ensemble_modelling: Build an ensemble SDM that assembles multiple algorithms

In hugocalcad/LigthSSDM: Optimized SSDM Package for large dataset

Description

Build an ensemble SDM that assembles multiple algorithms for a single species. The function takes as inputs an occurrence data frame made of presence/absence or presence-only records and a raster object for data extraction and projection. The function returns an S4 Ensemble.SDM class object containing the habitat suitability map, the binary map, the between-algorithm variance map and the associated evaluation tables (model evaluation, algorithm evaluation, algorithm correlation matrix and variable importance).

Usage

ensemble_modelling(algorithms, Occurrences, Env, Xcol = "Longitude",
  Ycol = "Latitude", Pcol = NULL, rep = 10, name = NULL,
  save = FALSE, path = getwd(), PA = NULL, cv = "holdout",
  cv.param = c(0.7, 1), thresh = 1001, metric = "SES",
  axes.metric = "Pearson", uncertainty = TRUE, tmp = FALSE,
  ensemble.metric = c("AUC"), ensemble.thresh = c(0.75),
  weight = TRUE, verbose = TRUE, GUI = FALSE, folder_tmp = NULL, ...)

Arguments

algorithms	character. A character vector specifying the algorithm name(s) to be run (see details below).
Occurrences	data frame. Occurrences table (can be processed first by load_occ).
Env	raster object. RasterStack object of environmental variables (can be processed first by load_var).
Xcol	character. Name of the column in the occurrence table containing Latitude or X coordinates.
Ycol	character. Name of the column in the occurrence table containing Longitude or Y coordinates.
Pcol	character. Name of the column in the occurrence table specifying whether a line is a presence or an absence. A value of 1 is presence and value of 0 is absence. If NULL presence-only dataset is assumed.
rep	integer. Number of repetitions for each algorithm.
name	character. Optional name given to the final Ensemble.SDM produced (by default 'Ensemble.SDM').
save	logical. If TRUE, the ensemble SDM is automatically saved.
path	character. If save is If TRUE, the path to the directory in which the ensemble SDM will be saved.
PA	list(nb, strat) defining the pseudo-absence selection strategy used in case of presence-only dataset. If PA is NULL, recommended PA selection strategy is used depending on the algorithm (see details below).
cv	character. Method of cross-validation used to evaluate the ensemble SDM (see details below).
cv.param	numeric. Parameters associated to the method of cross-validation used to evaluate the ensemble SDM (see details below).
thresh	numeric. A single integer value representing the number of equal interval threshold values between 0 and 1 (see optim.thresh).
metric	character. Metric used to compute the binary map threshold (see details below.)
axes.metric	Metric used to evaluate variable relative importance (see details below).
uncertainty	logical. If TRUE, generates an uncertainty map and an algorithm correlation matrix.
tmp	logical. If set to true, the habitat suitability map of each algorithm is saved in a temporary file to release memory. But beware: if you close R, temporary files will be deleted To avoid any loss you can save your ensemble SDM with save.model. Depending on number, resolution and extent of models, temporary files can take a lot of disk space. Temporary files are written in R environment temporary folder.
ensemble.metric	character. Metric(s) used to select the best SDMs that will be included in the ensemble SDM (see details below).
ensemble.thresh	numeric. Threshold(s) associated with the metric(s) used to compute the selection.
weight	logical. If TRUE, SDMs are weighted using the ensemble metric or, alternatively, the mean of the selection metrics.
verbose	logical. If TRUE, allows the function to print text in the console.
GUI	logical. Do not take this argument into account (parameter for the user interface).
folder_tmp	character. Carpeta temporal para el env.
...	additional parameters for the algorithm modelling function (see details below).

Details

algorithms

'all' calls all the following algorithms. Algorithms include Generalized linear model (GLM), Generalized additive model (GAM), Multivariate adaptive regression splines (MARS), Generalized boosted regressions model (GBM), Classification tree analysis (CTA), Random forest (RF), Maximum entropy (MAXENT), Artificial neural network (ANN), and Support vector machines (SVM). Each algorithm has its own parameters settable with the ... (see each algorithm section below to set their parameters).

"PA"

list with two values: nb number of pseudo-absences selected, and strat strategy used to select pseudo-absences: either random selection or disk selection. We set default recommendation from Barbet-Massin et al. (2012) (see reference).

cv

Cross-validation method used to split the occurrence dataset used for evaluation: holdout data are partitioned into a training set and an evaluation set using a fraction (cv.param[1]) and the operation can be repeated (cv.param[2]) times, k-fold data are partitioned into k (cv.param[1]) folds being k-1 times in the training set and once the evaluation set and the operation can be repeated (cv.param[2]) times, LOO (Leave One Out) each point is successively taken as evaluation data.

metric

Choice of the metric used to compute the binary map threshold and the confusion matrix (by default SES as recommended by Liu et al. (2005), see reference below): Kappa maximizes the Kappa, CCR maximizes the proportion of correctly predicted observations, TSS (True Skill Statistic) maximizes the sum of sensitivity and specificity, SES uses the sensitivity-specificity equality, LW uses the lowest occurrence prediction probability, ROC minimizes the distance between the ROC plot (receiving operating characteristic curve) and the upper left corner (1,1).

axes.metric

Metric used to evaluate the variable relative importance (difference between a full model and one with each variable successively omitted): Pearson (computes a simple Pearson's correlation r between predictions of the full model and the one without a variable, and returns the score 1-r: the highest the value, the more influence the variable has on the model), AUC, Kappa, sensitivity, specificity, and prop.correct (proportion of correctly predicted occurrences).

ensemble.metric

Ensemble metric(s) used to select SDMs: AUC, Kappa, sensitivity, specificity, and prop.correct (proportion of correctly predicted occurrences).

"..."

See algorithm in detail section

Value

an S4 Ensemble.SDM class object viewable with the plot.model function.

Generalized linear model (GLM)

Uses the glm function from the package 'stats', you can set the following parameters (see glm for more details):

test

character. Test used to evaluate the SDM, default 'AIC'.

epsilon

numeric. Positive convergence tolerance eps ; the iterations converge when |dev - dev_old|/(|dev| + 0.1) < eps. By default, set to 10e-08.

maxit

numeric. Integer giving the maximal number of IWLS (Iterative Weighted Last Squares) iterations, default 500.

Generalized additive model (GAM)

Uses the gam function from the package 'mgcv', you can set the following parameters (see gam for more details):

test

character. Test used to evaluate the model, default 'AIC'.

epsilon

numeric. This is used for judging conversion of the GLM IRLS (Iteratively Reweighted Least Squares) loop, default 10e-08.

maxit

numeric. Maximum number of IRLS iterations to perform, default 500.

Multivariate adaptive regression splines (MARS)

Uses the earth function from the package 'earth', you can set the following parameters (see earth for more details):

degree

integer. Maximum degree of interaction (Friedman's mi) ; 1 meaning build an additive model (i.e., no interaction terms). By default, set to 2.

Generalized boosted regressions model (GBM)

Uses the gbm function from the package 'gbm,' you can set the following parameters (see gbm for more details):

trees

integer. The total number of trees to fit. This is equivalent to the number of iterations and the number of basis functions in the additive expansion. By default, set to 2500.

final.leave

integer. minimum number of observations in the trees terminal nodes. Note that this is the actual number of observations not the total weight. By default, set to 1.

algocv

integer. Number of cross-validations, default 3.

thresh.shrink

integer. Number of cross-validation folds to perform. If cv.folds>1 then gbm, in addition to the usual fit, will perform a cross-validation. By default, set to 1e-03.

Classification tree analysis (CTA)

Uses the rpart function from the package 'rpart', you can set the following parameters (see rpart for more details):

final.leave

integer. The minimum number of observations in any terminal node, default 1.

algocv

integer. Number of cross-validations, default 3.

Random Forest (RF)

Uses the randomForest function from the package 'randomForest', you can set the following parameters (see randomForest for more details):

trees

integer. Number of trees to grow. This should not be set to a too small number, to ensure that every input row gets predicted at least a few times. By default, set to 2500.

final.leave

integer. Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown (and thus take less time). By default, set to 1.

Maximum Entropy (MAXENT)

Uses the maxent function from the package 'dismo'. Make sure that you have correctly installed the maxent.jar file in the folder ~\R\library\version\dismo\java available at https://www.cs.princeton.edu/~schapire/maxent/ (see maxent for more details).

Artificial Neural Network (ANN)

Uses the nnet function from the package 'nnet', you can set the following parameters (see nnet for more details):

maxit

integer. Maximum number of iterations, default 500.

Support vector machines (SVM)

Uses the svm function from the package 'e1071', you can set the following parameters (see svm for more details):

epsilon

float. Epsilon parameter in the insensitive loss function, default 1e-08.

algocv

integer. If an integer value k>0 is specified, a k-fold cross-validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression. By default, set to 3.

Warning

Depending on the raster object resolution the process can be more or less time and memory consuming.

References

M. Barbet-Massin, F. Jiguet, C. H. Albert, & W. Thuiller (2012) "Selecting pseudo-absences for species distribution models: how, where and how many?" Methods Ecology and Evolution 3:327-338 http://onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00172.x/full

C. Liu, P. M. Berry, T. P. Dawson, R. & G. Pearson (2005) "Selecting thresholds of occurrence in the prediction of species distributions." Ecography 28:85-393 http://www.researchgate.net/publication/230246974_Selecting_Thresholds_of_Occurrence_in_the_Prediction_of_Species_Distributions

See Also

modelling to build SDMs with a single algorithm, stack_modelling to build SSDMs.

Examples

## Not run: 
# Loading data
data(Env)
data(Occurrences)
Occurrences <- subset(Occurrences, Occurrences$SPECIES == 'elliptica')

# ensemble SDM building
ESDM <- ensemble_modelling(c('CTA', 'MARS'), Occurrences, Env, rep = 1,
                          Xcol = 'LONGITUDE', Ycol = 'LATITUDE',
                          ensemble.thresh = c(0.6))

# Results plotting
plot(ESDM)

## End(Not run)

hugocalcad/LigthSSDM documentation built on June 22, 2019, 12:43 a.m.