Forum: biomod2 package is now available !
Posted by: damien georges
Content: Dear BIOMOD-users,You were eagerly waiting for it, and we are happy to say that the new version of BIOMOD called biomod2 is now online on R-Forge. Although we kept the same modelling philosophy than the former version (which we will still maintain for a while), we have made crucial changes. biomod2 is now fully object-oriented and made for running on a single species only (see vignette MultiSpeciesModelling for multi-species modelling at once). For advanced BIOMOD users, the new functions might be a bit disturbing at the beginning. Then, you will see that this new version is much more advanced and practical than the former ones. Among the novelties, the addition of MAXENT in the modelling techniques, a large range of evaluation metrics, a more refined definition of ensemble modelling and ensemble forecasting, the possibility to give presence-only data and environmental rasters to biomod2 and let it extract pseudo-absence data directly. We have created several vignettes for you to get use to this new version and a figure explaining the different ways of giving data to BIOMOD. Please bear in mind that R-Forge is a development platform, it means that this new package would experience repeated updating the next couple of weeks (correcting bugs, adding documentation, adding functionalities) so think about updating the package before each new study you will do. Last but not least, all comments are welcome! If you find a bug, if you think some documentation points are unclear, if you think about new functionalities that may be useful, just let us know ASAP. We count on you to help finalizing this new version to a very nice tool. We will then release it to CRAN by the end of July. Please remember to add your code, R-version, OS and BIOMOD-version every time you report a bug or a mistake in the vignette or help files. Hoping you will enjoy this new version of BIOMOD. With our best wishes, Damien & Wilfried |
Latest Newsbiomod2 is now devel on githubdamien georges - 2020-03-02 17:08 -
biomod2 package is now available !damien georges - 2012-07-26 13:56 -
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| RE: Warning message about convergence after running GAM model [ Reply ] By: damien georges on 2020-06-19 13:53 | [forum:47866] |
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Hi, I think the best test is to go until projection step and make a deep visual inspection of the species distribution in current condition. I also advise you to compare GAM with other models prediction. If everithing is coherent then you can keep going. Cheers, Damien |
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| Warning message about convergence after running GAM model [ Reply ] By: Floor Maters on 2020-05-11 15:48 | [forum:47855] |
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Dear Damien and Wilfried, I am using biomod2 for my master thesis for predicting several cetacean distributions and get a warning message when trying to run my GAM model: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully When I looked up this warning I found this link (unrelated to biomod2 but related to GAM and the same error): https://github.com/ToonVanDaele/trias-test/issues/8 From this link I would say the error has something to do with poor data, which may be right as I don't have many sightings . I am using 5 environmental predictor layers, when removing or adding predictor layers I still get the warning. I only have presence points and increasing or decreasing the amount of pseudo absences does not remove the warning either. My question: do you have any idea whether I can ignore this warning or any suggestions on what to do? Ignoring the warning feels weird, but I also don't know what to change anymore. Thank you! Best, Floor ------------------------------------------------------------------ ################################################### ### 1: loading_data # load the library library(biomod2) # extra libraries library(raster) library(rgdal) library(maptools) library(dismo) library(ggplot2) # load our species data DataSpecies <- read.csv(path2sp,sep=';',fileEncoding="UTF-8-BOM") # Check species data head(DataSpecies) #plot species data qplot(x=x,y=y,colour=Spinner,data=DataSpecies) # the name of studied species myRespName <- 'Spinner' # the presence/absences data for our species myResp <- as.numeric(DataSpecies[,myRespName]) myResp # the XY coordinates of species data myRespXY <- DataSpecies[,c("x","y")] # check coordinates str(myRespXY) head(myRespXY) layers<-stack(list.files(path=path2layers,pattern='asc',full.names=TRUE )) # Check layers structure print(layers) # Plot layers plot(layers) # Descriptive statistics for environmental layers summary(layers) # Select variables to include in the model names(layers) myExpl<-layers[[c("bathymetry","chla_annual","d_coast_ls","slope","sss_annual")]] # Check layers structure print(myExpl) # Plot environmental variables on geographical space plot(myExpl) ################################################### ### 2: formating_data myBiomodData <- BIOMOD_FormatingData(resp.var = myResp, expl.var = myExpl, resp.xy = myRespXY, resp.name = myRespName ) # print_formating_data myBiomodData #: plot_formating_data plot(myBiomodData) ################################################### ### 3: modeling_options # Defining Models Options using default options # Consult help ?BIOMOD_ModelingOptions myBiomodOption <- BIOMOD_ModelingOptions() # Setting working directory setwd(path2work) ################################################### ### 4: modeling # Computing the models # See help ?BIOMOD_Modeling myBiomodModelOut <- (BIOMOD_Modeling( myBiomodData, # data including species records, species name, and variables models = c('GAM'), # modelling methods models.options = myBiomodOption, # options for modelling NbRunEval=10, # number of Evaluation run DataSplit=70, # % of data used as training Prevalence = 0.5, VarImport=1, # number of permutation to estimate var importance models.eval.meth = c('TSS','ROC'), # evaluation metrics #SaveObj = TRUE, # keep all results and outputs on hard drive #rescal.all.models = TRUE, # all models scaled with a binomial GLM: the range of the scale is 0-1000 do.full.models = TRUE, # models calibrated and evaluated with the whole dataset modeling.id = "allmodels")) # ID (=name) of modeling procedure Output: =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Spinner Modeling Summary -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= 5 environmental variables ( bathymetry chla_annual d_coast_ls slope sss_annual ) Number of evaluation repetitions : 11 Models selected : GAM Total number of model runs : 11 -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= -=-=-=- Run : Spinner_AllData -=-=-=--=-=-=- Spinner_AllData_RUN1 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN2 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN3 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN4 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN5 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN6 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN7 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN8 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN9 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_RUN10 Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=--=-=-=- Spinner_AllData_Full Model=GAM GAM_mgcv algorithm chosen Automatic formula generation... > GAM (mgcv) modelling... Evaluating Model stuff... Evaluating Predictor Contributions... -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Warning messages: 1: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 2: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 3: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 4: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 5: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 6: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 7: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 8: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully 9: In newton(lsp = lsp, X = G$X, y = G$y, Eb = G$Eb, UrS = G$UrS, L = G$L, : Iteration limit reached without full convergence - check carefully |
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