bwpca: Determine principal components of a matrix using...

In hms-dbmi/scde: Single Cell Differential Expression

Determine principal components of a matrix using per-observation/per-variable weights

Description

Implements a weighted PCA

Usage

bwpca(mat, matw = NULL, npcs = 2, nstarts = 1, smooth = 0,
  em.tol = 1e-06, em.maxiter = 25, seed = 1, center = TRUE,
  n.shuffles = 0)

Arguments

mat	matrix of variables (columns) and observations (rows)
matw	corresponding weights
npcs	number of principal components to extract
nstarts	number of random starts to use
smooth	smoothing span
em.tol	desired EM algorithm tolerance
em.maxiter	maximum number of EM iterations
seed	random seed
center	whether mat should be centered (weighted centering)
n.shuffles	optional number of per-observation randomizations that should be performed in addition to the main calculations to determine the lambda1 (PC1 eigenvalue) magnitude under such randomizations (returned in $randvar)

Value

a list containing eigenvector matrix ($rotation), projections ($scores), variance (weighted) explained by each component ($var), total (weighted) variance of the dataset ($totalvar)

Examples

set.seed(0)
mat <- matrix( c(rnorm(5*10,mean=0,sd=1), rnorm(5*10,mean=5,sd=1)), 10, 10)  # random matrix
base.pca <- bwpca(mat)  # non-weighted pca, equal weights set automatically
matw <- matrix( c(rnorm(5*10,mean=0,sd=1), rnorm(5*10,mean=5,sd=1)), 10, 10)  # random weight matrix
matw <- abs(matw)/max(matw)
base.pca.weighted <- bwpca(mat, matw)  # weighted pca

hms-dbmi/scde documentation built on April 19, 2023, 10:21 p.m.