This is a wrapper for the r.jive::jive function for computing JIVE.
jive(X, ...)list of input blocks.
additional arguments for r.jive::jive.
multiblock object including relevant scores and loadings. Relevant plotting functions: multiblock_plots
and result functions: multiblock_results.
Jive performs a decomposition of the variation in two or more blocks into low-dimensional representations of individual and joint variation plus residual variation.
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.
Overviews of available methods, multiblock, and methods organised by main structure: basic, unsupervised, asca, supervised and complex.
# 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
#>