huge.npn {huge}

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". If npn.func = "skeptic", the nonparanormal skeptic is applied.

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.

Details

The nonparanormal extends Gaussian graphical models to semiparametric Gaussian copula models.Motivated by sparse additive models, the nonparanormal method estimates the Gaussian copula by marginally transforming the variables using smooth functions.Computationally, the estimation of a nonparanormal transformation is very efficient and only requires one pass of the data matrix.

Value

data

A d by d nonparanormal correlation matrix if npn.func = "skeptic", and A n by d data matrix representing n observations in d transformed dimensions other wise.

See Also

huge and huge-package.

Examples

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

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

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

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