R/findPartialRsquared.R

In BIMSBbioinfo/deconvR: Simulation and Deconvolution of Omic Profiles

#' @title A function to extract partial R-squares from the model results based
#' on users desicions
#' @importFrom assertthat assert_that
#' @importFrom rsq rsq.partial
#' @importFrom stats lm
#' @details This function checks if deconvolution brings advantages on top of
#' the basic bimodal methylation profiles through partial R-squares. The
#' reference matrix usually follows a bimodal distribution in the case of
#' methylation, and taking the average of  the rows of methylation matrix might
#' give a pretty similar profile to the bulk methylation profile you are trying
#' to deconvolute. If the deconvolution is advantageous, partial R-squared is
#' expect to be high.
#' @param vec The user may provide a vector with which partial R-squared of the
#' results will be calculated. The length must match the number of rows of the
#' reference and bulk tables merged on the ID column (with NAs removed.Defaults
#' to row means of reference.
#' @param observed the observed value of the dependent variable
#' @param predicted the predicted value of the dependent variable
#' @param ref reference atlas provided by the user in matrix form
#' @param mix bulk data provided by the user in matrix form
#' @return a partial Rsuare value obtained from the result of the model
#' @keywords internal
#' @noRd
findPartialRsquared <- function(observed, predicted, ref, vec) {
  ## vector defaults to row means of reference
  if (is.null(vec)) {
    vec <- rowMeans(ref)
  } else {
    assert_that(length(vec) == length(predicted),
      msg = paste(
        "vector should be length",
        length(predicted), "but is length",
        length(vec)
      )
    )
  }
  # calculate partial r-squared
  rsq_partial <- rsq::rsq.partial(
    lm(predicted ~ observed + vec),
    lm(predicted ~ vec)
  )$partial.rsq
}