deBias: Recompute the '$d' component of a '"softImpute"' object...
In softImpute: Matrix Completion via Iterative Soft-Thresholded SVD

Description Usage Arguments Details Value Author(s) Examples

softImpute uses shrinkage when completing a matrix with missing values. This function debiases the singular values using ordinary least squares.

1	deBias(x, svdObject)

`x`	matrix with missing entries, or a matrix of class `"Incomplete"`
`svdObject`	an SVD object, the output of `softImpute`

Treating the "d" values as parameters, this function recomputes them by linear regression.

An svd object is returned, with components "u", "d", and "v".

Trevor Hastie
Maintainer: Trevor Hastie hastie@stanford.edu

set.seed(101)
n=200
p=100
J=50
np=n*p
missfrac=0.3
x=matrix(rnorm(n*J),n,J)%*%matrix(rnorm(J*p),J,p)+matrix(rnorm(np),n,p)/5
ix=seq(np)
imiss=sample(ix,np*missfrac,replace=FALSE)
xna=x
xna[imiss]=NA
fit1=softImpute(xna,rank=50,lambda=30)
fit1d=deBias(xna,fit1)