Hi James, I'd like to help you out, but I'm not sure I understand what the problem is.
Does the problem lie with building a predictive SVM, or getting the right values (class probabilities) to land in the right place on your map/plot? -steve On Wed, Aug 18, 2010 at 3:09 PM, Watling,James I <watli...@ufl.edu> wrote: > Dear R Community- > > I am a new user of support vector machines for species distribution modeling > and am using package e1071 to run svm() and predict.svm(). Briefly, I want > to create an svm model for classification of a factor response (species > presence or absence) based on climate predictor variables. I have used a > training dataset to train the model, and tested it against a validation data > set with good results: AUC is high, and the confusion matrix indicates low > commission and omission errors. The code for the best-fit model is: > > svm.model > <-svm(as.factor(acutus)~p_feb+p_jan+p_mar+p_sep+t_feb+t_july+t_june+t_mar,cost=10000, > gamma=1, probability=T) > > Because ultimately I want to create prediction maps of probabilities of > species occurrence under future climate change, I want to use the results of > the validated model to predict probability of presence using data describing > future conditions. I have created a data frame (predict.data) with new > values for the same predictor variables used in the original model; each > value corresponds to an observation from a raster grid of the study area. I > enabled the probability option when creating the original model, and acquire > the probabilities using the predict function: > pred.map <-predict(svm.model, predict.data, probability=T). However, when I > use probs<-attr(pred.map, "probabilities") to acquire the probabilities for > each grid cell, the spatial signature of the probabilities does make sense. > I have extracted the column of probabilities for class = 1 (probability of > presence), and the resulting map of the study area is spatially accurate (it > has the right shape), but the probability values are incorrect, or at least > in the wrong place. I am attaching a pdf (SVM prediction maps) of the > resulting map using probabilities obtained using the code described above > (page 1) and a map of what the prediction map should look like given spatial > autocorrelation in climate predictors (page 2, map generated using > openmodeller). Note that the openmodeller map was created with the same > input data and same svm algorithm (also using code from libsvm) as the model > in R, just run using different software. I don't know why the prediction map > of probabilities based on the model is so different from what I would > expect, and would appreciate any thoughts from the group. > > All the best > > James > > ******************************************************************************* > James I Watling, PhD > Postdoctoral Research Associate > University of Florida > Ft. Lauderdale Research & Education Center > 3205 College Avenue > Ft Lauderdale, FL 33314 USA > 954.577.6316 (phone) > 954.475.4125 (fax) > > > ******************************************************************************* > James I Watling, PhD > Postdoctoral Research Associate > University of Florida > Ft. Lauderdale Research & Education Center > 3205 College Avenue > Ft Lauderdale, FL 33314 USA > 954.577.6316 (phone) > 954.475.4125 (fax) > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
