bdgraph.npn: Nonparametric transfer

In BDgraph: Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Nonparametric transfer

Description

Transfers non-Gaussian data to Gaussian.

Usage

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

Arguments

data	(n x p) matrix or a data.frame corresponding to the data (n is the sample size and p is the number of variables).
npn	character with three options "shrinkage" (default), "truncation", and "skeptic". Option "shrinkage" is for the shrunken transformation, option "truncation" is for the truncated transformation and option "skeptic" is for the non-paranormal skeptic transformation. For more details see references.
npn.thresh	truncation threshold; it is only for the truncated transformation (npn= "truncation"). The default value is 1/(4n^{1/4} √{π \log(n)}).

Value

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

Author(s)

Reza Mohammadi [e-mail]

References

Liu, H., et al (2012). High Dimensional Semiparametric Gaussian Copula Graphical Models, Annals of Statistics, 40(4):2293-2326

Zhao, T. and Liu, H. (2012). The huge Package for High-dimensional Undirected Graph Estimation in R, Journal of Machine Learning Research, 13:1059-1062

See Also

bdgraph.sim, bdgraph, bdgraph.mpl

Examples

## Not run: 
# Generating multivariate normal 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" )

## End(Not run)

BDgraph documentation built on Dec. 28, 2022, 1:54 a.m.