ajive: ajive: Angle based Joint and Individual Variation Explained.

In idc9/r_jive: Angle Based Joint and Individual Variation Explained

Description

The ajive package provides functions to do: foo, bar and baz.

Computes the JIVE decomposition.

Usage

ajive(blocks, initial_signal_ranks, full = TRUE,
  n_wedin_samples = 1000, n_rand_dir_samples = 1000, joint_rank = NA)

Arguments

blocks	List. A list of the data matrices.
initial_signal_ranks	Vector. The initial signal rank estimates.
full	Boolean. Whether or not to store the full J, I, E matrices or just their SVDs (set to FALSE to save memory).
n_wedin_samples	Integer. Number of wedin bound samples to draw for each data matrix.
n_rand_dir_samples	Integer. Number of random direction bound samples to draw.
joint_rank	Integer or NA. User specified joint_rank. If NA will be estimated from data.

Value

The JIVE decomposition.

Foo functions

The foo functions ...

Examples

blocks <- sample_toy_data(n=200, dx=100, dy=500)
initial_signal_ranks <- c(2, 2)
jive_decomp <- ajive(blocks, initial_signal_ranks)

joint_scores <- jive_decomp[['joint_scores']]
J_1 <- jive_decomp[[1]][['joint']][['full']]
U_individual_2 <- jive_decomp[[2]][['individual']][['u']]
individual_rank_2 <- jive_decomp[[2]][['individual']][['rank']]

idc9/r_jive documentation built on Oct. 20, 2020, 4:23 p.m.