P2 <- predict(m2, newdata=preds, filename= 'p2.img') # model Mean performance (per species), using test dataset (generated using partitioning): # toral number of replicates per model : 2 (per species) # thods (data partitioning) : subsampling # names of modelling methods : rf, cart, fda, mars, svm # Here we are going to fit 5 models and evaluate them through 2 runs of subsampling, each draw 30 percent # (i.e., subsampling, cross-validation, bootsrapping) There are also several methods to do that # be one time or several times (several replicates). # data available, therefore, we can split the dataset as an alternative solution. However, for most of cases, there is no such #(if so, we would specify in the test argument of sdmData). It is a better idea to have an independent dataset #(the data that were used to fit the mdoel). # in the above example, the performance statistics were calculated based on the training dataset # as you can see, a report is generates shows how many percent of models were successful, and # model performance (per species), using training test dataset: # model run success percentage (per species) : # names of modelling methods : glm, gam, brt # in the following example, we use 3 different methods to fit the models. This document provides a very quick demonstration on the package followed by some examples, that would be helpful to get a guick start with the package. For more information, check the published paper by Naimi and Araujo (2016) in the journal of Ecography. a unified interface is used to fit different models offered by different packages) 2) is able to support markedly different modelling approaches 3) enables scientists to modify the existing methods, extend the framework by developing new methods or procedures, and share them to be reproduced by the other scientists 4) handles spatial as well as temporal data for single or multiple species 5) employs high performance computing solutions to speed up modelling and simulations, and finally 6) uses flexible and easy-to-use GUI interface. The sdm package is designed to create a comprehensive modelling and simulation framework that: 1) provides a standardised and unified structure for handling species distributions data and modelling techniques (e.g. Sdm is an object-oriented, reproducible and extensible R platform for species distribution modelling. Getting started with sdm package Babak Naimi, Miguel B.
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