Rajive {RaJIVE} | R Documentation |
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)
[Package RaJIVE version 1.0 Index]