Principal Component Analysis - PCA

This is a wrapper for the pls::PCR function for computing PCA.

Usage

pca(X, scale = FALSE, ncomp = 1, ...)

Arguments

X

matrix of input data.

scale

logical indicating if variables should be standardised (default=FALSE).

ncomp

integer number of principal components to return.

...

additional arguments to pls:pcr.

Value

multiblock object with scores, loadings, mean X values and explained variances. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

Details

PCA is a method for decomposing a matrix into subspace components with sample scores and variable loadings. It can be formulated in various ways, but the standard formulation uses singular value decomposition to create scores and loadings. PCA is guaranteed to be the optimal way of extracting orthogonal subspaces from a matrix with regard to the amount of explained variance per component.

References

Pearson, K. (1901) On lines and planes of closest fit to points in space. Philosophical Magazine, 2, 559–572.

See also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex. Common functions for computation and extraction of results and plotting are found in multiblock_results and multiblock_plots, respectively.

Examples

data(potato)
X <- potato$Chemical

pca.pot  <- pca(X, ncomp = 2)