gendata: Generate simulated data

In feiyoung/SpagFainfer: Estimation and inference on high-dimensional group-sparse factor models

Description

Generate simulated data from high dimensional group-sparse factor model.

Usage

  gendata(n, p, seed=1, q=6, pzero= floor(p/4), sigma2=0.1, gamma=1, heter=F, rho=1)

Arguments

n	a positive integer, the sample size.
p	an positive integer, the variable dimension.
seed	a nonnegative integer, the random seed, default as 1.
q	a positive integer, the number of factors.
pzero	a positive integer, the number of zero loading vectors, default as p/4.
sigma2	a positive real number, the homogenous variance of error term.
gamma	a positive number, the common component of heteroscedasticity of error term.
heter	a logical value, indicates whether generate heteroscendastic error term.
rho	a positive number, controlling the magnitude of loading matrix.

Value

return a list including two components:

X	a n-by-p matrix, the observed data matrix.
H0	a n-by-q matrix, the true lantent factor matrix.
B0	a p-by-q matrix, the true loading matrix, the last pzero rows are vectors of zeros.
ind_nz	a integer vector, the index vector for which rows of B0 not zeros.

Note

nothing

Author(s)

Liu Wei

See Also

Factorm.

Examples

  dat <- gendata(n=300, p = 500)
  str(dat)

feiyoung/SpagFainfer documentation built on April 4, 2020, 5:20 p.m.