R/add_model_criterion.R

In bsitar: Bayesian Super Imposition by Translation and Rotation Growth Curve Analysis

#' Add model fit criteria to model
#' 
#' @description The \strong{add_model_criterion()} is a wrapper around the
#'   [brms::add_criterion()]. Note that arguments \code{compare} and
#'   \code{pointwise} are relevant only for [brms::add_loo] whereas arguments
#'   \code{summary}, \code{robust}, and \code{probs} ignored except for the
#'   [brms::bayes_R2()].
#' 
#' @param model An object of class \code{bgmfit}.
#' 
#' @inheritParams growthparameters.bgmfit
#' @inherit brms::add_criterion.brmsfit params description return
#' @inheritParams brms::bayes_R2.brmsfit
#' @inheritParams brms::waic.brmsfit
#' @inheritParams fitted_draws.bgmfit
#'  
#' @return An object of class class \code{bgmfit} with fit criteria added.
#' 
#' @export
#' @seealso [brms::add_loo] [brms::add_loo] [brms::add_ic()] [brms::add_waic()]
#'   [brms::bayes_R2()]
#' 
#' @inherit berkeley author
#'
#' @examples
#' 
#' \donttest{
#' # Fit Bayesian SITAR model 
#' 
#' # To avoid mode estimation which takes time, the Bayesian SITAR model fit to 
#' # the 'berkeley_exdata' has been saved as an example fit ('berkeley_exfit').
#' # See 'bsitar' function for details on 'berkeley_exdata' and 'berkeley_exfit'.
#' 
#' model <- berkeley_exfit
#' 
#' model <- add_model_criterion(model, criterion = c("waic"))
#' 
#' }
#' 
add_model_criterion.bgmfit <-
  function(model,
           criterion = c("loo", "waic"),
           ndraws = NULL,
           draw_ids = NULL,
           compare = TRUE,
           pointwise = FALSE,
           model_names = NULL,
           summary = TRUE,
           robust = FALSE,
           probs = c(0.025, 0.975),
           newdata = NULL,
           resp = NULL,
           cores = 1,
           deriv_model = NULL,
           verbose = FALSE,
           expose_function = FALSE,
           usesavedfuns = NULL,
           clearenvfuns = NULL,
           envir = NULL,
           ...) {
    
    if(is.null(envir)) {
      if(!is.null(model$model_info$exefuns[[1]])) {
        envir <- environment(model$model_info$exefuns[[1]])
      } else {
        envir <- parent.frame()
      }
    }
    
    if(is.null(usesavedfuns)) {
      if(!is.null(model$model_info$exefuns[[1]])) {
        usesavedfuns <- TRUE
      } else if(is.null(model$model_info$exefuns[[1]])) {
        if(expose_function) {
          model <- expose_model_functions(model, envir = envir)
          usesavedfuns <- TRUE
        } else if(!expose_function) {
          usesavedfuns <- FALSE
        }
      }
    } else { # if(!is.null(usesavedfuns)) {
      if(!usesavedfuns) {
        if(expose_function) {
          model <- expose_model_functions(model, envir = envir)
          usesavedfuns <- TRUE
        }
      } else if(usesavedfuns) {
        check_if_functions_exists(model, checks = TRUE, 
                                  usesavedfuns = usesavedfuns)
      }
    }
    
    
    
    if(is.null(ndraws)) {
      ndraws <- brms::ndraws(model)
    }
    
    if(is.null(deriv_model)) {
      deriv_model <- TRUE
    }
    
  
    
    full.args <- evaluate_call_args(cargs = as.list(match.call())[-1], 
                                           fargs = formals(), 
                                           dargs = list(...), 
                                           verbose = verbose)
    
    full.args$model <- model
    full.args$deriv <- deriv <- 0
    
    if(!is.null(model$xcall)) {
      arguments <- get_args_(as.list(match.call())[-1], model$xcall)
      newdata <- newdata
    } else {
      newdata <- do.call(get.newdata, full.args)
    }
    
    
    if(!is.null(model$model_info$decomp)) {
      if(model$model_info$decomp == "QR") deriv_model<- FALSE
    }
    
    expose_method_set <- model$model_info[['expose_method']]
    
    model$model_info[['expose_method']] <- 'NA' # Over ride method 'R'
    
    o <- post_processing_checks(model = model,
                                xcall = match.call(),
                                resp = resp,
                                envir = envir,
                                deriv = deriv, 
                                all = FALSE,
                                verbose = verbose)
    
    oall <- post_processing_checks(model = model,
                                   xcall = match.call(),
                                   resp = resp,
                                   envir = envir,
                                   deriv = deriv, 
                                   all = TRUE,
                                   verbose = FALSE)
    
    
    test <- setupfuns(model = model, resp = resp,
                      o = o, oall = oall, 
                      usesavedfuns = usesavedfuns, 
                      deriv = deriv, envir = envir, 
                      deriv_model = deriv_model, 
                      ...)
    
    if(is.null(test)) return(invisible(NULL))
    
    
    if(!isTRUE(
      check_pkg_version_exists('brms', minversion = '2.20.17', 
                               prompt = FALSE,
                               stop = FALSE,
                               verbose = FALSE))) {
      if(is.null(check_if_functions_exists(model, o, model$xcall,
                                           usesavedfuns = usesavedfuns))) {
        return(invisible(NULL))
      }
    }
    
    
    misc <- c("verbose", "usesavedfuns", "clearenvfuns", 
              "envir", "fullframe", "dummy_to_factor")
    calling.args <- post_processing_args_sanitize(model = model,
                                          xcall = match.call(),
                                          resp = resp,
                                          envir = envir,
                                          deriv = deriv, 
                                          dots = list(...),
                                          misc = misc,
                                          verbose = verbose)
   
    
    calling.args$object    <- full.args$model
    calling.args$x         <- full.args$model
    calling.args$object    <- full.args$model <- NULL
    calling.args$criterion <- criterion
    
    suppressWarnings({
      . <- do.call(brms::add_criterion, calling.args)
    })
    
    
   
    # Restore function(s)
    assign(o[[1]], model$model_info[['exefuns']][[o[[1]]]], envir = envir)
    
    if(!is.null(eval(full.args$clearenvfuns))) {
      if(!is.logical(eval(full.args$clearenvfuns))) {
        stop('clearenvfuns must be NULL or a logical')
      } else {
        setcleanup <- eval(full.args$clearenvfuns)
      }
    }
    
    if(is.null(eval(full.args$clearenvfuns))) {
      if(is.null(eval(full.args$usesavedfuns))) {
        full.args$usesavedfuns <- usesavedfuns
      }
      if(eval(full.args$usesavedfuns)) {
        setcleanup <- TRUE 
      } else {
        setcleanup <- FALSE
      }
    }
    
    # Cleanup environment if requested
    if(setcleanup) {
      suppressWarnings({
        tempgenv <- envir
        for (oalli in names(oall)) {
          if(exists(oalli, envir = tempgenv )) {
            remove(list=oalli, envir = tempgenv)
          }
        }
        tempgenv <- test
        for (oalli in names(oall)) {
          if(exists(oalli, envir = tempgenv )) {
            remove(list=oalli, envir = tempgenv)
          }
        }
      })
    } # if(setcleanup) {
    
    .
  }


#' @rdname add_model_criterion.bgmfit
#' @export
add_model_criterion <- function(model, ...) {
  UseMethod("add_model_criterion")
}