gscca.simu.normal: Simulation for gscca

In DongyueXie/scca: Sparse Canonical Correlation Analysis - Intergrated Methods

Description

Simulation for gscca

Usage

gscca.simu.normal(niter, edgex, edgey, u, v, n = 50, data.type = "normal",
  sigma_z = 1, sigma_x = NULL, sigma_y = NULL, theta = 0.01,
  lambda_z = 10, n_x = 1000, n_y = 1000, cv.method = "bic",
  gamma.u = NULL, gamma.v = NULL, lambda.u = NULL, lambda.v = NULL,
  maxsteps = 20, plain = T, Sx = NULL, Sy = NULL, verbose = F,
  K = 5, ccor = F)

Arguments

gamma.u, gamma.v:	ratio of l1 peanlty and fused penalty
lambda.u, lambda.v:	fused penalty. lambda*fused penalty+gamma*lambda*l1 penalty.
Sx, Sy:	structure matrix for Sx,Sy.
maxsteps:	the number of lambdas to try, default to be 20. Largest 20 values from solution path
ccor:	whether re-estiamte the coeeficient when doing BIC

DongyueXie/scca documentation built on May 29, 2019, 2:37 p.m.