jive.predict: Predict JIVE scores for new data

In r.jive: Perform JIVE Decomposition for Multi-Source Data

Description

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

Usage

jive.predict(data.new, jive.output)

Arguments

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.

Value

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

Author(s)

Adam Kaplan

References

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

See Also

jive

Examples

##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]]

Example output

r.jive documentation built on Nov. 17, 2020, 9:07 a.m.