Robust Angle based Joint and Individual Variation Explained

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,
  num_cores = 1L
)

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.
num_cores: Integer. Number of cores to use for parallel computation (block SVD, singular value extraction, Wedin bound resampling, and random direction bound sampling). Default 1L (serial). Set to a value greater than 1 to enable parallel execution via mclapply and registerDoParallel.

Value

An object of class "rajive": a named list containing:

block_decomps: A list matrix (length \(3 \times K\)) of per-block decompositions. For block \(k\): individual component at index \(3(k-1)+1\), joint component at \(3(k-1)+2\), noise (residual) at \(3(k-1)+3\).
joint_scores: The \(n \times r_J\) matrix of shared joint score vectors, where \(r_J\) is the estimated (or user-supplied) joint rank.
joint_rank: Integer. The estimated (or user-supplied) joint rank.
joint_rank_sel: A list of diagnostic information from the joint rank selection step (observed singular values, Wedin samples, random direction samples, thresholds).

Examples

# \donttest{
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)
# }