sharpel1pca: SharpEl1-PCA

In pcaL1: L1-Norm PCA Methods

SharpEl1-PCA

Description

Performs a principal component analysis using the algorithm SharpEl1-PCA described by Brooks and Dula (2017, submitted)

Usage

   sharpel1pca(X, projDim=1, center=TRUE, projections="none")

Arguments

X	data, must be in matrix or table form.
projDim	number of dimensions to project data into, must be an integer, default is 1.
center	whether to center the data using the median, default is TRUE.
projections	whether to calculate reconstructions and scores using the L1 norm ("l1") the L2 norm ("l2") or not at all ("none", default).

Details

The calculation is performed according to the algorithm described by Brooks and Dula (2017, submitted). The algorithm finds successive, orthogonal fitted lines in the data.

Value

'sharpel1pca' returns a list with class "sharpel1pca" containing the following components:

loadings	the matrix of variable loadings. The matrix has dimension ncol(X) x projDim. The columns define the projected subspace.
scores	the matrix of projected points. The matrix has dimension nrow(X) x projDim.
dispExp	the proportion of L1 dispersion explained by the loadings vectors. Calculated as the L1 dispersion of the score on each component divided by the L1 dispersion in the original data.
projPoints	the matrix of projected points in terms of the original coordinates. The matrix has dimension nrow(X) x ncol(X).
minobjectives	the L1 distance of points to their projections in the fitted subspace.

References

Brooks J.P. and Dula J.H. (2017) Estimating L1-Norm Best-Fit Lines, submitted.

Examples

##for a 100x10 data matrix X, 
## lying (mostly) in the subspace defined by the first 2 unit vectors, 
## projects data into 1 dimension.
X <- matrix(c(runif(100*2, -10, 10), rep(0,100*8)),nrow=100) +
                matrix(c(rep(0,100*2),rnorm(100*8,0,0.1)),ncol=10)
mysharpel1pca <- sharpel1pca(X)

##projects data into 2 dimensions.
mysharpel1pca <- sharpel1pca(X, projDim=2, center=FALSE, projections="l1")

## plot first two scores
plot(mysharpel1pca$scores)

pcaL1 documentation built on Jan. 22, 2023, 1:55 a.m.