Joint and Individual Variation Explained - JIVE

This is a wrapper for the r.jive::jive function for computing JIVE.

Usage

jive(X, ...)

Arguments

X

list of input blocks.

...

additional arguments for r.jive::jive.

Value

multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots and result functions: multiblock_results.

Details

Jive performs a decomposition of the variation in two or more blocks into low-dimensional representations of individual and joint variation plus residual variation.

References

Lock, E., Hoadley, K., Marron, J., and Nobel, A. (2013) Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann Appl Stat, 7 (1), 523–542.

See also

Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.

Examples

 # Too time consuming for testing
  data(candies)
  candyList <- lapply(1:nlevels(candies$candy),function(x)candies$assessment[candies$candy==x,])
  can.jive  <- jive(candyList)
#> Estimating  joint and individual ranks via permutation...
#> Running JIVE algorithm for ranks:
#> joint rank: 1 , individual ranks: 1 1 1 1 1 
#> JIVE algorithm converged after  16  iterations.
#> Re-estimating  joint and individual ranks via permutation...
#> Running JIVE algorithm for ranks:
#> joint rank: 1 , individual ranks: 1 0 1 0 1 
#> JIVE algorithm converged after  13  iterations.
#> Re-estimating  joint and individual ranks via permutation...
#> Final joint rank: 1 , final individual ranks: 1 0 1 0 1 
  summary(can.jive)
#> $Method
#> [1] "perm"
#> 
#> $Ranks
#>      Source     Rank
#> [1,] "Joint"    "1" 
#> [2,] "Source_1" "1" 
#> [3,] "Source_2" "0" 
#> [4,] "Source_3" "1" 
#> [5,] "Source_4" "0" 
#> [6,] "Source_5" "1" 
#> 
#> $Variance
#>            Source_1 Source_2 Source_3 Source_4 Source_5
#> Joint         0.690    0.852    0.793    0.878    0.624
#> Individual    0.079    0.000    0.056    0.000    0.223
#> Residual      0.230    0.148    0.151    0.122    0.153
#>