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RE: biomod2 using a Random Forest regression based approach [ Reply ] By: Maya Guéguen on 2023-11-14 09:14	[forum:49807]
Hello Gabio, Jinyu, biomod2 has changed and improved a lot lately. New documentation website with examples is available here : https://biomodhub.github.io/biomod2/index.html Issues are now managed directly onto the github page : https://github.com/biomodhub/biomod2/issues Feel free to post if you have any questions / problems, or to use it to look for similar problems. Maya

RE: biomod2 using a Random Forest regression based approach [ Reply ] By: Jinyu Li on 2023-08-21 00:57	[forum:49804]
Hi, Fábio, We have encountered the same error when we tried to run the biomod2 using the Random Forest algorithm and the regression-based option activated instead of the classification approach. Should we change other parameters using the BIOMOD_ModelingOptions? How do you settled the problem? Bests, Jinyu

biomod2 using a Random Forest regression based approach [ Reply ] By: Fábio Matos on 2021-01-26 16:44

[forum:48853]

Hi, I tried to run the biomod2 using the Random Forest algorithm and the regression-based option activated instead of the classification approach (the default option in the package) to compare results between the two methods. However, the following error came out: Error in predict.randomForest(get_formal_model(object), as.data.frame(newdata[not_na_rows, : 'prob' or 'vote' not meaningful for regression Please find a reproducible example below. What am I doing wrong? Should I change other parameters using the BIOMOD_ModelingOptions? Thank you in advance! Best, Fábio ########### library(biomod2) # species occurrences DataSpecies <- read.csv(system.file("external/species/mammals_table.csv", package="biomod2"), row.names = 1) head(DataSpecies) # the name of studied species myRespName <- 'GuloGulo' # the presence/absences data for our species myResp <- as.numeric(DataSpecies[,myRespName]) # the XY coordinates of species data myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")] # Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12) myExpl = raster::stack( system.file( "external/bioclim/current/bio3.grd", package="biomod2"), system.file( "external/bioclim/current/bio4.grd", package="biomod2"), system.file( "external/bioclim/current/bio7.grd", package="biomod2"), system.file( "external/bioclim/current/bio11.grd", package="biomod2"), system.file( "external/bioclim/current/bio12.grd", package="biomod2")) # 1. Formatting Data myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName) # 2. Defining Models Options using default options. myBiomodOption <- BIOMOD_ModelingOptions() myBiomodOption@RF$do.classif <- F # 3. Doing Modelisation myBiomodModelOut <- BIOMOD_Modeling( myBiomodData, models = c('RF'), models.options = myBiomodOption, NbRunEval=1, DataSplit=70, models.eval.meth = c('TSS'), do.full.models = FALSE)