Fits sdm for single or multiple species using single or multiple methods specified by a user in
methods argument, and evaluates their performance.
Specifies the structure of the model, types of features, etc.
Character. Specifies the methods, used to fit the models
sdm fits multiple models and can be used to generate multiple runs (replicates) of each method through partitioning (using one or several partitioning methods including:
Each model is evaluated against training data, and if available, splitted data (through partitioning; called dependent test data as well, i.e., "dep.test") and/or indipendent test data ("indep.test").
User should make sure that the methods are available and the required packages for them are installed before putting their names in the function, otherwise, the methods that cannot be run for any reason, are excluded by the function. It is a good practice to call
installAll function (just one time when the sdm is installed), that tries to install all the packages that may be needed somewhere in the
A new method can be adopted and added to the package by a user using
add function. It is also possible to get an instance of an existing method, override the setting and definition, and then add it with a new name (e.g., my.glm).
The output would be a single object (
sdmModels) that can be read/reproduced everywhere (e.g., on a new machine). A setting object can also be taken (exported) out of the output
sdmModels object, that can be used to reproduce the same practice but given new conditions (i.e., new dataset, area. etc.)
an object of class
Babak Naimi firstname.lastname@example.org
Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881
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## Not run: file <- system.file("external/pa_df.csv", package="sdm") df <- read.csv(file) head(df) d <- sdmData(sp~b15+NDVI,train=df) d #---- # Example 1: fit using 3 models, and no evaluation (evaluation based on training dataset): m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm')) m # Example 3: fit using 5 models, and # evaluates using 10 runs of subsampling replications taking 30 percent as test: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','gbm','svm','rf'), replication='sub',test.percent=30,n=10) m # Example 3: fits using 5 models, and # evaluates using 10 runs of both 5-folds cross-validation and bootsrapping replication methods m <- sdm(sp~.,data=d,methods=c('gbm','tree','mars','mda','fda'), replication=c('cv','boot'),cv.folds=5,n=10) m # Example 4: fit using 3 models; evaluate the models using subsampling, # and override the default settings for the method brt: m <- sdm(sp~b15+NDVI,data=d,methods=c('glm','gam','brt'),test.p=30, modelSettings=list(brt=list(n.trees=500,train.fraction=0.8))) m ## End(Not run)
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