[R] probabilities from predict.svm

[Steve Lianoglou]

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 <watlingj at 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)
>
>
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>



-- 
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