Description Usage Arguments Value Author(s) References See Also Examples

Computes joint and individual variation explained (JIVE) scores for new data via iterative least squares, with fixed loadings given by a previous JIVE analysis.

1 | ```
jive.predict(data.new, jive.output)
``` |

`data.new` |
A list of two or more linked data matrices on which to estimate JIVE scores. These matrices must have the same column dimension N, which is assumed to be common. |

`jive.output` |
An object of class "jive", with row dimensions matching those for data.new. |

`joint.scores` |
r X N matrix of joint scores |

`individual.scores` |
List where entry [[i]] gives the r_i X N matrix of individual scores for source i |

`errors` |
Vector of the proportion of total variance explained over iterations during estimation |

`joint.load` |
d X r matrix of joint loadings |

`indiv.load` |
List where entry [[i]] gives the d_i X N matrix of individual laodings for source i |

Adam Kaplan

Kaplan, A. and Lock, E.F. (2017). Prediction with Dimension Reduction of Multiple Molecular Data Sources for Patient Survival. arXiv:1704.02069, 2017.

1 2 3 4 5 6 7 8 9 10 | ```
##Load data that were simulated as in Section 2.4 of Lock et al., 2013,
##with rank 1 joint structure, and rank 1 individual structure for each dataset
data(SimData)
##load JIVE results (using default settings) for simulated data
data(SimResults)
#predict JIVE scores for data (treated as "new data" here)
pred.results <- jive.predict(SimData,Results)
##estimated joint structure is pred.results$joint.load %*% pred.results$joint.scores
##estimated individual structure for source i is
##pred.results$indiv.load[[i]] %*% pred.results$indiv.scores[[i]]
``` |

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