<p>Data for implementing the example given for CRASSMAT.</p>

data

matrix

sparse

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

Nick Kunz

crassmat

Conditional Random Sampling Sparse Matrices

A function

<p>A sparse matrix containing 15 columns and 3000 observations</p>

A: Sparse Matrix A

Description

Usage

Arguments

Format