singleIC {GFM}R Documentation

IC(PC) criteria for selecting number

Description

IC(PC) criteria for selecting number of factors in generalized factor models.

Usage

  singleIC(X, group, type, q_set=1:10, dropout=0, dc_eps=1e-4,
      maxIter=10,output=FALSE, fast_version=TRUE)

Arguments

X

a matrix with dimension of n*p(p=(p1+p2+..+p_d)),observational mixed data matrix, d is the types of variables, p_j is the dimension of j-th type variable.

group

a vector with length equal to p, specify each column of X belonging to which group.

type

a d-dimensional character vector, specify the type of variables in each group. For example, type=c'poisson', 'binomial'), and it is referred to the help file of glm.fit function for more details.

q_set

a positive integer vector, specify the candidates of factor number q, (optional) default as c(1:10) according to Bai,2013.

dropout

a proper subset of $[1, 2, ..., d]$, specify which group to be dropped in obtaining the initial estimate of factor matrix $H$, and the aim is to ensure the convergence of algorithm leaded by weak signal variable types. Optional parameter with default as 0, no group dropping.

dc_eps

positive real number, specify the tolerance of varing quantity of objective function in the algorithm. Optional parameter with default as 1e-4.

maxIter

a positive integer, specify the times of iteration. Optional parameter with default as 50.

output

a logical value with TRUE or FALSE, specify whether ouput the mediate information in iteration process, (optional) default as FALSE.

fast_version

logical value with TRUE or FALSE, fast_version = TRUE: use the fast algorithm which omit the one-step updating, but it cannot ensure the estimation efficieny; fast_version = FALSE: use the original algorithm; (optional) default as FALSE;

Details

This function also has the MATLAB version at https://github.com/feiyoung/MGFM/blob/master/singleIC.m, which runs faster in MATLAB environment.

Value

return an integer, the estimated number of factors.

Note

nothing

Author(s)

Liu Wei

References

Liu, W., Lin, H., Zheng, S., & Liu, J. (2021). Generalized factor model for ultra-high dimensional correlated variables with mixed types. Journal of the American Statistical Association, (just-accepted), 1-42.

Bai, J. and Liao, Y. (2013). Statistical inferences using large esti- mated covariances for panel data and factor models.

See Also

nothing

Examples

  ## Homogeneous  normal variables
  dat <- gendata(q = 2, n=100, p=100, rho=3)
  group <- rep(1,ncol(dat$X))
  type <- 'gaussian'
  # select q automatically
  singleIC(dat$X, group, type, q_set = 1:3, output = FALSE)

[Package GFM version 1.1.0 Index]