Description Usage Arguments Details Value Author(s) Examples

MSE of factor models `w`

and `h`

given sparse matrix *A*

1 |

`A` |
matrix of features-by-samples in dense or sparse format (preferred classes are "matrix" or "Matrix::dgCMatrix", respectively). Prefer sparse storage when more than half of all values are zero. |

`w` |
dense matrix of class |

`d` |
diagonal scaling vector of rank length |

`h` |
dense matrix of class |

`mask_zeros` |
handle zeros as missing values, available only when |

Mean squared error of a matrix factorization of the form *A = wdh* is given by

*\frac{∑_{i,j}{(A - wdh)^2}}{ij}*

where *i* and *j* are the number of rows and columns in *A*.

Thus, this function simply calculates the cross-product of *wh* or *wdh* (if *d* is specified),
subtracts that from *A*, squares the result, and calculates the mean of all values.

If no diagonal scaling vector is present in the model, input `d = rep(1, k)`

where `k`

is the rank of the model.

**Parallelization.** Calculation of mean squared error is performed in parallel across columns in `A`

using the number of threads set by `setRcppMLthreads`

.
By default, all available threads are used, see `getRcppMLthreads`

.

mean squared error of the factorization model

Zach DeBruine

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