Linear Dependence Statistics for High-Dimensional data
This package consists of functions with statistical methods related to the estimation and testing of multiple correlation, partial correlation and regression coefficient matrices when data is high-dimensional (n<p).
Joint estimation of two partial correlation matrices (see
and joint estimation of two regression coefficient matrices (see
are currently implemented in the package. These use a weighted-fused lasso penalized maximum
likelihood estimator such that they encourage both sparsity and similarity between estimated
ldstatsHD also contains approaches to select the sparsity tuning parameter of graphical
lasso estimators such that several risk functions based on characteristics of the estimated
networks are available (see
lambdaSelection). Among others, statistics that
measure clustering structure or network connectivity can be used to find the desired networks.
It finally includes statistical methods that test global dependence characteristics:
(i) a test for equality of two correlation matrices as well as a test for Identity correlation matrix
eqCorrMatTest); (ii) a test for equality of two correlation matrix rows
as well as a test to check if a variable is linearly independent of the rest of the variables in a dataset
A particularity of the implemented methods in ldstatsHD is that it permits cases where datasets are dependent (e.g. paired data).
ldstatsHD provides two partial correlation matrix simulators such that all methods can be tested using
using simulated data: see
pcorSimulator to generate a single partial correlation / dataset
pcorSimulatorJoint to generate a joint partial correlation matrix and two (dependent) datasets.
Caballe, Adria <email@example.com>, Natalia Bochkina and Claus Mayer.
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.