bdgraph.npn: Nonparametric transfer

Description

This function transfer non-Gaussian data to Gaussian models.

Usage

bdgraph.npn(data, npn = "shrinkage", npn.thresh = NULL)

Arguments

data

it could be a matrix or a data.frame of the data ( $n \times p$).

npn

a character with three options "shrinkage" (default), "truncation", and "skeptic". Option "shrinkage" is for the shrunken transformation, option "truncation" is for the truncated transformation, option "skeptic" is for the non-paranormal skeptic t

npn.thresh

the truncation threshold; it is only for the truncated transformation (when npn = "truncation"). The default value is $1/(4n^{1/4} \sqrt{\pi \log(n)})$.

Value

dataa $n \times p$ matrix of transferred data, if npn = "shrinkage" or "truncation", and a non-paranormal correlation matrix ($p \times p$), if npn = "skeptic".

References

Liu, H., Han, F., Yuan, M., Lafferty, J., and Wasserman, L., (2012). High Dimensional Semiparametric Gaussian Copula Graphical Models. Annals of Statistics Zhao, T. and H. Liu. (2012). The huge Package for High-dimensional Undirected Graph Estimation in R. Journal of Machine Learning Research Mohammadi, A. and E. C. Wit (2012). Gaussian graphical model determination based on birth-death MCMC inference, arXiv:1210.5371v4. http://arxiv.org/abs/1210.5371v4

Examples

Run this code

# generating the data from a random graph
  data.sim <- bdgraph.sim(n = 6, p = 4, size = 4)
  data     <- (data.sim $ data -3) ^ 4
  data
  
  # transfer the data by truncation 
  bdgraph.npn(data, npn = "truncation")
  
  # transfer the data by shrunken 
  bdgraph.npn(data, npn = "shrunken")

  # transfer the data by skeptic 
  bdgraph.npn(data, npn = "skeptic")