huge (version 1.0.3)

huge.npn: NonparaNormal(NPN) transformation

Description

Implements the Gausianization to help relax the assumption of normality.

Usage

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

Arguments

x

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

npn.func

The transformation function used in the npn 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

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

Details

The NPN is a very helpful tool to relax the normality assumption. It assumes the data come from a Gaussian cornopula and computationally, fitting a high dimensional npn is no more difficult than estimating a multivariate Gaussian and one can also apply to other existing estimation algorithms. The output data are already standardized as sample mean zero and unit variance.

References

1.Tuo Zhao and Han Liu. HUGE: A Package for High-dimensional Undirected Graph Estimation. Technical Report, Carnegie Mellon University, 2010 2.Han Liu, John Lafferty and Larry Wasserman. The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. Journal of Machine Learning Research (JMLR), 2009

See Also

huge and huge-package.

Examples

# generate data
L = huge.generator(graph = "cluster", g = 5)

# transform the non-Gaussian data using the shrunken ECDF
Q = huge.npn(L$data^5)

# transform the non-Gaussian data using the truncated ECDF
Q = huge.npn(5^(L$data), npn.func = "truncation")