Li,
You can use the "predict" function in the 'raster' package. There are also
examples with randomForest and other techniques in this vignette that comes
with the 'dismo' package:
http://cran.r-project.org/web/packages/dismo/vignettes/sdm.pdf
Robert
> Li Wen-2 wrote
> > Dear list member
> >
>
Jaime,
Please do not cross-post your questions, and provide sessionInfo() results
when reporting something that does not work. I think this error means that
you do not have maxent.jar installed. What does maxent() return?
Robert
On Wed, Oct 31, 2012 at 4:14 AM, Jaime Burbano Girón wrote:
> Hi de
ent species, or do I have to fit the model
>> manually for each species (resp. in a loop?) How can I access the results
>> of the single species (like the 'me' in the examples of maxent).
>>
>> Another maxent related question: What happens if one of the predictor
&g
no Girón :
> Hi to everybody. Few weeks ago I performed successfully a Thin plate spline
> regression ("Tps" function from fields package) following instructions given
> in this forum by professor Robert J. Hijmans.
>
> In that moment I calculated successfully the standard
You can also do this with the 'raster' package. See ?raster::predict
Robert
On Mon, Jul 12, 2010 at 11:40 PM, Kingsford Jones
wrote:
> Not sure which 'logistic function' you're asking about, but logistic
> regression is a case of the generalized linear model and can be fit
> with base::glm. It
or pass Z values
>
> This is the code I used:
>
> data<-read.table("precp00-09ContSampleDem.csv",sep=",",header=TRUE)
> fit<-Tps(data[,c("x","y")],data[,"Ene"],z=data$dem)
> elev=raster("dem_col.asc")
> result<
Jorge,
You could use the function 'interpolate' in 'raster'. It makes
predictions for smaller chunks of data and puts these back together.
There is an example for Tps. You would need to provide elevation data
(as a RasterLayer) as the first argument.
Robert
On Thu, Jun 17, 2010 at 1:25 PM, Jorge V
Dear Liliana,
This error:
> # predict the maxent model to the predictor stack
> mep <- predict(me, predictors)
> Error in .local(object, ...) : missing layers (or wrong names)
occurs when all variables that were used to build the model are not
also available in the RasterStack (at least not unde
