AlternateSVD

a numeric, number of latent factors to estimate

an n by s matrix,  each column is a vector of known covariates for n samples, s covariates are considered, default to <code>NULL</code>

pred

a numeric, maximum number of iteration allowed, default to 10

max.iter

a numeric, tolerance level for the algorithm to converge, default to 1e-04

The algorithm alternates between 1) computing latent loadings u and latent variable v and 2) estimating noise standard deviation for each of the N genes.

~kwd1

~kwd2

These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.

AlternateSVD: Alternating singular value decomposition

Description

Usage

Arguments

Value