R/Class_tskrrHomogeneous.R

In xnet: Two-Step Kernel Ridge Regression for Network Predictions

#' Class tskrrHomogeneous
#'
#' The class tskrrHomogeneous is a subclass of the superclass
#' \code{\link[xnet:tskrr-class]{tskrr}} specifically for
#' homogeneous networks.
#'
#' @slot y the matrix with responses
#' @slot k the eigen decomposition of the kernel matrix for the rows
#' @slot lambda.k the lambda value used for k
#' @slot pred the matrix with the predictions
#' @slot symmetry a character value that can have the possible values
#' \code{"symmetric"}, \code{"skewed"} or \code{"not"}. It indicates
#' whether the \code{y} matrix is symmetric, skewed-symmetric or not
#' symmetric.
#' @slot has.hat a logical value indicating whether the kernel hat matrices
#' are stored in the object.
#' @slot Hk the kernel hat matrix for the rows.
#' @slot labels a list with elements \code{k} and \code{g} (see
#' \code{\link{tskrr-class}}). For homogeneous networks, \code{g}
#' is always \code{NA}. If \code{k} is \code{NA}, the labels used
#' are integers indicating the row resp column number.
#'
#' @include Class_tskrr.R
#' @rdname tskrrHomogeneous-class
#' @name tskrrHomogeneous-class
#' @aliases tskrrHomogeneous
#' @exportClass tskrrHomogeneous
setClass("tskrrHomogeneous",
         contains = "tskrr",
         slots = c(symmetry = "character"),
         prototype = list(symmetry = "not")
         )

validTskrrHomogeneous <- function(object){

  if(!object@symmetry %in% c("symmetric","skewed", "not"))
    return("symmetry should be one of: symmetric, skewed or not.")

  else if(object@has.hat && !valid_dimensions(object@y, object@Hk))
    return("The dimensions of the original kernel matrices and the observations don't match.")

  else if(!length(object@labels$g) == 1 || !is.na(object@labels$g))
    return("The element g of labels should be NA")

  else if(
    (length(object@labels$k) == 1 && !is.na(object@labels$k)) &&
    (length(object@labels$k) != nrow(object@y))
     )
    return("The element k should either be NA or a character vector with the same number of values as there are rows in the Y matrix.")

  else
    return(TRUE)
}

setValidity("tskrrHomogeneous",
            validTskrrHomogeneous)