pqcomp: Principal Quantile Components

In obleeker/quant.pca: Principal Quantile Components Analysis

Description

Calculate principal quantile components (PQC) via smooth approximation of the asymmetric L1-Norm.

Usage

pqcomp(data, projDim, tau, lambda, muEst = TRUE,
  epsilon = 1/log10(nrow(data)), iterTol = 200, convTol = NA,
  preOut = NA, progBar = TRUE, doPar = FALSE, doSeq = FALSE,
  randomSeed = NA)

Arguments

data	data matrix with rows as data entries and columns as variables
projDim	no. of desired principal components
tau	regulization parameter
lambda	regulization parameter
muEst	calculate a constant mu
epsilon	approxiamtion parameter
iterTol	no. of max iterataions
convTol	set algorithm to stop if weigths did not change for convTol no. of consecutive iterations (deactivated for tau=0.5)
preOut	continues based on previous output (if doSeq is TRUE, the components of a previos PCA method are sufficient)
progBar	optional progressbar
doPar	use parallel backend (*nix systems recommended),
doSeq	run via sequential optimization
randomSeed	select a random seed as in set.seed for fixed initialization

Value

list object containing components and loadings

Examples

# generating data
n = 100
X = data.frame(cbind(rnorm(n),rnorm(n),rnorm(n)))
# running the main method
pqcomp(data = X, projDim = 2, tau =  0.9, lambda = 0.1, muEst = T)

obleeker/quant.pca documentation built on July 7, 2019, 12:41 a.m.