R/fit_gau.R

In sjevelazco/flexsdm: Tools for Data Preparation, Fitting, Prediction, Evaluation, and Post-Processing of Species Distribution Models

#' Fit and validate Gaussian Process models
#'
#' @param data data.frame. Database with response (0,1) and predictors values.
#' @param response character. Column name with species absence-presence data (0,1).
#' @param predictors character. Vector with the column names of quantitative
#' predictor variables (i.e. continuous variables).
#' Usage predictors = c("aet", "cwd", "tmin")
#' @param predictors_f character. Vector with the column names of qualitative
#' predictor variables (i.e. ordinal or nominal variables type). Usage predictors_f = c("landform")
#' @param partition character. Column name with training and validation partition groups.
#' @param background data.frame. Database with response column only with 0 and predictors variables. All
#' column names must be consistent with data
#' @param thr character. Threshold used to get binary suitability values (i.e. 0,1), useful for threshold-dependent performance metrics. It is possible to use more than one threshold type. A vector must be provided for this argument. The following threshold criteria are available:
#' \itemize{
#'   \item lpt: The highest threshold at which there is no omission.
#'   \item equal_sens_spec: Threshold at which the sensitivity and specificity are equal.
#'   \item max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS).
#'   \item max_jaccard: The threshold at which the Jaccard index is the highest.
#'   \item max_sorensen: The threshold at which the Sorensen index is the highest.
#'   \item max_fpb: The threshold at which FPB (F-measure on presence-background data) is highest.
#'   \item sensitivity: Threshold based on a specified sensitivity value.
#'   Usage thr = c('sensitivity', sens='0.6') or thr = c('sensitivity'). 'sens' refers to sensitivity value. If a sensitivity value is not specified, the default used is 0.9.
#'   }
#' If more than one threshold type is used they must be concatenated, e.g., thr=c('lpt', 'max_sens_spec', 'max_jaccard'), or thr=c('lpt', 'max_sens_spec', 'sensitivity', sens='0.8'), or thr=c('lpt', 'max_sens_spec', 'sensitivity'). Function will use all threshold criteria if none is specified.
#'
#' @return
#'
#' A list object with:
#' \itemize{
#' \item model: A "graf" class object. This object can be used for predicting.
#' \item predictors: A tibble with quantitative (c column names) and qualitative (f column names) variables use for modeling.
#' \item performance: Performance metrics (see \code{\link{sdm_eval}}).
#' Threshold dependent metrics are calculated based on the threshold criteria specified in the argument.
#' \item data_ens: Predicted suitability for each test partition. This database is used in \code{\link{fit_ensemble}}
#' }
#'
#' @seealso \code{\link{fit_gam}}, \code{\link{fit_glm}}, \code{\link{fit_gbm}},
#' \code{\link{fit_max}}, \code{\link{fit_net}}, \code{\link{fit_raf}}, and \code{\link{fit_svm}}.
#'
#' @export
#'
#' @importFrom dplyr %>% select all_of starts_with filter pull bind_rows mutate tibble group_by summarise across relocate left_join
#' @importFrom stats sd dist dnorm pnorm qnorm
#'
#' @examples
#' \dontrun{
#' data("abies")
#'
#' # Using k-fold partition method
#' abies2 <- part_random(
#'   data = abies,
#'   pr_ab = "pr_ab",
#'   method = c(method = "kfold", folds = 3)
#' )
#' abies2
#'
#' bg <- abies2
#' bg$pr_ab <- 0
#'
#'
#' gaup_t1 <- fit_gau(
#'   data = abies2,
#'   response = "pr_ab",
#'   predictors = c("aet", "ppt_jja", "pH", "awc", "depth"),
#'   predictors_f = c("landform"),
#'   partition = ".part",
#'   background = bg,
#'   thr = c("max_sens_spec", "equal_sens_spec", "max_sorensen")
#' )
#'
#' gaup_t1$model
#' gaup_t1$predictors
#' gaup_t1$performance
#' gaup_t1$data_ens
#'
#' # Using bootstrap partition method only with presence-absence
#' abies2 <- part_random(
#'   data = abies,
#'   pr_ab = "pr_ab",
#'   method = c(method = "boot", replicates = 5, proportion = 0.7)
#' )
#' abies2
#'
#' gaup_t2 <- fit_gau(
#'   data = abies2,
#'   response = "pr_ab",
#'   predictors = c("ppt_jja", "pH", "awc"),
#'   predictors_f = c("landform"),
#'   partition = ".part",
#'   thr = c(type = c("lpt", "max_sens_spec", "sensitivity"), sens = "0.8")
#' )
#' gaup_t2
#' }
fit_gau <- function(data,
                    response,
                    predictors,
                    predictors_f = NULL,
                    background = NULL,
                    partition,
                    thr = NULL) {
  . <- model <- TPR <- IMAE <- rnames <- thr_value <- n_presences <- n_absences <- NULL
  variables <- dplyr::bind_rows(c(c = predictors, f = predictors_f))

  data <- data.frame(data)
  if (!is.null(background)) background <- data.frame(background)

  if (is.null(predictors_f)) {
    data <- data %>%
      dplyr::select(dplyr::all_of(response), dplyr::all_of(predictors), dplyr::starts_with(partition))
    if (!is.null(background)) {
      background <- background %>%
        dplyr::select(dplyr::all_of(response), dplyr::all_of(predictors), dplyr::starts_with(partition))
    }
  } else {
    data <- data %>%
      dplyr::select(dplyr::all_of(response), dplyr::all_of(predictors), dplyr::all_of(predictors_f), dplyr::starts_with(partition))
    data <- data.frame(data)
    for (i in predictors_f) {
      data[, i] <- as.factor(data[, i])
    }
    if (!is.null(background)) {
      background <- background %>%
        dplyr::select(dplyr::all_of(response), dplyr::all_of(predictors), dplyr::all_of(predictors_f), dplyr::starts_with(partition))
      for (i in predictors_f) {
        background[, i] <- as.factor(background[, i])
      }
    }
  }

  if (!is.null(background)) {
    if (!all(table(c(names(background), names(data))) == 2)) {
      stop("Column names of database used in 'data' and 'background' arguments do not match")
    }
  }

  # Remove NAs
  complete_vec <- stats::complete.cases(data[, c(response, unlist(variables))])
  if (sum(!complete_vec) > 0) {
    message(sum(!complete_vec), " rows were excluded from database because NAs were found")
    data <- data %>% dplyr::filter(complete_vec)
  }
  rm(complete_vec)
  if (!is.null(background)) {
    complete_vec <- stats::complete.cases(background[, c(response, unlist(variables))])
    if (sum(!complete_vec) > 0) {
      message(sum(!complete_vec), " rows were excluded from database because NAs were found")
      background <- background %>% dplyr::filter(complete_vec)
    }
    rm(complete_vec)
  }


  # Compare pr_ab and background column names
  p_names <- names(data %>% dplyr::select(dplyr::starts_with(partition)))
  for (i in p_names) {
    if (!is.null(background)) {
      Npart_p <- data %>%
        dplyr::filter(!!as.symbol(response) == 1) %>%
        dplyr::pull({{ i }}) %>%
        unique() %>%
        sort()
      Npart_bg <- background %>%
        dplyr::filter(!!as.symbol(response) == 0) %>%
        dplyr::pull({{ i }}) %>%
        unique() %>%
        sort()
      if (!all(table(c(Npart_p, Npart_bg)) == 2)) {
        stop(
          paste(
            "Partition groups between presences and background do not match:\n",
            paste("Part. group presences:", paste(Npart_p, collapse = " "), "\n"),
            paste("Part. group background:", paste(Npart_bg, collapse = " "), "\n")
          )
        )
      }
    }
  }
  rm(i)


  # Fit models
  np <- ncol(data %>% dplyr::select(dplyr::starts_with(partition)))
  p_names <- names(data %>% dplyr::select(dplyr::starts_with(partition)))
  eval_partial_list <- list()
  pred_test_ens <- data %>%
    dplyr::select(dplyr::starts_with(partition)) %>%
    apply(., 2, unique) %>%
    data.frame() %>%
    as.list() %>%
    lapply(., function(x) {
      x <- stats::na.exclude(x)
      x[!(x %in% c("train-test", "test"))] %>% as.list()
    })

  for (h in 1:np) {
    message("Replica number: ", h, "/", np)

    out <- pre_tr_te(data, p_names, h)
    train <- out$train
    test <- out$test
    np2 <- out$np2
    rm(out)

    eval_partial <- as.list(rep(NA, np2))
    pred_test <- list()
    mod <- list()

    # In the follow code function will substitutes absences by background points
    # only in train database in order to fit gau with presences and background
    # and validate models with presences and absences
    if (!is.null(background)) {
      background2 <- pre_tr_te(background, p_names, h)
      train <- lapply(train, function(x) x[x[, response] == 1, ])
      train <- mapply(dplyr::bind_rows, train, background2$train, SIMPLIFY = FALSE)
      bgt_test <- background2$test
      rm(background2)
    }

    for (i in 1:np2) {
      tryCatch({
        message("Partition number: ", i, "/", np2)
        set.seed(1)
        mod[[i]] <-
          graf(
            y = train[[i]][, response],
            x = train[[i]][, c(predictors, predictors_f)],
            opt.l = FALSE,
            method = "Laplace"
          )

        # Predict for presences absences data
        ## Eliminate factor levels not used in fitting
        if (!is.null(predictors_f)) {
          for (fi in 1:length(predictors_f)) {
            lev <- train[[i]][, predictors_f[fi]] %>%
              unique() %>%
              as.character()
            lev_filt <- test[[i]][, predictors_f[fi]] %in% lev
            test[[i]] <- test[[i]][lev_filt, ]
            if (!is.null(background)) {
              lev_filt <- bgt_test[[i]][, predictors_f[fi]] %in% lev
              bgt_test[[i]] <- bgt_test[[i]][lev_filt, ]
            }
          }
        }

        # Predict for presences absences data
        pred_test <- data.frame(
          pr_ab = test[[i]][, response],
          pred =
            suppressWarnings(
              predict.graf(
                mod[[i]],
                newdata = test[[i]][c(predictors, predictors_f)],
                type = "response",
                CI = NULL
              )[, 1]
            )
        )

        pred_test_ens[[h]][[i]] <- pred_test %>%
          dplyr::mutate(rnames = rownames(test[[i]]))

        # Predict for background data
        if (!is.null(background)) {
          bgt <-
            data.frame(
              pr_ab = bgt_test[[i]][, response],
              pred = suppressWarnings(
                predict.graf(
                  mod[[i]],
                  newdata = bgt_test[[i]][c(predictors, predictors_f)],
                  type = "response",
                  CI = NULL
                )[, 1]
              )
            )
        }

        # Validation of model
        if (is.null(background)) {
          eval <-
            sdm_eval(
              p = pred_test$pred[pred_test$pr_ab == 1],
              a = pred_test$pred[pred_test$pr_ab == 0],
              thr = thr
            )
        } else {
          eval <-
            sdm_eval(
              p = pred_test$pred[pred_test$pr_ab == 1],
              a = pred_test$pred[pred_test$pr_ab == 0],
              thr = thr,
              bg = bgt$pred
            )
        }
        eval_partial[[i]] <- dplyr::tibble(model = "gau", eval)
      })
    }

    # Create final database with parameter performance
    names(eval_partial) <- 1:np2
    eval_partial <-
      eval_partial[sapply(eval_partial, function(x) !is.null(dim(x)))] %>%
      dplyr::bind_rows(., .id = "partition")
    eval_partial_list[[h]] <- eval_partial
  }

  eval_partial <- eval_partial_list %>%
    dplyr::bind_rows(., .id = "replica")

  eval_final <- eval_partial %>%
    dplyr::group_by(model, threshold) %>%
    dplyr::summarise(dplyr::across(
      TPR:IMAE,
      list(mean = mean, sd = stats::sd)
    ), .groups = "drop")

  # Bind data for ensemble
  pred_test_ens <-
    lapply(pred_test_ens, function(x) {
      dplyr::bind_rows(x, .id = "part")
    }) %>%
    dplyr::bind_rows(., .id = "replicates") %>%
    dplyr::tibble() %>%
    dplyr::relocate(rnames)

  # Fit final models with best settings
  set.seed(1)
  suppressMessages(mod <-
    graf(
      y = data[, response],
      x = data[, c(predictors, predictors_f)],
      opt.l = FALSE,
      method = "Laplace"
    ))

  suppressWarnings(pred_test <- data.frame(
    pr_ab = data.frame(data)[, response],
    pred = suppressMessages(
      predict.graf(
        mod,
        newdata = data[, c(predictors, predictors_f)],
        type = "response",
        CI = NULL
      )[, 1]
    )
  ))

  if (is.null(background)) {
    threshold <- sdm_eval(
      p = pred_test$pred[pred_test$pr_ab == 1],
      a = pred_test$pred[pred_test$pr_ab == 0],
      thr = thr
    )
  } else {
    background <- suppressWarnings(predict.graf(
      mod,
      newdata = background[c(predictors, predictors_f)],
      type = "response",
      CI = NULL
    )[, 1])
    threshold <- sdm_eval(
      p = pred_test$pred[pred_test$pr_ab == 1],
      a = pred_test$pred[pred_test$pr_ab == 0],
      thr = thr,
      bg = background
    )
  }

  result <- list(
    model = mod,
    predictors = variables,
    performance = dplyr::left_join(eval_final, threshold[1:4], by = "threshold") %>% dplyr::relocate(model, threshold, thr_value, n_presences, n_absences),
    data_ens = pred_test_ens
  )
  return(result)
}