ensemble.batch | R Documentation |

The main function allows for batch processing of different species and different environmental RasterStacks. The function makes internal calls to `ensemble.calibrate.weights`

, `ensemble.calibrate.models`

and `ensemble.raster`

.

ensemble.batch(x = NULL, xn = c(x), species.presence = NULL, species.absence = NULL, presence.min = 20, thin.km = 0.1, an = 1000, excludep = FALSE, target.groups = FALSE, get.block = FALSE, block.default = runif(1) > 0.5, get.subblocks = FALSE, SSB.reduce = FALSE, CIRCLES.d = 250000, k.splits = 4, k.test = 0, n.ensembles = 1, VIF.max = 10, VIF.keep = NULL, SINK = FALSE, CATCH.OFF = FALSE, RASTER.datatype = "INT2S", RASTER.NAflag = -32767, models.save = FALSE, threshold.method = "spec_sens", threshold.sensitivity = 0.9, threshold.PresenceAbsence = FALSE, ENSEMBLE.best = 0, ENSEMBLE.min = 0.7, ENSEMBLE.exponent = 1, ENSEMBLE.weight.min = 0.05, input.weights = NULL, MAXENT = 1, MAXNET = 1, MAXLIKE = 1, GBM = 1, GBMSTEP = 0, RF = 1, CF = 1, GLM = 1, GLMSTEP = 1, GAM = 1, GAMSTEP = 1, MGCV = 1, MGCVFIX = 0, EARTH = 1, RPART = 1, NNET = 1, FDA = 1, SVM = 1 , SVME = 1, GLMNET = 1, BIOCLIM.O = 0, BIOCLIM = 1, DOMAIN = 1, MAHAL = 1, MAHAL01 = 1, PROBIT = FALSE, Yweights = "BIOMOD", layer.drops = NULL, factors = NULL, dummy.vars = NULL, formulae.defaults = TRUE, maxit = 100, MAXENT.a = NULL, MAXENT.an = 10000, MAXENT.path = paste(getwd(), "/models/maxent", sep=""), MAXNET.classes = "default", MAXNET.clamp = FALSE, MAXNET.type = "cloglog", MAXLIKE.formula = NULL, MAXLIKE.method = "BFGS", GBM.formula = NULL, GBM.n.trees = 2001, GBMSTEP.tree.complexity = 5, GBMSTEP.learning.rate = 0.005, GBMSTEP.bag.fraction = 0.5, GBMSTEP.step.size = 100, RF.formula = NULL, RF.ntree = 751, RF.mtry = floor(sqrt(raster::nlayers(x))), CF.formula = NULL, CF.ntree = 751, CF.mtry = floor(sqrt(raster::nlayers(x))), GLM.formula = NULL, GLM.family = binomial(link = "logit"), GLMSTEP.steps = 1000, STEP.formula = NULL, GLMSTEP.scope = NULL, GLMSTEP.k = 2, GAM.formula = NULL, GAM.family = binomial(link = "logit"), GAMSTEP.steps = 1000, GAMSTEP.scope = NULL, GAMSTEP.pos = 1, MGCV.formula = NULL, MGCV.select = FALSE, MGCVFIX.formula = NULL, EARTH.formula = NULL, EARTH.glm = list(family = binomial(link = "logit"), maxit = maxit), RPART.formula = NULL, RPART.xval = 50, NNET.formula = NULL, NNET.size = 8, NNET.decay = 0.01, FDA.formula = NULL, SVM.formula = NULL, SVME.formula = NULL, GLMNET.nlambda = 100, GLMNET.class = FALSE, BIOCLIM.O.fraction = 0.9, MAHAL.shape = 1) ensemble.mean(RASTER.species.name = "Species001", RASTER.stack.name = "base", positive.filters = c("tif", "_ENSEMBLE_"), negative.filters = c("xml"), RASTER.format = "GTiff", RASTER.datatype = "INT2S", RASTER.NAflag = -32767, abs.breaks = 6, pres.breaks = 6, sd.breaks = 9, p = NULL, a = NULL, pt = NULL, at = NULL, threshold = -1, threshold.method = "spec_sens", threshold.sensitivity = 0.9, threshold.PresenceAbsence = FALSE) ensemble.plot(RASTER.species.name = "Species001", RASTER.stack.name = "base", plot.method=c("suitability", "presence", "count", "consensussuitability", "consensuspresence", "consensuscount", "consensussd"), dev.new.width = 7, dev.new.height = 7, main = paste(RASTER.species.name, " ", plot.method, " for ", RASTER.stack.name, sep=""), positive.filters = c("tif"), negative.filters = c("xml"), p=NULL, a=NULL, threshold = -1, threshold.method = "spec_sens", threshold.sensitivity = 0.9, threshold.PresenceAbsence = FALSE, abs.breaks = 6, abs.col = NULL, pres.breaks = 6, pres.col = NULL, sd.breaks = 9, sd.col = NULL, absencePresence.col = NULL, count.col = NULL, ...)

`x` |
RasterStack object ( |

`xn` |
RasterStack object ( |

`species.presence` |
presence points used for calibrating the suitability models, available in 3-column (species, x, y) or (species, lon, lat) dataframe |

`species.absence` |
background points used for calibrating the suitability models, either available in a 3-column (species, x, y) or (species, lon, lat), or available in a 2-column (x, y) or (lon, lat) dataframe. In case of a 2-column dataframe, the same background locations will be used for all species. |

`presence.min` |
minimum number of presence locations for the organism (if smaller, no models are fitted). |

`thin.km` |
Threshold for minimum distance (km) between presence point locations for focal species for model calibrations in each run. A new data set is randomly selected via |

`an` |
number of background points for calibration to be selected with |

`excludep` |
parameter that indicates (if |

`target.groups` |
Parameter that indicates (if |

`get.block` |
if |

`block.default` |
if |

`get.subblocks` |
if |

`SSB.reduce` |
If |

`CIRCLES.d` |
Radius in m of circular neighbourhoods (created with |

`k` |
If larger than 1, the mumber of groups to split between calibration (k-1) and evaluation (1) data sets (for example, |

`k.splits` |
If larger than 1, the number of splits for the |

`k.test` |
If larger than 1, the mumber of groups to split between calibration (k-1) and evaluation (1) data sets when calibrating the final models (for example, |

`n.ensembles` |
If larger than 1, the number of different ensembles generated per species in batch processing. |

`VIF.max` |
Maximum Variance Inflation Factor of variables; see |

`VIF.keep` |
character vector with names of the variables to be kept; see |

`SINK` |
Append the results to a text file in subfolder 'outputs' (if |

`CATCH.OFF` |
Disable calls to function |

`RASTER.format` |
Format of the raster files that will be generated. See |

`RASTER.datatype` |
Format of the raster files that will be generated. See |

`RASTER.NAflag` |
Value that is used to store missing data. See |

`models.save` |
Save the list with model details to a file (if |

`threshold.method` |
Method to calculate the threshold between predicted absence and presence; possibilities include |

`threshold.sensitivity` |
Sensitivity value for |

`threshold.PresenceAbsence` |
If |

`ENSEMBLE.best` |
The number of individual suitability models to be used in the consensus suitability map (based on a weighted average). In case this parameter is smaller than 1 or larger than the number of positive input weights of individual models, then all individual suitability models with positive input weights are included in the consensus suitability map. In case a vector is provided, |

`ENSEMBLE.min` |
The minimum input weight (typically corresponding to AUC values) for a model to be included in the ensemble. In case a vector is provided, function |

`ENSEMBLE.exponent` |
Exponent applied to AUC values to convert AUC values into weights (for example, an exponent of 2 converts input weights of 0.7, 0.8 and 0.9 into 0.7^2=0.49, 0.8^2=0.64 and 0.9^2=0.81). See details. |

`ENSEMBLE.weight.min` |
The minimum output weight for models included in the ensemble, applying to weights that sum to one. Note that |

`input.weights` |
array with numeric values for the different modelling algorithms; if |

`MAXENT` |
Input weight for a maximum entropy model ( |

`MAXNET` |
number: if larger than 0, then a maximum entropy model ( |

`MAXLIKE` |
Input weight for a maxlike model ( |

`GBM` |
Input weight for a boosted regression trees model ( |

`GBMSTEP` |
Input weight for a stepwise boosted regression trees model ( |

`RF` |
Input weight for a random forest model ( |

`CF` |
number: if larger than 0, then a random forest model ( |

`GLM` |
Input weight for a generalized linear model ( |

`GLMSTEP` |
Input weight for a stepwise generalized linear model ( |

`GAM` |
Input weight for a generalized additive model ( |

`GAMSTEP` |
Input weight for a stepwise generalized additive model ( |

`MGCV` |
Input weight for a generalized additive model ( |

`MGCVFIX` |
number: if larger than 0, then a generalized additive model with fixed d.f. regression splines ( |

`EARTH` |
Input weight for a multivariate adaptive regression spline model ( |

`RPART` |
Input weight for a recursive partioning and regression tree model ( |

`NNET` |
Input weight for an artificial neural network model ( |

`FDA` |
Input weight for a flexible discriminant analysis model ( |

`SVM` |
Input weight for a support vector machine model ( |

`SVME` |
Input weight for a support vector machine model ( |

`GLMNET` |
Input weight for a GLM with lasso or elasticnet regularization ( |

`BIOCLIM.O` |
Input weight for the original BIOCLIM algorithm ( |

`BIOCLIM` |
Input weight for the BIOCLIM algorithm ( |

`DOMAIN` |
Input weight for the DOMAIN algorithm ( |

`MAHAL` |
Input weight for the Mahalonobis algorithm ( |

`MAHAL01` |
Input weight for the Mahalanobis algorithm ( |

`PROBIT` |
If |

`Yweights` |
chooses how cases of presence and background (absence) are weighted; |

`layer.drops` |
vector that indicates which layers should be removed from RasterStack |

`factors` |
vector that indicates which variables are factors; see also |

`dummy.vars` |
vector that indicates which variables are dummy variables (influences formulae suggestions) |

`formulae.defaults` |
Suggest formulae for most of the models (if |

`maxit` |
Maximum number of iterations for some of the models. See also |

`MAXENT.a` |
background points used for calibrating the maximum entropy model ( |

`MAXENT.an` |
number of background points for calibration to be selected with |

`MAXENT.path` |
path to the directory where output files of the maximum entropy model are stored; see also |

`MAXNET.classes` |
continuous feature classes, either "default" or any subset of "lqpht" (linear, quadratic, product, hinge, threshold). Note that the "default" option chooses feature classes based on the number of presence locations as "l" (< 10 locations), "lq" (10 - 14 locations), "lqh" (15 - 79 locations) or "lqph" (> 79 locations). See also |

`MAXNET.clamp` |
restrict predictors and features to the range seen during model training; see also |

`MAXNET.type` |
type of response required; see also |

`MAXLIKE.formula` |
formula for the maxlike algorithm; see also |

`MAXLIKE.method` |
method for the maxlike algorithm; see also |

`GBM.formula` |
formula for the boosted regression trees algorithm; see also |

`GBM.n.trees` |
total number of trees to fit for the boosted regression trees model; see also |

`GBMSTEP.tree.complexity` |
complexity of individual trees for stepwise boosted regression trees; see also |

`GBMSTEP.learning.rate` |
weight applied to individual trees for stepwise boosted regression trees; see also |

`GBMSTEP.bag.fraction` |
proportion of observations used in selecting variables for stepwise boosted regression trees; see also |

`GBMSTEP.step.size` |
number of trees to add at each cycle for stepwise boosted regression trees (should be small enough to result in a smaller holdout deviance than the initial number of trees [50]); see also |

`RF.formula` |
formula for the random forest algorithm; see also |

`RF.ntree` |
number of trees to grow for random forest algorithm; see also |

`RF.mtry` |
number of variables randomly sampled as candidates at each split for random forest algorithm; see also |

`CF.formula` |
formula for random forest algorithm; see also |

`CF.ntree` |
number of trees to grow in a forest; see also |

`CF.mtry` |
number of input variables randomly sampled as candidates at each node for random forest like algorithms; see also |

`GLM.formula` |
formula for the generalized linear model; see also |

`GLM.family` |
description of the error distribution and link function for the generalized linear model; see also |

`GLMSTEP.steps` |
maximum number of steps to be considered for stepwise generalized linear model; see also |

`STEP.formula` |
formula for the "starting model" to be considered for stepwise generalized linear model; see also |

`GLMSTEP.scope` |
range of models examined in the stepwise search; see also |

`GLMSTEP.k` |
multiple of the number of degrees of freedom used for the penalty (only k = 2 gives the genuine AIC); see also |

`GAM.formula` |
formula for the generalized additive model; see also |

`GAM.family` |
description of the error distribution and link function for the generalized additive model; see also |

`GAMSTEP.steps` |
maximum number of steps to be considered in the stepwise generalized additive model; see also |

`GAMSTEP.scope` |
range of models examined in the step-wise search n the stepwise generalized additive model; see also |

`GAMSTEP.pos` |
parameter expected to be set to 1 to allow for fitting of the stepwise generalized additive model |

`MGCV.formula` |
formula for the generalized additive model; see also |

`MGCV.select` |
if |

`MGCVFIX.formula` |
formula for the generalized additive model with fixed d.f. regression splines; see also |

`EARTH.formula` |
formula for the multivariate adaptive regression spline model; see also |

`EARTH.glm` |
list of arguments to pass on to |

`RPART.formula` |
formula for the recursive partioning and regression tree model; see also |

`RPART.xval` |
number of cross-validations for the recursive partioning and regression tree model; see also |

`NNET.formula` |
formula for the artificial neural network model; see also |

`NNET.size` |
number of units in the hidden layer for the artificial neural network model; see also |

`NNET.decay` |
parameter of weight decay for the artificial neural network model; see also |

`FDA.formula` |
formula for the flexible discriminant analysis model; see also |

`SVM.formula` |
formula for the support vector machine model; see also |

`SVME.formula` |
formula for the support vector machine model; see also |

`GLMNET.nlambda` |
The number of |

`GLMNET.class` |
Use the predicted class to calculate the mean predictions of GLMNET; see also |

`BIOCLIM.O.fraction` |
Fraction of range representing the optimal limits, default value of 0.9 as in the original BIOCLIM software ( |

`MAHAL.shape` |
parameter that influences the transformation of output values of |

`RASTER.species.name` |
First part of the names of the raster files, expected to identify the modelled species (or organism). |

`RASTER.stack.name` |
Last part of the names of the raster files, expected to identify the predictor stack used. |

`positive.filters` |
vector that indicates parts of filenames for files that will be included in the calculation of the mean probability values |

`negative.filters` |
vector that indicates parts of filenames for files that will not be included in the calculation of the mean probability values |

`abs.breaks` |
Number of breaks in the colouring scheme for absence (only applies to |

`pres.breaks` |
Number of breaks in the colouring scheme for presence (only applies to |

`sd.breaks` |
Number of breaks in the colouring scheme for standard deviation (only applies to |

`p` |
presence points used for calibrating the suitability models, typically available in 2-column (x, y) or (lon, lat) dataframe; see also |

`a` |
background points used for calibrating the suitability models, typically available in 2-column (x, y) or (lon, lat) dataframe; see also |

`pt` |
presence points used for evaluating the suitability models, typically available in 2-column (lon, lat) dataframe; see also |

`at` |
background points used for calibrating the suitability models, typicall available in 2-column (lon, lat) dataframe; see also |

`threshold` |
Threshold value that will be used to distinguish between presence and absence. If < 0, then a threshold value will be calculated from the provided presence |

`plot.method` |
Choice of maps to be plotted: |

`dev.new.width` |
Width for new graphics device ( |

`dev.new.height` |
Heigth for new graphics device ( |

`main` |
main title for the plots. |

`abs.col` |
specify colours for absence (see examples on how not to plot areas where the species is predicted absent) |

`pres.col` |
specify colours for presence |

`sd.col` |
specify colours for standard deviation |

`absencePresence.col` |
specify colours for absence - presence maps (see examples on how not to plot areas where the species is predicted absent) |

`count.col` |
specify colours for number of algorithms or ensembles (see examples on how not to plot areas where the species is predicted absent) |

`...` |
Other items passed to function |

This function allows for batch processing of different species and different environmental RasterStacks. The function makes internal calls to `ensemble.spatialThin`

, `ensemble.VIF`

, `ensemble.calibrate.weights`

, `ensemble.calibrate.models`

and `ensemble.raster`

.

Different ensemble runs allow for different random selection of k-fold subsets, background locations or spatial thinning of presence locations.

`ensemble.calibrate.weights`

results in a cross-validation procedure whereby the data set is split in calibration and testing subsets and the best weights for the ensemble model are determined (including the possibility for weights = 0).

`ensemble.calibrate.models`

is the step whereby models are calibrated using all the available presence data.

`ensemble.raster`

is the final step whereby raster layers are produced for the ensemble model.

Function `ensemble.mean`

results in raster layers that are based on the summary of several ensemble layers: the new ensemble has probability values that are the mean of the probabilities of the different raster layers, the presence-absence threshold is derived for this new ensemble layer, whereas the count reflects the number of ensemble layers where presence was predicted. Note the assumption that input probabilities are scaled between 0 and 1000 (as the output from `ensemble.raster`

), whereas thresholds are based on actual probabilities (scaled between 0 and 1). After the mean probability has been calculated, the niche overlap (`nicheOverlap`

) with the different input layers is calculated.

Function `ensemble.plot`

plots suitability, presence-absence or count maps. In the case of suitability maps, the presence-absence threshold needs to be provide as suitabilities smaller than the threshold will be coloured red to orange, whereas suitabilities larger than the threshold will be coloured light blue to dark blue.

The function finally results in ensemble raster layers for each species, including the fitted values for the ensemble model, the estimated presence-absence and the count of the number of submodels prediction presence and absence.

Roeland Kindt (World Agroforestry Centre), Eike Luedeling (World Agroforestry Centre) and Evert Thomas (Bioversity International)

Kindt R. 2018. Ensemble species distribution modelling with transformed suitability values. Environmental Modelling & Software 100: 136-145. doi: 10.1016/j.envsoft.2017.11.009

Buisson L, Thuiller W, Casajus N, Lek S and Grenouillet G. 2010. Uncertainty in ensemble forecasting of species distribution. Global Change Biology 16: 1145-1157

Phillips SJ, Dudik M, Elith J et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19: 181-197.

`ensemble.calibrate.weights`

, `ensemble.calibrate.models`

, `ensemble.raster`

## Not run: # based on examples in the dismo package # get predictor variables library(dismo) predictor.files <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''), pattern='grd', full.names=TRUE) predictors <- stack(predictor.files) # subset based on Variance Inflation Factors predictors <- subset(predictors, subset=c("bio5", "bio6", "bio16", "bio17", "biome")) predictors predictors@title <- "base" # presence points presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='') pres <- read.table(presence_file, header=TRUE, sep=',') pres[,1] <- rep("Bradypus", nrow(pres)) # choose background points background <- randomPoints(predictors, n=1000, extf = 1.00) # north and south for new predictions (as if new climates) ext2 <- extent(-90, -32, 0, 23) predictors2 <- crop(predictors, y=ext2) predictors2 <- stack(predictors2) predictors2@title <- "north" ext3 <- extent(-90, -32, -33, 0) predictors3 <- crop(predictors, y=ext3) predictors3 <- stack(predictors3) predictors3@title <- "south" # fit 3 ensembles with batch processing, choosing the best ensemble model based on the # average weights of 4-fold split of calibration and testing data # final models use all available presence data and average weights determined by the # ensemble.calibrate.weights function (called internally) # batch processing can handle several species by using 3-column species.presence and # species.absence data sets # note that these calculations can take a while ensemble.nofactors <- ensemble.batch(x=predictors, xn=c(predictors, predictors2, predictors3), species.presence=pres, species.absence=background, k.splits=4, k.test=0, n.ensembles=3, SINK=TRUE, layer.drops=c("biome"), ENSEMBLE.best=0, ENSEMBLE.exponent=c(1, 2, 3), ENSEMBLE.min=0.7, MAXENT=0, MAXNET=1, MAXLIKE=1, GBM=1, GBMSTEP=0, RF=1, CF=1, GLM=1, GLMSTEP=1, GAM=1, GAMSTEP=1, MGCV=1, MGCVFIX=1, EARTH=1, RPART=1, NNET=1, FDA=1, SVM=1, SVME=1, GLMNET=1, BIOCLIM.O=1, BIOCLIM=1, DOMAIN=1, MAHAL=0, MAHAL01=1, PROBIT=TRUE, Yweights="BIOMOD", formulae.defaults=TRUE) # summaries for the 3 ensembles for the species # summaries are based on files in folders ensemble/suitability, # ensemble/presence and ensemble/count # ensemble.mean is used internally in ensemble.batch ensemble.mean(RASTER.species.name="Bradypus", RASTER.stack.name="base", p=pres, a=background) # plot mean suitability without specifying colours plot1 <- ensemble.plot(RASTER.species.name="Bradypus", RASTER.stack.name="base", plot.method="consensussuitability", p=pres, a=background, abs.breaks=4, pres.breaks=9) plot1 # only colour the areas where species is predicted to be present # option is invoked by having no absence breaks # same colourscheme as \url{http://www.worldagroforestry.org/atlas-central-america} LAatlascols <- grDevices::colorRampPalette(c("#FFFF80", "#38E009","#1A93AB", "#0C1078")) plot2 <- ensemble.plot(RASTER.species.name="Bradypus", RASTER.stack.name="base", plot.method="consensussuitability", p=pres, a=background, abs.breaks=0, pres.breaks=9, pres.col=LAatlascols(8)) plot2 # only colour the areas where species is predicted to be present # option is invoked by only setting one colour for absence-presence plot3 <- ensemble.plot(RASTER.species.name="Bradypus", RASTER.stack.name="base", plot.method="consensuspresence", absencePresence.col=c("#90EE90")) # only colour presence area by specifying colours > 0 plot4 <- ensemble.plot(RASTER.species.name="Bradypus", RASTER.stack.name="base", plot.method="consensuscount", count.col=LAatlascols(3)) ## End(Not run)

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