deBias {softImpute} | R Documentation |
Recompute the $d
component of a "softImpute"
object
through regression.
Description
softImpute
uses shrinkage when completing a matrix with
missing values. This function debiases the singular values using
ordinary least squares.
Usage
deBias(x, svdObject)
Arguments
x |
matrix with missing entries, or a matrix of class |
svdObject |
an SVD object, the output of |
Details
Treating the "d"
values as parameters, this function recomputes
them by linear regression.
Value
An svd object is returned, with components "u", "d", and "v".
Author(s)
Trevor Hastie
Maintainer: Trevor Hastie hastie@stanford.edu
Examples
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)
[Package softImpute version 1.4-1 Index]