R/check_input_args.R

In tystan/deltacomp: Functions to Analyse Compositional Data and Produce Confidence Intervals for Relative Increases and Decreases in the Compositional Components

#' Sanity checks for arguments passed to predict_delta_comps()
#'
#' @description Sanity checks for arguments passed to predict_delta_comps()
#'
#' @param dataf A \code{data.frame} containing data
#' @param y Name (as string/character vector of length 1) of outcome variable in \code{dataf}
#' @param comps Character vector of names of compositions in \code{dataf}. See details for more information.
#' @param covars Character vector of covariates names  (non-comp variables) in \code{dataf} or NULL for none (default).
#' @param deltas A vector of time-component changes (as proportions of compositions , i.e., values between -1 and 1). Optional. 
#' @export
#' @details 
#' Throws errors for any problematic input. Returns \code{TRUE} invisibly if no issues found.
# @examples
# check_input_args(
#   dataf = fat_data,
#   y = "fat",
#   comps = c("sl", "sb", "lpa", "mvpa"),
#   covars = NULL,
#   deltas = seq(-60, 60, by = 5) / (24 * 60)
# )
# 
# 




check_input_args <- function(dataf, y, comps, covars, deltas) {
  
  
  if (!is.data.frame(dataf)) {
    stop("dataf supplied must be a data.frame.")
  } else if (nrow(dataf) < 1) {
    stop("dataf supplied must be a non-empty data.frame.")
  }
  
  if (is_null_or_na(y) | length(y) != 1 | !is.character(y)) {
    stop("Please supply a character string for the outcome column in dataf.")
  }
  
  if (is_null_or_na(comps)) {
    stop("The provided compositional column names must be a non-empty vector.")
  } else if (!is.character(comps)) {
    stop("Please supply a character string of the compositional component column names in dataf.")
  } else if (length(comps) < 2) {
    stop("At least two compositional components are required to create an ilr linear regression.")
  }
  
  if (!is_null_or_na(covars) & !is.character(covars)) {
    stop("Please supply a character string of the covariate column names in dataf (optionally NULL or NA for no covariates).")
  } 
  
  if (any(abs(deltas) > 1)) {
    stop("deltas must be specified as positive and negative proportions of a composition. i.e., values in (-1, 1).")
  }
  
  n_covars <- ifelse(is_null_or_na(covars), 0, length(covars))
  if (nrow(dataf) <= (length(comps) + n_covars)) {
    stop("The number of rows in dataf should far exceed the number of predictors (compositional variables and covariates). Reconsider your model specification.")
  }
  
  
  return(invisible(TRUE))
  
  
}