sdm_predict: Spatial predictions from individual and ensemble models

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

Spatial predictions from individual and ensemble models

Description

This function allows the geographical prediction of one or more models constructed with the fit_ or tune_ function set, models fitted with esm_ function set (i.e., ensemble of small models approach), or models constructed with fit_ensemble function. It can return continuous or continuous and binary predictions for one or more thresholds

Usage

sdm_predict(
  models,
  pred,
  nchunk = 1,
  thr = NULL,
  con_thr = FALSE,
  predict_area = NULL,
  clamp = TRUE,
  pred_type = "cloglog"
)

Arguments

models	list of one or more models fitted with fit_ or tune_ functions. In case use models fitted with fit_ensemble or esm_ family function only one model could be used. Usage models = mglm or models = list(mglm, mraf, mgbm)
pred	SpatRaster. Raster layer with predictor variables. Names of layers must exactly match those used in model fitting.
nchunk	integer. Number of chunks to split data used to predict models (i.e., SpatRaster used in pred argument). Predicting models in chunks helps reduce memory requirements in cases where models are predicted for large scales and high resolution. Default = 1
thr	character. Threshold used to get binary suitability values (i.e., 0,1). It is possible to use more than one threshold type. It is mandatory to use the same threshold/s used to fit the models. The following threshold types are available: lpt: The highest threshold at which there is no omission. equal_sens_spec: Threshold at which the sensitivity and specificity are equal. max_sens_spec: Threshold at which the sum of the sensitivity and specificity is the highest (aka threshold that maximizes the TSS). max_jaccard: The threshold at which the Jaccard index is the highest. max_sorensen: The threshold at which the Sorensen index is highest. max_fpb: The threshold at which FPB is highest. sensitivity: Threshold based on a specified sensitivity value used to fit the models. all: All the threshold used in the model outputs used in 'models' argument will be used. Usage thr = c('lpt', 'max_sens_spec', 'max_jaccard'), thr=c('lpt', 'max_sens_spec', 'sensitivity'), or thr='all'. If no threshold is specified (i.e., thr = NULL) function will return continuous prediction only. Default NULL
con_thr	logical. If true predictions with suitability values above threshold/s will be returned. Default = FALSE
predict_area	SpatVector, SpatialPolygon, or SpatialPolygonDataFrame. Spatial polygon used for restring prediction into only a given region. Default = NULL
clamp	logical. It is set with TRUE, predictors and features are restricted to the range seen during model training. Only valid for Maxent model (see tune_mx and fit_mx). Default TRUE.
pred_type	character. Type of response required available "link", "exponential", "cloglog" and "logistic". Only valid for Maxent model (see tune_mx and fit_mx). Default "cloglog".

Value

A list of SpatRaster with continuous and/or binary predictions

See Also

fit_ensemble

Examples

## Not run: 
require(dplyr)
require(terra)

data("spp")
somevar <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(somevar)

# Extract data
some_sp <- spp %>%
  filter(species == "sp3")

some_sp <-
  sdm_extract(
    data = some_sp,
    x = "x",
    y = "y",
    env_layer = somevar
  )

# Partition
some_sp <- part_random(
  data = some_sp,
  pr_ab = "pr_ab",
  method = c(method = "rep_kfold", folds = 3, replicates = 5)
)


## %######################################################%##
#                                                          #
####          Create different type of models           ####
#                                                          #
## %######################################################%##
# Fit some models
mglm <- fit_glm(
  data = some_sp,
  response = "pr_ab",
  predictors = c("CFP_1", "CFP_2", "CFP_3", "CFP_4"),
  partition = ".part",
  poly = 2
)
mraf <- fit_raf(
  data = some_sp,
  response = "pr_ab",
  predictors = c("CFP_1", "CFP_2", "CFP_3", "CFP_4"),
  partition = ".part",
)
mgbm <- fit_gbm(
  data = some_sp,
  response = "pr_ab",
  predictors = c("CFP_1", "CFP_2", "CFP_3", "CFP_4"),
  partition = ".part"
)

# Fit an ensemble model
mensemble <- fit_ensemble(
  models = list(mglm, mraf, mgbm),
  ens_method = "meansup",
  thr = NULL,
  thr_model = "max_sens_spec",
  metric = "TSS"
)

# Fit a model with the Ensembles of Small Models approach
# Without threshold specification and with kfold
msmall <- esm_gam(
  data = some_sp,
  response = "pr_ab",
  predictors = c("CFP_1", "CFP_2", "CFP_3", "CFP_4"),
  partition = ".part",
  thr = NULL
)


## %######################################################%##
#                                                           #
####      Predict different kind of models               ####
#                                                           #
## %######################################################%##

# sdm_predict can be used for predict one or more models fitted with fit_ or tune_ functions

# a single model
ind_p <- sdm_predict(
  models = mglm,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# a list of models
list_p <- sdm_predict(
  models = list(mglm, mraf, mgbm),
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# Predict an ensemble model
# (only is possilbe use one fit_ensemble)
ensemble_p <- sdm_predict(
  models = mensemble,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

# Predict an ensemble of small models
# (only is possible to use one ensemble of small models)
small_p <- sdm_predict(
  models = msmall,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL
)

## %######################################################%##
#                                                          #
####              Predict model using chunks            ####
#                                                          #
## %######################################################%##
# Predicting models in chunks helps reduce memory requirements in
# cases where models are predicted for large scales and high resolution

ind_p <- sdm_predict(
  models = mglm,
  pred = somevar,
  thr = "max_fpb",
  con_thr = FALSE,
  predict_area = NULL,
  nchunk = 4
)

## End(Not run)

sjevelazco/flexsdm documentation built on Feb. 28, 2025, 9:07 a.m.