rangeSVM_predict | R Documentation |
rangeSVM_predict()
returns a raster representing the ranges of the species
predicted by the fitted SVM tuned with rangeSVM()
.
rangeSVM_predict(svm, r, sdm = NULL)
svm |
Model object for the SVM, returned by |
r |
Raster with the extent desired for the prediction. The values for cells used for predictions must have non-NA values, but the particular values do not matter. |
sdm |
Raster or RasterStack representing environmental suitability (can be predictions from SDMs). These rasters must match the predictor variables used in the SVM. Default is NULL. |
The values of the output raster are 1, 2, ..., corresponding to xy1, xy2, and any additional species used in rangeSVM()
.
These values represent the identities of the species.
The Raster representing the SVM predictions.
r1.sdm <- raster::raster(raster::extent(c(-72, -64, 41, 50)), res = c(0.008333333, 0.008333333)) raster::values(r1.sdm) <- (1:raster::ncell(r1.sdm))^2 r2.sdm <- raster::raster(raster::extent(c(-72, -64, 41, 50)), res = c(0.008333333, 0.008333333)) raster::values(r2.sdm) <- (raster::ncell(r2.sdm):1)^2 r3.sdm <- raster::raster(raster::extent(c(-72, -64, 41, 50)), res = c(0.008333333, 0.008333333)) r3.sdm [1] <- 10 r3.sdm <- raster::distance(r3.sdm) sp1.xy <- data.frame(dismo::randomPoints(r1.sdm, 15, prob = TRUE)) colnames(sp1.xy) <- c("longitude", "latitude") sp2.xy <- data.frame(dismo::randomPoints(r2.sdm, 15, prob = TRUE)) colnames(sp2.xy) <- c("longitude", "latitude") sp3.xy <- data.frame(dismo::randomPoints(r3.sdm, 15, prob = TRUE)) colnames(sp3.xy) <- c("longitude", "latitude") # Spatial SVMs (this can take about a minute to run) svm.SP <- rangeSVM(sp1.xy, sp2.xy, sp3.xy, nrep=5) # Use SVM to create a raster of predicted regions rand_svm.SP <- rangeSVM_predict(svm = svm.SP, r = r1.sdm)
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