huge (version 0.7)

huge.npn: Nonparanormal transformation

Description

Implements the nonparanormal transformation to relax the normality assumption.

Usage

huge.npn(x, npn.func = "shrinkage", npn.thresh, verbose = TRUE)

Arguments

x

The n by d data matrix representing n observations in d dimensions

npn.func

The transformation function used in the nonparanormal transformation. If npn.func = "truncation", the truncated ECDF is applied. If npn.func = "shrinkage", the shrunken ECDF is applied. The default is "shrinkage".

npn.thresh

The truncation threshold used in nonparanormal transformation, only applicable when npn.func = "truncation". The default value is 1/(4*(n^0.25)* sqrt(pi*log(n))).

verbose

If verbose = FALSE, tracing information printing is disabled. The default value is TRUE.

Value

• An object with S3 class "npn" is returned:
• data
  The n by d data matrix representing n observations in d transformed dimensions
• npn.func
  The npn.func from the input

Details

The transformed data are already standardized as sample mean zero and unit variance.

References

Tuo Zhao and Han Liu. HUGE: A Package for High-dimensional Undirected Graph Estimation. Technical Report, Carnegie Mellon University, 2010 Han Liu, John Lafferty and Larry Wasserman. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research (JMLR), Vol.10, Page 2295-2328, 2009

See Also

huge and huge-package.

Examples

n = 100
L = huge.generator(n=n, graph = "hub")

# nonparanormal transformation using the shrunken ECDF
Q = huge.npn(L$data)

# nonparanormal transformation using the truncated ECDF
Q = huge.npn(L$data, npn.func = "truncation")


# nonparanormal transformation using truncated ECDF with specific threhold
Q = huge.npn(L$data, npn.func = "truncation", npn.thresh = 1/n)