Open chenyongpeng1 opened 1 day ago
HeleneBlt
commented
1 day ago Hello !
I just sent a commit to correct some issues with the clusters. Could you try with this new version? And if you have the same error, could you try to give us your best traduction : it will be better than an automatic traduction. 🙏
Thanks a lot ! Hélène
chenyongpeng1
commented
1 day ago hello！ I removed and reinstalled the latest version .Unfortunately, this issue still persists, and it occurs during the hyperparameter tuning process.
tuned.gam <- bm_Tuning(model = 'GAM',
+ tuning.fun = 'gam', ## see in ModelsTable
+ bm.options = opt.d@options$GAM.binary.mgcv.gam,
+ do.formula = F,
+ bm.format = myBiomodData ,
+ metric.eval = "TSS")
> Dataset _PA1_allRun
> Tuning parameters...Error in summary.connection(connection) : 链结无效
> Dataset _PA2_allRun
> Tuning parameters...Error in summary.connection(connection) : 链结无效
> Dataset _PA3_allRun
> Tuning parameters...Error in summary.connection(connection) : 链结无效
> Dataset _PA4_allRun
> Tuning parameters...Error in summary.connection(connection) : 链结无效
错误于{: task 1 failed - "找不到对象'tuned.mod'"The connection is not valid.
> myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
+ models.chosen = 'all',
+ em.by = "all",
+ metric.select = c('TSS'),
+ metric.select.thresh = c(0.8),
+ var.import = 3,
+ metric.eval = c('TSS'),
+ em.algo = c( 'EMcv', 'EMca', 'EMwmean'),
+ # em.algo = c('EMca', 'EMwmean'),
+ EMci.alpha = 0.05,
+ EMwmean.decay = 'proportional')
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Build Ensemble Models -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
! all models available will be included in ensemble.modeling
! Ensemble Models will be filtered and/or weighted using validation dataset (if possible). Please use `metric.select.dataset` for alternative options.
> Evaluation & Weighting methods summary :
TSS over 0.8
!!! Removed models using the Full dataset as ensemble models cannot merge repetition dataset (RUN1, RUN2, ...) with Full dataset unless em.by = 'PA+run'.
> mergedData_mergedRun_mergedAlgo ensemble modeling
! Additional projection required for ensemble models merging several pseudo-absence dataset...
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Do Single Models Projection -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Do Single Models Projection -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Do Single Models Projection -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Do Single Models Projection -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
错误于{:
task 1 failed - "task 1 failed - "在为函数“get_predictions”选择方法时计算参数“obj”时出错：链结无效""Hi again,
For the error with bm_tuning, could you try to run :
env <- foreach:::.foreachGlobals
rm(list=ls(name=env), pos=env)and try again ? 🙏
Generally, I will advise you to clean everything and to restart with a new session! 🙈
Is it possible you have some conflict with another get_predictions function?
Thanks a lot Hélène
chenyongpeng1
commented
1 day ago Hi again, please wait a minute! it is restarting with a new session .
chenyongpeng1
commented
21 hours ago HI again， it works well! great!!! Thanks a lot, chenyongpeng
Dear biomod2 Team,
I hope this email finds you well.
I am currently encountering an issue while using the biomod2 package. I have updated to the latest version, biomod2 4.2.6-2, and noticed the addition of the tune.args and cluster functionalities.
BIOMOD_Modelingsection works better than in the previous version. However, I encountered the following error inBIOMOD_EnsembleModeling,and I suspect it might be related to the MaxEnt model.Code used to get the error
myBiomodData -=-=-=-=-=-=-=-=-=-=-=-= BIOMOD.formated.data -=-=-=-=-=-=-=-=-=-=-=- = dir.name = D:/apist sp.name = apis 2406 presences, 0 true absences and 24829 undefined points in dataset 9 explanatory variables bio13 bio14 bio18 bio19 Min. : 0.0 Min. : 0.0 Min. : 0 Min. : 0.0 1st Qu.: 48.0 1st Qu.: 3.0 1st Qu.: 92 1st Qu.: 24.0 Median : 87.0 Median : 11.0 Median : 191 Median : 60.0 Mean : 140.4 Mean : 21.9 Mean : 249 Mean : 143.1 3rd Qu.: 204.0 3rd Qu.: 27.0 3rd Qu.: 319 3rd Qu.: 140.0 Max. :2439.0 Max. :448.0 Max. :4732 Max. :4402.0 bio2 bio3 bio5 elev Min. : 1.000 Min. : 9.456 Min. :-8.82 Min. :-260.0 1st Qu.: 8.528 1st Qu.: 23.358 1st Qu.:21.34 1st Qu.: 138.0 Median :10.907 Median : 35.117 Median :28.80 Median : 347.0 Mean :10.913 Mean : 40.063 Mean :26.85 Mean : 631.9 3rd Qu.:13.238 3rd Qu.: 53.603 3rd Qu.:33.42 3rd Qu.: 799.0 Max. :20.253 Max. :100.000 Max. :48.11 Max. :5970.0 hii_v2geo1 Min. :-128.000 1st Qu.: 1.256 Median : 8.094 Mean : 7.776 3rd Qu.: 17.707 Max. : 62.279 4 Pseudo Absences dataset available ( PA1, PA2, PA3, PA4 ) with 2428 (PA1, PA2), 10000 (PA3, PA4) pseudo absences -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=