[Biomod-commits] Error in ensemble by algorithm
Josep M Serra diaz
pep.bioalerts at gmail.com
Tue Mar 12 20:16:54 CET 2013
Wilfried and colleagues,
This error in ensembling by algorithm comes up again, even though I am not
selecting for a high quality threshold.
I am using last version of R and biomod2
question 1: Why does this warning appear appear?
*
"Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions "*
question 2: the real error that breaks the ensembling is this one. Why is
that?
*Error in roc.default(Obs, Fit, percent = T) : No control observation.*
Thanks a lot for your time and effort,
Pep
Find history hereunder:
myBiomodEM.algo <- BIOMOD_EnsembleModeling (
+ em.by="algo" ,
+ modeling.output =
myBiomodModelOut,
+ chosen.models = 'all',
+ eval.metric = 'TSS',
+ eval.metric.quality.threshold =
c(0.0), # we want them all
+ prob.mean=T,
+ prob.cv = T,
+ prob.ci = T,
+ prob.ci.alpha = 0.05,
+ prob.median = T,
+ committee.averaging = T,
+ prob.mean.weight = F,
+ prob.mean.weight.decay =
'proportional'
+ )
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Build
Ensemble Models
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
! all models available will be included in ensemble.modeling
> Evaluation & Weighting methods summary :
TSS over 0
> GLM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GLM,
Quercushumilis_AllData_RUN2_GLM, Quercushumilis_AllData_RUN3_GLM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GLM ...
> Projecting Quercushumilis_AllData_RUN2_GLM ...
> Projecting Quercushumilis_AllData_RUN3_GLM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> GBM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GBM,
Quercushumilis_AllData_RUN2_GBM, Quercushumilis_AllData_RUN3_GBM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GBM ...
> Projecting Quercushumilis_AllData_RUN2_GBM ...
> Projecting Quercushumilis_AllData_RUN3_GBM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> GAM_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_GAM,
Quercushumilis_AllData_RUN2_GAM, Quercushumilis_AllData_RUN3_GAM
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_GAM ...
> Projecting Quercushumilis_AllData_RUN2_GAM ...
> Projecting Quercushumilis_AllData_RUN3_GAM ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
"SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
> CTA_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN1_CTA,
Quercushumilis_AllData_RUN2_CTA, Quercushumilis_AllData_RUN3_CTA
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN1_CTA ...
> Projecting Quercushumilis_AllData_RUN2_CTA ...
> Projecting Quercushumilis_AllData_RUN3_CTA ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...
> ANN_AllRun ensemble modeling
> TSS
> models kept : Quercushumilis_AllData_RUN3_ANN
! Models projections for whole zonation required...
> Projecting Quercushumilis_AllData_RUN3_ANN ...
> Mean of probabilities...
> Coef of variation of probabilities...
> Median of ptobabilities...
> Confidence Interval...
> 2.5 %
> 97.5 %
> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
"SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS", :
Observed or fited data contains a unique value.. Be carefull with this
models predictions
Error in roc.default(Obs, Fit, percent = T) : No control observation.
In addition: There were 50 or more warnings (use warnings() to see the
first 50)
>
> 2013/3/11 Wilfried Thuiller <wilfried.thuiller at ujf-grenoble.fr>
>
>> Dear Josep,
>>
>> Please make sure to use the latest version (2.1.13). From the what is
>> pasted below, it seems that the minimum threshold to select the models for
>> the ensemble forecast is too high and no models are selected.
>> Try to put 0.4 for instance and run the script again.
>>
>> Best regards,
>> Wilfried
>>
>>
>>
>> Le 10 mars 2013 à 20:20, Josep M Serra diaz a écrit :
>>
>> Dear BIOMODers,
>>
>> I found an error while trying to perform modeling ensemble by algorithm in
>> order to produce an output for each statistical technique
>>
>>
>> Any clue of what does this mean???
>>
>> The strane
>>
>>
>> ########################
>>
>> #ensemble through algorithm
>> myBiomodEM.algo <- BIOMOD_EnsembleModeling (
>> em.by="algo" ,
>> modeling.output = myBiomodModelOut,
>> chosen.models = 'all',
>> eval.metric = 'TSS',
>> eval.metric.quality.threshold =
>> c(0.6),
>> prob.mean=T,
>> prob.cv = T,
>> prob.ci = T,
>> prob.ci.alpha = 0.05,
>> prob.median = T,
>> committee.averaging = T,
>> prob.mean.weight = F,
>> prob.mean.weight.decay =
>> 'proportional'
>> )
>>
>>
>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>> Build Ensemble Models
>> -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
>>
>> ! all models available will be included in ensemble.modeling
>>
>> Evaluation & Weighting methods summary :
>>
>> TSS over 0.6
>>
>>
>> GLM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> GBM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> GAM_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> CTA_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> ANN_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> FDA_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> MARS_AllRun ensemble modeling
>>
>> TSS
>>
>> ! No models kept due to treshold filtering... Ensemble Modeling was
>> skip!
>>
>> RF_AllRun ensemble modeling
>>
>> TSS
>>
>> models kept : Quercusilex_AllData_RUN2_RF
>>
>> ! Models projections for whole zonation required...
>>
>> Projecting Quercusilex_AllData_RUN2_RF ...
>>
>>
>> Mean of probabilities...
>>
>> Coef of variation of probabilities...
>>
>> Median of ptobabilities...
>>
>> Confidence Interval...
>>
>> 2.5 %
>>
>> 97.5 %
>>
>> Comittee averaging...Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR",
>>
>> "SR", "ACCURACY", "BIAS", :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>> :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> Warning in FUN(c("KAPPA", "TSS", "ROC", "FAR", "SR", "ACCURACY", "BIAS",
>> :
>>
>> Observed or fited data contains a unique value.. Be carefull with this
>> models predictions
>>
>> *Error in roc.default(Obs, Fit, percent = T) : No control observation.*
>>
>> In addition: There were 50 or more warnings (use warnings() to see the
>> first 50)
>>
>> warnings()
>>
>> Warning messages:
>> 1: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 2: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 3: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 4: In (forecast_1 * observed_1) + (forecast_0 * observed_0) :
>> NAs produced by integer overflow
>> 5: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 6: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 7: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 8: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 9: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 10: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 11: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 12: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 13: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 14: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 15: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 16: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 17: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 18: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 19: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 20: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 21: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 22: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 23: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 24: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 25: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 26: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 27: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 28: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 29: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 30: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 31: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 32: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 33: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 34: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 35: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 36: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 37: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 38: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 39: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 40: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 41: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 42: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 43: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 44: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 45: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 46: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 47: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 48: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 49: In forecast_0 * observed_0 : NAs produced by integer overflow
>> 50: In forecast_0 * observed_0 : NAs produced by integer overflow
>> _______________________________________________
>> Biomod-commits mailing list
>> Biomod-commits at lists.r-forge.r-project.org
>>
>> https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/biomod-commits
>>
>>
>> --------------------------
>> Dr. Wilfried Thuiller
>> Laboratoire d'Ecologie Alpine, UMR CNRS 5553
>> Université Joseph Fourier
>> BP53, 38041 Grenoble cedex 9, France
>> tel: +33 (0)4 76 51 44 97
>> fax: +33 (0)4 76 51 42 79
>>
>> Email: wilfried.thuiller at ujf-grenoble.fr
>> Personal website: http://www.will.chez-alice.fr
>> Team website: http://www-leca.ujf-grenoble.fr/equipes/emabio.htm
>>
>> ERC Starting Grant TEEMBIO project:
>> http://www.will.chez-alice.fr/Research.html
>> FP6 European EcoChange project: http://www.ecochange-project.eu
>>
>>
>>
>>
>>
>>
>>
>>
>
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