal components
elbowPlot(ir.pca)


#Example 2
# Getting a clean data set (without missing values)
cars <- read.csv("https://dl.dropboxusercontent.com/u/12599702/autosclean.csv", sep = ";", dec = ",")
cars.x <- cars[,1:16] # These are the independent variables

# Performing prcomp
cars.pca <- prcomp(cars.x, center = TRUE, scale. = TRUE)

# Generating elbow plot to detect the most important principal components
elbowPlot(cars.pca)