R/satuRn-framework.R

In statOmics/satuRn: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications

#' @title The StatModel class for satuRn
#'
#' @description
#'
#' The `StatModel` class contains a statistical model generated by 
#' the DTU analysis.
#'
#' Models are created by the dedicated user-level function `fitDTU()`
#' or manually, using the `StatModel()` constructor.
#' In the former case, each quantitative feature is assigned its s
#' tatistical model and the models are stored as a variable in 
#' a `DataFrame` object, that in turn will  be stored in the `RowData` slot of 
#' a `SummarizedExperiment object`.
#'
#' @slot type `character(1)` defining type of the used model. Default is
#' `"fitError"`, i.e. an error model. 
#' If not an error, class type will be `"glm"`.
#' @slot params A `list()` containing the parameters of the fitted model.
#' @slot varPosterior `numeric()` of posterior variance.
#' @slot dfPosterior `numeric()` of posterior degrees of freedom.
#'
#' @rdname satuRn-framework
#'
#' @author Jeroen Gilis
#'
#' @export
#'
#' @examples
#' ## A fully specified dummy model
#' myModel <- StatModel(
#'     type = "glm",
#'     params = list(x = 3, y = 7, b = 4),
#'     varPosterior = c(0.1, 0.2, 0.3),
#'     dfPosterior = c(6, 7, 8)
#' )
#' myModel
.StatModel <- setClass("StatModel",
    slots = c(
        type = "character",
        params = "list",
        varPosterior = "numeric",
        dfPosterior = "numeric"
    )
)

#' @name StatModel
#' @title StatModel
#'
#' @description Function for contstructing a new `StatModel` object.
#'
#' @param type default set to fiterror, can be a glm
#'
#' @param params A list containing the parameters of the fitted glm
#'
#' @param varPosterior Numeric, posterior variance of the glm, default is NA
#'
#' @param dfPosterior Numeric, posterior degrees of freedom of the glm, 
#'     default is NA
#'
#' @examples
#' ## A fully specified dummy model
#' myModel <- StatModel(
#'     type = "glm",
#'     params = list(x = 3, y = 7, b = 4),
#'     varPosterior = c(0.1, 0.2, 0.3),
#'     dfPosterior = c(6, 7, 8)
#' )
#' myModel
#' @return A StatModel object
#'
#' @importFrom methods new
#'
#' @export

StatModel <- function(type = "fitError",
    params = list(),
    varPosterior = NA_real_,
    dfPosterior = NA_real_) {
    out <- new("StatModel")
    out@type <- type
    out@params <- params
    out@varPosterior <- varPosterior
    out@dfPosterior <- dfPosterior
    return(out)
}