biomod2 (version 4.2-5)

BIOMOD_EnsembleModeling: Create and evaluate an ensemble set of models and predictions

Description

This function allows to combine a range of models built with the BIOMOD_Modeling function in one (or several) ensemble model. Modeling uncertainty can be assessed as well as variables importance, ensemble predictions can be evaluated against original data, and created ensemble models can be projected over new conditions (see Details).

Usage

BIOMOD_EnsembleModeling(
  bm.mod,
  models.chosen = "all",
  em.by = "PA+run",
  em.algo,
  metric.select = "all",
  metric.select.thresh = NULL,
  metric.select.table = NULL,
  metric.select.dataset = NULL,
  metric.eval = c("KAPPA", "TSS", "ROC"),
  var.import = 0,
  EMci.alpha = 0.05,
  EMwmean.decay = "proportional",
  nb.cpu = 1,
  seed.val = NULL,
  do.progress = TRUE,
  prob.mean,
  prob.median,
  prob.cv,
  prob.ci,
  committee.averaging,
  prob.mean.weight,
  prob.mean.weight.decay,
  prob.ci.alpha
)

Value

A BIOMOD.ensemble.models.out object containing models outputs, or links to saved outputs.
Models outputs are stored out of R (for memory storage reasons) in 2 different folders created in the current working directory :

1. a models folder, named after the resp.name argument of BIOMOD_FormatingData, and containing all ensemble models

2. a hidden folder, named .BIOMOD_DATA, and containing outputs related files (original dataset, calibration lines, pseudo-absences selected, predictions, variables importance, evaluation values...), that can be retrieved with get_[...] or load functions, and used by other biomod2 functions, like BIOMOD_EnsembleForecasting

Arguments

bm.mod

a BIOMOD.models.out object returned by the BIOMOD_Modeling function

models.chosen

a vector containing model names to be kept, must be either all or a sub-selection of model names that can be obtained with the get_built_models function

em.by

a character corresponding to the way kept models will be combined to build the ensemble models, must be among all, algo, PA, PA+algo, PA+run

em.algo

a vector corresponding to the ensemble models that will be computed, must be among 'EMmean', 'EMmedian', 'EMcv', 'EMci', 'EMca', 'EMwmean'

metric.select

a vector containing evaluation metric names to be used together with metric.select.thresh to exclude single models based on their evaluation scores (for ensemble methods like probability weighted mean or committee averaging). Must be among all (same evaluation metrics than those of bm.mod), user.defined (and defined through metric.select.table) or ROC, TSS, KAPPA, ACCURACY, BIAS, POD, FAR, POFD, SR, CSI, ETS, HK, HSS, OR, ORSS

metric.select.thresh

(optional, default NULL)
A vector of numeric values corresponding to the minimum scores (one for each metric.select) below which single models will be excluded from the ensemble model building

metric.select.table

(optional, default NULL)
If metric.select = 'user.defined', a data.frame containing evaluation scores calculated for each single models and that will be compared to metric.select.thresh values to exclude some of them from the ensemble model building, with metric.select rownames, and models.chosen colnames

metric.select.dataset

(optional, default 'validation' if possible). A character determining which dataset should be used to filter and/or weigh the ensemble models should be among 'evaluation', 'validation' or 'calibration'.

metric.eval

a vector containing evaluation metric names to be used, must be among ROC, TSS, KAPPA, ACCURACY, BIAS, POD, FAR, POFD, SR, CSI, ETS, HK, HSS, OR, ORSS

var.import

(optional, default NULL)
An integer corresponding to the number of permutations to be done for each variable to estimate variable importance

EMci.alpha

(optional, default 0.05)
A numeric value corresponding to the significance level to estimate confidence interval

EMwmean.decay

(optional, default proportional)
A value defining the relative importance of the weights (if 'EMwmean' was given to argument em.algo). A high value will strongly discriminate good models from the bad ones (see Details), while proportional will attribute weights proportionally to the models evaluation scores

nb.cpu

(optional, default 1)
An integer value corresponding to the number of computing resources to be used to parallelize the single models predictions and the ensemble models computation

seed.val

(optional, default NULL)
An integer value corresponding to the new seed value to be set

do.progress

(optional, default TRUE)
A logical value defining whether the progress bar is to be rendered or not

prob.mean

(deprecated, please use em.algo instead)
A logical value defining whether to compute the mean probabilities across predictions or not

prob.median

(deprecated, please use em.algo instead)
A logical value defining whether to compute the median probabilities across predictions or not

prob.cv

(deprecated, please use em.algo instead)
A logical value defining whether to compute the coefficient of variation across predictions or not

prob.ci

(deprecated, please use em.algo instead)
A logical value defining whether to compute the confidence interval around the prob.mean ensemble model or not

committee.averaging

(deprecated, please use em.algo instead)
A logical value defining whether to compute the committee averaging across predictions or not

prob.mean.weight

(deprecated, please use em.algo instead)
A logical value defining whether to compute the weighted sum of probabilities across predictions or not

prob.mean.weight.decay

(deprecated, please use EMwmean.decay instead)
old argument name for EMwmean.decay

prob.ci.alpha

(deprecated, please use EMci.alpha instead)
old argument name for EMci.alpha

Author

Wilfried Thuiller, Damien Georges, Robin Engler

Details

Models sub-selection (models.chosen)

Applying get_built_models function to the bm.mod object gives the names of the single models created with the BIOMOD_Modeling function. The models.chosen argument can take either a sub-selection of these single model names, or the all default value, to decide which single models will be used for the ensemble model building.

Models assembly rules (em.by)

Single models built with the BIOMOD_Modeling function can be combined in 5 different ways to obtain ensemble models :

• PA+run : each combination of pseudo-absence and repetition datasets is done, merging algorithms together

• PA+algo : each combination of pseudo-absence and algorithm datasets is done, merging repetitions together

• PA : pseudo-absence datasets are considered individually, merging algorithms and repetitions together

• algo : algorithm datasets are considered individually, merging pseudo-absence and repetitions together

• all : all models are combined into one

Hence, depending on the chosen method, the number of ensemble models built will vary.
Be aware that if no evaluation data was given to the BIOMOD_FormatingData function, some ensemble model evaluations may be biased due to difference in data used for single model evaluations. Be aware that all of these combinations are allowed, but some may not make sense depending mainly on how pseudo-absence datasets have been built and whether all of them have been used for all single models or not (see PA.nb.absences and models.pa parameters in BIOMOD_FormatingData and BIOMOD_Modeling functions respectively).

Evaluation metrics

• metric.select : the selected metrics must be chosen among the ones used within the BIOMOD_Modeling function to build the model.output object, unless metric.select = 'user.defined' and therefore values will be provided through the metric.select.table parameter.
  In the case of the selection of several metrics, they will be used at different steps of the ensemble modeling function :

  1. remove low quality single models, having a score lower than metric.select.thresh

  2. perform the binary transformation needed if 'EMca' was given to argument em.algo

  3. weight models if 'EMwmean' was given to argument em.algo

• metric.select.thresh : as many values as evaluation metrics selected with the metric.select parameter, and defining the corresponding quality thresholds below which the single models will be excluded from the ensemble model building.

• metric.select.table : a data.frame must be given if metric.select = 'user.defined' to allow the use of evaluation metrics other than those calculated within biomod2. The data.frame must contain as many columns as models.chosen with matching names, and as many rows as evaluation metrics to be used. The number of rows must match the length of the metric.select.thresh parameter. The values contained in the data.frame will be compared to those defined in metric.select.thresh to remove low quality single models from the ensemble model building.

• metric.select.dataset : a character determining the dataset which evaluation metric should be used to filter and/or weigh the ensemble models. Should be among evaluation, validation or calibration. By default BIOMOD_EnsembleModeling will use the validation dataset unless no validation is available in which case calibration dataset are used.

• metric.eval : the selected metrics will be used to validate/evaluate the ensemble models built

Ensemble-models algorithms

The set of models to be calibrated on the data.
6 modeling techniques are currently available :

• EMmean : Mean of probabilities over the selected models. Old name: prob.mean

• EMmedian : Median of probabilities over the selected models
  The median is less sensitive to outliers than the mean, however it requires more computation time and memory as it loads all predictions (on the contrary to the mean or the weighted mean). Old name: prob.median

• EMcv : Coefficient of variation (sd / mean) of probabilities over the selected models
  This model is not scaled. It will be evaluated like all other ensemble models although its interpretation will be obviously different. CV is a measure of uncertainty rather a measure of probability of occurrence. If the CV gets a high evaluation score, it means that the uncertainty is high where the species is observed (which might not be a good feature of the model). The lower is the score, the better are the models. CV is a nice complement to the mean probability. Old name: prob.cv

• EMci & EMci.alpha : Confidence interval around the mean of probabilities of the selected models
  It is also a nice complement to the mean probability. It creates 2 ensemble models :

  • LOWER : there is less than 100 * EMci.alpha / 2 % of chance to get probabilities lower than the given ones

  • UPPER : there is less than 100 * EMci.alpha / 2 % of chance to get probabilities upper than the given ones

  These intervals are calculated with the following function : $$I_c = [ \bar{x} - \frac{t_\alpha sd }{ \sqrt{n} }; \bar{x} + \frac{t_\alpha sd }{ \sqrt{n} }]$$
  Old parameter name: prob.ci & prob.ci.alpha

• EMca : Probabilities from the selected models are first transformed into binary data according to the thresholds defined when building the model.output object with the BIOMOD_Modeling function, maximizing the evaluation metric score over the testing dataset. The committee averaging score is obtained by taking the average of these binary predictions. It is built on the analogy of a simple vote :

  • each single model votes for the species being either present (1) or absent (0)

  • the sum of 1 is then divided by the number of single models voting

  The interesting feature of this measure is that it gives both a prediction and a measure of uncertainty. When the prediction is close to 0 or 1, it means that all models agree to predict 0 or 1 respectively. When the prediction is around 0.5, it means that half the models predict 1 and the other half 0.
  Old parameter name: committee.averaging

• EMwmean & EMwmean.decay : Probabilities from the selected models are weighted according to their evaluation scores obtained when building the model.output object with the BIOMOD_Modeling function (better a model is, more importance it has in the ensemble) and summed.
  Old parameter name: prob.mean.weight & prob.mean.weight.decay

The EMwmean.decay is the ratio between a weight and the next or previous one. The formula is : W = W(-1) * EMwmean.decay. For example, with the value of 1.6 and 4 weights wanted, the relative importance of the weights will be 1/1.6/2.56(=1.6*1.6)/4.096(=2.56*1.6) from the weakest to the strongest, and gives 0.11/0.17/0.275/0.445 considering that the sum of the weights is equal to one. The lower the EMwmean.decay, the smoother the differences between the weights enhancing a weak discrimination between models.

If EMwmean.decay = 'proportional', the weights are assigned to each model proportionally to their evaluation scores. The discrimination is fairer than using the decay method where close scores can have strongly diverging weights, while the proportional method would assign them similar weights.

It is also possible to define the EMwmean.decay parameter as a function that will be applied to single models scores and transform them into weights. For example, if EMwmean.decay = function(x) {x^2}, the squared of evaluation score of each model will be used to weight the models predictions.

See Also

BIOMOD_FormatingData, bm_ModelingOptions, bm_CrossValidation, bm_VariablesImportance, BIOMOD_Modeling, BIOMOD_EnsembleForecasting, bm_PlotEvalMean, bm_PlotEvalBoxplot, bm_PlotVarImpBoxplot, bm_PlotResponseCurves

Other Main functions: BIOMOD_EnsembleForecasting(), BIOMOD_FormatingData(), BIOMOD_LoadModels(), BIOMOD_Modeling(), BIOMOD_Projection(), BIOMOD_RangeSize()

Examples

library(terra)
# Load species occurrences (6 species available)
data(DataSpecies)
head(DataSpecies)

# Select the name of the studied species
myRespName <- 'GuloGulo'

# Get corresponding presence/absence data
myResp <- as.numeric(DataSpecies[, myRespName])

# Get corresponding XY coordinates
myRespXY <- DataSpecies[, c('X_WGS84', 'Y_WGS84')]

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
data(bioclim_current)
myExpl <- terra::rast(bioclim_current)

# \dontshow{
myExtent <- terra::ext(0,30,45,70)
myExpl <- terra::crop(myExpl, myExtent)
# }

## ----------------------------------------------------------------------- #
file.out <- paste0(myRespName, "/", myRespName, ".AllModels.models.out")
if (file.exists(file.out)) {
  myBiomodModelOut <- get(load(file.out))
} else {

  # Format Data with true absences
  myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                       expl.var = myExpl,
                                       resp.xy = myRespXY,
                                       resp.name = myRespName)

  # Model single models
  myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                      modeling.id = 'AllModels',
                                      models = c('RF', 'GLM'),
                                      CV.strategy = 'random',
                                      CV.nb.rep = 2,
                                      CV.perc = 0.8,
                                      OPT.strategy = 'bigboss',
                                      metric.eval = c('TSS','ROC'),
                                      var.import = 3,
                                      seed.val = 42)
}

## ----------------------------------------------------------------------- #
# Model ensemble models
myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
                                      models.chosen = 'all',
                                      em.by = 'all',
                                      em.algo = c('EMmean', 'EMca'),
                                      metric.select = c('TSS'),
                                      metric.select.thresh = c(0.7),
                                      metric.eval = c('TSS', 'ROC'),
                                      var.import = 3,
                                      seed.val = 42)
myBiomodEM

# Get evaluation scores & variables importance
get_evaluations(myBiomodEM)
get_variables_importance(myBiomodEM)

# Represent evaluation scores
bm_PlotEvalMean(bm.out = myBiomodEM, dataset = 'calibration')
bm_PlotEvalBoxplot(bm.out = myBiomodEM, group.by = c('algo', 'algo'))

# # Represent variables importance
# bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'algo', 'algo'))
# bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'algo', 'merged.by.PA'))
# bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('algo', 'expl.var', 'merged.by.PA'))

# # Represent response curves
# bm_PlotResponseCurves(bm.out = myBiomodEM, 
#                       models.chosen = get_built_models(myBiomodEM),
#                       fixed.var = 'median')
# bm_PlotResponseCurves(bm.out = myBiomodEM, 
#                       models.chosen = get_built_models(myBiomodEM),
#                       fixed.var = 'min')
# bm_PlotResponseCurves(bm.out = myBiomodEM, 
#                       models.chosen = get_built_models(myBiomodEM, algo = 'EMmean'),
#                       fixed.var = 'median',
#                       do.bivariate = TRUE)