PCA: principal component analysis

Perform a principal component analysis (Pearson 1901; Hotelling 1933) on the data.

Usage

analyze.pca(dataSet, center = TRUE, scale = TRUE)

Arguments

dataSet

The 2d data set of data.

center

A boolean (default = TRUE) indicating whether the variables should be shifted to be zero centered.

scale

A boolean (default = TRUE) indicating whether the variables should be scaled to have unit variance before the analysis takes place.

Value

A list containing the following components:

sdev

The standard deviations of the principal components.

rotation

The matrix of variable loadings.

center

The centering used.

scale

The scaling used.

x

The principal component scores.

References

Hotelling H (1933). “Analysis of a Complex of Statistical Variables into Principal Components.” Journal of Educational Psychology, 24(6), 417–441. doi:10.1037/h0071325 .

Pearson K (1901). “On Lines and Planes of Closest Fit to Systems of Points in Space.” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572. doi:10.1080/14786440109462720 .