IPODFUN

an N by N projection matrix X(X'X)^{-1}X'

sigma

a k by 1 initial value for coeffient beta

betaInit

a string, if "hard", conduct hard thresholding, if "soft", conduct soft thresholding, default to "hard"

method

a numeric,  tolerance of convergence,  default to 1e-04

Y = X beta + gamma + sigma epsilon
 estimate k by 1 coefficients vector beta and N by 1 outlier indicator vector gamma from (Y,X).

~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.

IPODFUN: compute the iterative penalized outlier detection given the noise standard deviation sigma

Description

Usage

Arguments

Value

Details

References