Hi, you may use the evaluates function, put the outcome of sdmData as the first argument, and the raster generated by ensemble as the second argumen: e <- evaluates(d, ens) e@statistics e@threshold_based

@@babaknaimi thank you so much, it works

It was a problem in the predict function that has been fixed in the new version. Install the latest version of the sdm package!

@@biogeoinformatics Thank you, it solved the problem of the matching number of cells, but now when I run the models (m), Maxent models don't work but the rest of the algorithms work, can you give me an insight on that?

@@felipeespinoza8647 Maxent requires rJava in R and Java on your machine (OS). You need to first install JDK on your machine (not within R; on your operating system), then it should work. You may need to restart R. You can download JDK from here: www.oracle.com/java/technologies/downloads/

I did that already, and it as working, but after installing the latest version I get this error: > m2 <- sdm(species~., data = d, methods = 'maxent', replication='sub', + test.p=30, n=3) > m2 class : sdmModels ======================================================== number of species : 1 number of modelling methods : 1 names of modelling methods : maxent replicate.methods (data partitioning) : subsampling number of replicates (each method) : 3 toral number of replicates per model : 3 (per species) test percentage (in subsampling) : 30 ------------------------------------------ model run success percentage (per species) : ------------------------------------------ method species ---------------------- maxent : 0 % ################################################################### Error in if (wtest == "test.dep") cat("model Mean performance (per species), using test dataset (generated using partitioning): ") else if (wtest == : missing value where TRUE/FALSE needed

In your code, there is no sense in using parallel as you only have one method and one replication, though I don't think it is the reason for crashing. It could be related to the Java run time, rJava, or even lack of memory. The first step is to see if it works appropriately in a non-parallelised code

@@biogeoinformatics Thank you for your response - I tried the code without parallel and just kept maxent it crashed as well. The R studio crashes without any error message (only when maxent is added in the code) for other methods the code works well. At last I ran the script in R (not studio) the code worked. Surprisingly the same thing is not working in studio. I was able to run the script in R 4.2.2. Thank you for this efficient code !

I will add the possibility in the new version

I am updating the package but meanwhile, you may use a raster format with extension of .grd, so use: p1 <- predict(m, newdata=biom, filename='pr.grd')

@@babaknaimi I Tried .grd extension but it shows( Error in o[[id[j]]] : subscript out of bounds). Any Solution :)

@@babaknaimi hi! I have the same problem. Is there any update yet, or any planned date? Thank you very much!

Use installAll() to install all the required packages

Convert your species data to a SpatialPointsDataFrame

@@babaknaimi class(sp) [1] "data.frame" Sir can you please tell me the code used for it

@@babaknaimi I have converted species data to spatial data frame sir > class(spwc) [1] "sf" "data.frame" > spwc Simple feature collection with 32 features and 1 field Geometry type: POINT Dimension: XY Bounding box: xmin: 76.64554 ymin: 10.49855 xmax: 76.74306 ymax: 11.0425 CRS: NA. But even now I am facing issue sir > d <- sdmData(species∼., spwc, predictors=biom) Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘sdmData’ for signature ‘"formula", "sf", "missing", "RasterStack"’

You need to convert it to SpatialPointsDataFrame (not sf) coordinates(sp) <- c("X", "Y") Replace X and Y with the names of coordinate columns

My guess is that your input data (generated by sdmData) is presence-only. Put the setting in the bg argument in the sdmData to have background generated

@UCqDtax1iPvWGBm-BGvraORA I would check the column names in points (make sure you would have one column contains presences as 1), also check d object to see if the name of species is the same as you used in formula in the sdm function

@@babaknaimi Thank you so much, it works now. I truly appreciate it, sir.

It is because of the changes in GDAL library. I am fixing in the new version. Meanwhile, you may use the native raster format in the raster package, a file with extension of .grd

Convert sp to a SpatialPointsDataFrame

Thanks, It's work now!

You can use the evaluates function, put the sdmdata object as the first argument, and the raster output of ensemble as the second argument

@@biogeoinformatics thanks for the reply :) but have another question. I successfully ran the model, now looking through the outcomes and have one question. In the variable importance, is the metric based on the jackknife approach? and if not how are correlation test and auc test done?

I got i5 9th gen and it works perfectly fine for me.

Me . Did you find any solution to that?

I practiced the code so I have it in .R file. Let me know if you need it.

@@zainulabdin4466bro i need it. It will be very helpful if you are able to provide me the code

Actually I got the code to run but it keeps running for eternity and I have to abort r in order to stop it.. so there's a little problem.. not sure what makes it behave this way..

You can simply run installAll() which installs all required packages including those required by gui (gui requires shiny).

@@yadavghimirey I also think you are returning to console before closing the open shiny gui. You need to close the shiny gui first then come back to R console to continue with other codes.

@@biogeoinformatics Thank you very much

@@wyclifeoluoch9376 Thx! :)

Once you have the output of sdmData object, say d, you can convert it to data.frame object, extract species and coordinates columns, and filter where species == 0. df <- as.data.frame(d) df_spec_coord <- data.frame(species = df$species, coordinates(d)) df_pseudo_absences <- df_spec_coord %>% dplyr::filter(species == 0) plot(coords.x1 ~ coords.x2, data = df_pseudo_absences) Then you'll have the coordinates of generated pseudo_absences which you can put to plotting function of your choice.