R: make predictions from an svd object

complete {softImpute} | R Documentation

## make predictions from an svd object

### Description

These functions produce predictions from the low-rank solution of `softImpute`

### Usage

    complete(x, object, unscale = TRUE)
    impute(object, i, j, unscale = TRUE)

### Arguments

`x` | a matrix with NAs or a matrix of class `"Incomplete"`.
`object` | an svd object with components u, d and v
`i` | vector of row indices for the locations to be predicted
`j` | vector of column indices for the locations to be predicted
`unscale` | if `object` has `biScale` attributes, and `unscale=TRUE`, the imputations reversed the centering and scaling on the predictions.

### Details

`impute` returns a vector of predictions, using the reconstructed low-rank matrix representation represented by `object`. It is used by complete, which returns a complete matrix with all the missing values imputed.

### Value

Either a vector of predictions or a complete matrix. WARNING: if `x` has large dimensions, the matrix returned by `complete` might be too large.

### Author(s)

Trevor Hastie

### See Also

`softImpute`, `biScale` and `Incomplete`

### 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)
    complete(xna,fit1)

[Package softImpute version 1.4 Index]