R/HDTD-package.R

In HDTD: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD)

#' Estimation and Hypothesis Testing in High-Dimensional Transposable Data
#'
#' The package HDTD offers functions to estimate and test the matrix parameters
#' of transposable data in high-dimensional settings.
#'
#' The term transposable data refers to datasets that are structured in a
#' matrix form such that both the rows and columns correspond to variables of
#' interest. For example, consider microarray studies in genetics where
#' multiple RNA samples across different tissues are available per subject. In
#' this case, a data matrix can be created with row variables the genes, column
#' variables the tissues and measurements the corresponding expression levels.
#'
#' The function \code{\link{meanmat.hat}} estimates the mean matrix of the
#' transposable data.
#'
#' The mean relationship of the row and column variables can be tested using
#' the function \code{\link{meanmat.ts}}. The implemented test is nonparametric
#' and not seriously restricted by the dependence structure among and/or
#' between the row and column variables.
#' See \cite{Touloumis et al. (2015)} for more details.
#'
#' The function \code{\link{covmat.hat}} provides Stein-type shrinkage
#' estimators for the row covariance matrix and/or for the column covariance
#' matrix under a matrix-variate normal model.
#' See \cite{Touloumis et al. (2016)} for more details.
#'
#' The sphericity and identity hypothesis for the row or column covariance
#' matrix can be tested using the function \code{\link{covmat.ts}}. Both tests
#' are nonparametric, i.e., they do not rely on a normality assumption.
#' See \cite{Touloumis et al. (2017)} for more details.
#'
#' There are three utility functions that allow the user to change to
#' interchange the role of row and column variables
#' (\code{\link{transposedata}}), to center the transposable data
#' (\code{\link{centerdata}}) or to rearrange the order of the row and/or
#' column variables (\code{\link{orderdata}}).
#'
#' @name HDTD-package
#' @aliases HDTD-package HDTD
#' @docType package
#' @author Anestis Touloumis, John Marioni, Simon Tavare.
#'
#' Maintainer: Anestis.Touloumis <A.Touloumis@@brighton.ac.uk>
#' @references Touloumis, A., Tavare, S. and Marioni, J. C. (2015) Testing the
#' Mean Matrix in High-Dimensional Transposable Data.
#' \emph{Biometrics} \bold{71}, 157--166
#'
#' Touloumis, A., Marioni, J. C. and Tavare, S. (2016) HDTD: Analyzing
#' multi-tissue gene expression data. \emph{Bioinformatics}
#' \bold{32}, 2193--2195.
#'
#' Touloumis, A., Marioni, J. C. and Tavare, S. (2019+) Hypothesis Testing for
#' the Covariance Matrix in High-Dimensional Transposable Data with Kronecker
#' Product Dependence Structure. \emph{Statistica Sinica}.
#' @keywords package
#' @examples
#' data(VEGFmouse)
#' ## The sample mean matrix.
#' sample_mean <- meanmat.hat(datamat = VEGFmouse, N = 40)
#' sample_mean
#' ## Testing conservation of the overall gene expression across tissues.
#' tissues_mean_test <- meanmat.ts(datamat = VEGFmouse, N = 40, group.sizes = 9)
#' tissues_mean_test
#' # Estimating the gene and column covariance matrices.
#' est_cov_mat <- covmat.hat(datamat = VEGFmouse, N = 40)
#' est_cov_mat
#' ## Hypothesis tests for the covariance matrix of the genes (rows).
#' genes_cov_test <- covmat.ts(datamat = VEGFmouse, N = 40)
#' genes_cov_test
#' ## Hypothesis tests for the covariance matrix of the tissues (columns).
#' tissues_cov_test <- covmat.ts(datamat = VEGFmouse, N = 40, voi = 'columns')
#' tissues_cov_test
#' @useDynLib HDTD, .registration = TRUE
#' @import Rcpp
#' @importFrom stats na.omit pnorm
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