R/model_bic.R

In Malindrie/scShapes: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data

#' model_bic
#'
#' This function is used to calculate the Bayesian information criterion of the models fitted in
#' \code{fit_models}.
#'
#' @param fit_list A list of models fitted from \code{fit_models}
#'
#' @export
#'
#' @importFrom stats BIC
#' @importFrom methods is
#'
#' @return A dataframe containing the BIC values for each distribution type (P, NB, ZIP, ZINB).
#'
#' @examples
#'
#' data(scData)
#'
#' # apply the model_bic function to calculate the BIC values on the models
#' # obtained after running fit_models function.
#'
#' library(BiocParallel)
#' scData_models <- fit_models(counts=scData$counts, cexpr=scData$covariates, lib.size=scData$lib_size,
#' BPPARAM=bpparam())
#'
#' scData_bicvals <- model_bic(scData_models)


model_bic <- function(fit_list){

  bic_p <- t(as.data.frame(lapply(fit_list$P, model_BIC)))
  bic_nb <- t(as.data.frame(lapply(fit_list$NB, model_BIC)))
  bic_zip <- t(as.data.frame(lapply(fit_list$ZIP, model_BIC)))
  bic_zinb <- t(as.data.frame(lapply(fit_list$ZINB, model_BIC)))

  bic <- cbind(bic_p, bic_nb, bic_zip, bic_zinb)
  colnames(bic) <- c("P_bic", "NB_bic", "ZIP_bic", "ZINB_bic")

  return(bic)

}


model_BIC <- function(z){
  ifelse((!is(z, "character")),
         stats::BIC(z), "NA")
}