NPN - Non paranormal Transformation

This functions allows us to transform non-normal multivariate data to that of non paranormal data.

Usage

npn(x, npn_func = "shrinkage", npn_thresh = NULL, verbose = TRUE)

Arguments

x

The multivariate non-normal data to be transformed.

npn_func

Optional parameter - The method of transformation to be applied. Can either be "shrinkage" or "truncation" but defaults to "shrinkage".

npn_thresh

Optional parameter - The truncation threshold that is used when making use of truncation.

verbose

Optional parameter - Prints additional output of the selected approach. Can either be "TRUE" or "FALSE" and defaults to "TRUE".

Value

Returns the transformed data matrix.

References

Liu, H., Han, F., Yuan, M., Lafferty, J. and Wasserman, L., 2012. The nonparanormal skeptic. arXiv preprint arXiv:1206.6488.

Liu, H., Lafferty, J. and Wasserman, L., 2009. The nonparanormal: Semiparametric estimation of high dimensional undirected graphs. Journal of Machine Learning Research, 10(10).

Xue, L. and Zou, H., 2012. Regularized rank-based estimation of high-dimensional nonparanormal graphical models. The Annals of Statistics, 40(5), pp.2541-2571.

Examples

data <- data_generator(n_X = 100, p = 50, seed = 123)
X <- data$X
X_transformed <- npn(X, npn_func = "truncation")
#> Nonparanomral transformation via truncated ECDF.