R/statistics.R

In astamm/fdahotelling: Inference for Functional Data Analysis in R

#' Statistics for functional data
#'
#' Compute various statistics for functional data using numerical integration
#' when needed. A total of 7 statistics are available in the package and the
#' user can even define its own statistic for use within the statistical test
#' functions.
#'
#' @seealso A detailed description of the individual statistics for functional
#'   data provided by the \pkg{fdahotelling} package can be found in the vignette
#'   \emph{Available statistics for functional data}.
#' @param x Dataframe or matrix containing the data collected from 1st
#'   population.
#' @param y Dataframe or matrix containing the data collected from 2nd
#'   population (default: \code{NULL}).
#' @param mu True mean value (or difference between mean values) under the null
#'   hypothesis (default = \code{0}).
#' @param paired Is the input data paired? (default: \code{FALSE}).
#' @param step_size The step size used to perform integral approximation via the
#'   method of rectangles (default: \code{0}). When set to \code{0}, it assumes
#'   that we are dealing with multivariate data rather than functional data and
#'   thus no integration is necessary.
#' @return All \code{stat_*} functions return a named numeric vector of size 1
#'   storing the value of the corresponding statistic except \code{stat_all}
#'   that outputs a vector of size 7 containing the values of all 7 statistics
#'   currently available in the \pkg{fdahotelling} package.
#' @name statistics
NULL

#' @rdname statistics
#' @export
stat_hotelling <- function(x, y = NULL, mu = NULL,
                           paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_hotelling_impl(x = l$x, y = l$y, mu = l$mu,
                      paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_L1 <- function(x, y = NULL, mu = NULL,
                    paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_L1_impl(x = l$x, y = l$y, mu = l$mu,
               paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_L1_std <- function(x, y = NULL, mu = NULL,
                        paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_L1_std_impl(x = l$x, y = l$y, mu = l$mu,
                   paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_L2 <- function(x, y = NULL, mu = NULL,
                    paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_L2_impl(x = l$x, y = l$y, mu = l$mu,
               paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_L2_std <- function(x, y = NULL, mu = NULL,
                        paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_L2_std_impl(x = l$x, y = l$y, mu = l$mu,
                   paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_Linf <- function(x, y = NULL, mu = NULL,
                      paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_Linf_impl(x = l$x, y = l$y, mu = l$mu,
                 paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_Linf_std <- function(x, y = NULL, mu = NULL,
                          paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_Linf_std_impl(x = l$x, y = l$y, mu = l$mu,
                     paired = paired, step_size = step_size)
}

#' @rdname statistics
#' @export
stat_all <- function(x, y = NULL, mu = NULL,
                     paired = FALSE, step_size = 0) {
  l <- check_arguments(x = x, y = y, mu = mu, step_size = step_size)
  stat_all_impl(x = l$x, y = l$y, mu = l$mu,
                paired = paired, step_size = step_size)
}