Rajive: Robust Angle based Joint and Individual Variation Explained

In RaJIVE: Robust Angle Based Joint and Individual Variation Explained

Description

Computes the robust aJIVE decomposition with parallel computation.

Usage

Rajive(
  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 aJIVE decomposition.

Examples

n <- 50
pks <- c(100, 80, 50)
Y <- ajive.data.sim(K =3, rankJ = 3, rankA = c(7, 6, 4), n = n,
                   pks = pks, dist.type = 1)
initial_signal_ranks <-  c(7, 6, 4)
data.ajive <- list((Y$sim_data[[1]]), (Y$sim_data[[2]]), (Y$sim_data[[3]]))
ajive.results.robust <- Rajive(data.ajive, initial_signal_ranks)

RaJIVE documentation built on Feb. 4, 2021, 5:05 p.m.