[Biomod-commits] Need help with some basic questions
Ashley Brooks
ashleycbrooks84 at gmail.com
Wed Feb 22 21:40:35 CET 2012
Hi!
Thanks for your reply, it definitely helps! However I'm still having a
little trouble working through this whole process.
So I have averaged the binary prop.mean.weighted projections (shown below)
and next I want to average the thresholds (from
consensus_cccm80a2a_results) correct? How do I navigate to these
thresholds. And once I have taken the average can I visualize the averaged
output using level.plot?
MyAverage=apply(cbind(Total_consensus_cccm80a2a_Bin[,1,"prob.mean.weighted"],
Total_consensus_had80a2a_Bin[,1,"prob.mean.weighted"]),1,mean)
Thanks so much for your help!
On Wed, Feb 22, 2012 at 12:36 PM, Wilfried Thuiller <
wilfried.thuiller at ujf-grenoble.fr> wrote:
> Dear Ashley,
>
> The weights, thresholds etc are the same because they are estimated from
> the models, not from the projections. The Ensemble.Forecasting function
> does not re-run the models, it just pick up the results from every model
> run in Models, and then estimate the weights and threshold. These weights
> are then used to make the ensemble forecast. Obviously, the weighting
> scheme is not going to change in function of the climate change scenario
> you use because it only relates to the models themselves that are
> independent of the climate change scenarios.
>
> Concerning your second question, of course it could be done easily. BIOMOD
> does not do the for you but this is rather easy to compute.
>
> Averaging needs to be done with the probability values (not the binary
> ones).
>
> For instance, if you want to average your two projections for Cflorida:
>
> load the Total_consensus projections for each GCMs. They should be named
> along these lines
>
> load("MYPATH/Total_consensus_cccm80a2a")
>
> Let's say your second GCM is called hadcm3
>
> load("MYPATH/Total_consensus_hadcm3a2a")
>
> then let's say you want to average the prob.mean.weighted projections.
>
> Each Total_consensus file is a 3-D array. In the second dimension, you
> should just have one column (one species). The third dimension corresponds
> to the different ensemble forecast strategies.
>
> We are going to use the mean function between the two datasets, using the
> apply function.
>
> MyAverage =
> apply(cbind(Total_consensus_cccm80a2a[,1,"prob.mean.weighted"], Total_consensus_hadcm3a2a[,1,"prob.mean.weighted"]),
> 1, mean)
>
> For the threshold, just do the same and take the average.
>
> Does it help?
>
> Wilfried
>
>
>
>
>
>
>
> Le 22 févr. 2012 à 18:18, Ashley Brooks a écrit :
>
> Hi All!
>
> I have a few questions that I'm a little confused about and need some
> clarification:
>
> 1) I have run a few projections and then ensemble forecasts using
> different sets of future climate data. However the weights, thresholds,
> etc for the different runs have the same exact output. Is this normal or
> have I messed something up somewhere? See below:
>
> > Initial.State(Response=my.data[1], Explanatory=my.data[,4:13],
> IndependentResponse=my.data[1], IndependentExplanatory=my.data[,4:13])
>
> > Models(GLM=T, TypeGLM="poly", Test="AIC", GBM=T, No.trees=2000, GAM=T,
> Spline=3, CTA=T, CV.tree=50, ANN=T, CV.ann=2, SRE=T,quant=0.05, FDA=T,
> MARS=T, RF=T, NbRunEval=3, DataSplit=70, Yweights=NULL, Roc=T,
> Optimized.Threshold.Roc=T, Kappa=T, TSS=T, KeepPredIndependent=T,
> VarImport=5, NbRepPA=2, strategy="circles", coor=Coor, distance=2,
> nb.absences=1000)
>
> > Projection(Proj=cccm80a2a[,3:12], Proj.name='cccm80a2a', GLM=T, GBM=T,
> GAM=T, CTA=T, ANN=T, SRE=T, quant=0.05, FDA=F, MARS=T, RF=T, BinRoc=T,
> BinKappa=F, BinTSS=F, FiltRoc=T, FiltKappa=F, FiltTSS=F,
> repetition.models=T)
>
> > Ensemble.Forecasting(Proj.name="cccm80a2a", weight.method='Roc',
> PCA.media=F, binary=T, bin.method='Roc', Test=T, decay=1.6,
> repetition.models=T)
> Cflorida
>
> consensus_cccm80a2a_results
> $Cflorida
> $Cflorida$weights
> ANN CTA GAM GBM GLM MARS FDA RF SRE
> PA1 0.0371 0.0232 0.1522 0.2434 0.0951 0.0594 0 0.3895 0
> PA1_rep1 0.0371 0.0232 0.1522 0.0594 0.0951 0.3895 0 0.2434 0
> PA1_rep2 0.0594 0.0232 0.1522 0.3895 0.0951 0.0371 0 0.2434 0
> PA1_rep3 0.0232 0.0483 0.0951 0.2434 0.0483 0.1522 0 0.3895 0
> PA2 0.0371 0.0232 0.0951 0.1522 0.0594 0.3165 0 0.3165 0
> PA2_rep1 0.1022 0.0232 0.1022 0.2434 0.0371 0.3895 0 0.1022 0
> PA2_rep2 0.0371 0.0232 0.0951 0.1522 0.0594 0.2434 0 0.3895 0
> PA2_rep3 0.0302 0.0302 0.0951 0.1522 0.0594 0.3165 0 0.3165 0
>
> $Cflorida$PCA.median
> model.selected
> PA1 NA
> PA1_rep1 NA
> PA1_rep2 NA
> PA1_rep3 NA
> PA2 NA
> PA2_rep1 NA
> PA2_rep2 NA
> PA2_rep3 NA
>
> $Cflorida$thresholds
> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1
> PA2_rep2 PA2_rep3
> prob.mean 552.4331 476.5176 476.5653 459.1243 485.8907 485.8569
> 428.8626 292.2260
> prob.mean.weighted 567.7707 305.4160 432.0515 423.9614 447.9690 351.9328
> 297.7658 237.1679
> median 599.1970 529.4070 492.7660 582.6600 594.6120 520.6430
> 497.5000 301.3960
> Roc.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
> Kappa.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
> TSS.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
>
> $Cflorida$test.results
> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2
> PA2_rep1 PA2_rep2
> prob.mean 0.9986525 0.9958785 0.9951836 0.9978842 0.9985311
> 0.9991299 0.9975960
> prob.mean.weighted 0.9995932 0.9970734 0.9957514 0.9991808 0.9993503
> 0.9987062 0.9987006
> median 0.9982740 0.9945085 0.9942486 0.9974350 0.9977938
> 0.9975819 0.9970339
> Roc.mean 0.9979492 0.9911780 0.9908023 0.9960763 0.9982514
> 0.9969859 0.9920226
> Kappa.mean 0.9991215 0.9951836 0.9917288 0.9977910 0.9988672
> 0.9991667 0.9926667
> TSS.mean 0.9987797 0.9946638 0.9928955 0.9974322 0.9986610
> 0.9993701 0.9954548
> PA2_rep3
> prob.mean 0.9971582
> prob.mean.weighted 0.9979944
> median 0.9972599
> Roc.mean 0.9948672
> Kappa.mean 0.9934689
> TSS.mean 0.9931836
>
> And then I get the same exact results for other runs as well:
>
> > Projection(Proj=cccm80b2b[,3:12], Proj.name='cccm80b2b', GLM=T, GBM=T,
> GAM=T, CTA=T, ANN=T, SRE=T, quant=0.05, FDA=F, MARS=T, RF=T, BinRoc=T,
> BinKappa=F, BinTSS=F, FiltRoc=T, FiltKappa=F, FiltTSS=F,
> repetition.models=T)
>
> > Ensemble.Forecasting(Proj.name="cccm80b2b", weight.method='Roc',
> PCA.media=F, binary=T, bin.method='Roc', Test=T, decay=1.6,
> repetition.models=T)
> Cflorida
>
> consensus_cccm80b2b_results
> $Cflorida
> $Cflorida$weights
> ANN CTA GAM GBM GLM MARS FDA RF SRE
> PA1 0.0371 0.0232 0.1522 0.2434 0.0951 0.0594 0 0.3895 0
> PA1_rep1 0.0371 0.0232 0.1522 0.0594 0.0951 0.3895 0 0.2434 0
> PA1_rep2 0.0594 0.0232 0.1522 0.3895 0.0951 0.0371 0 0.2434 0
> PA1_rep3 0.0232 0.0483 0.0951 0.2434 0.0483 0.1522 0 0.3895 0
> PA2 0.0371 0.0232 0.0951 0.1522 0.0594 0.3165 0 0.3165 0
> PA2_rep1 0.1022 0.0232 0.1022 0.2434 0.0371 0.3895 0 0.1022 0
> PA2_rep2 0.0371 0.0232 0.0951 0.1522 0.0594 0.2434 0 0.3895 0
> PA2_rep3 0.0302 0.0302 0.0951 0.1522 0.0594 0.3165 0 0.3165 0
>
> $Cflorida$PCA.median
> model.selected
> PA1 NA
> PA1_rep1 NA
> PA1_rep2 NA
> PA1_rep3 NA
> PA2 NA
> PA2_rep1 NA
> PA2_rep2 NA
> PA2_rep3 NA
>
> $Cflorida$thresholds
> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1
> PA2_rep2 PA2_rep3
> prob.mean 552.4331 476.5176 476.5653 459.1243 485.8907 485.8569
> 428.8626 292.2260
> prob.mean.weighted 567.7707 305.4160 432.0515 423.9614 447.9690 351.9328
> 297.7658 237.1679
> median 599.1970 529.4070 492.7660 582.6600 594.6120 520.6430
> 497.5000 301.3960
> Roc.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
> Kappa.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
> TSS.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000
> 500.0000 500.0000
>
> $Cflorida$test.results
> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2
> PA2_rep1 PA2_rep2 PA2_rep3
> prob.mean 0.9986525 0.9958785 0.9951836 0.9978842 0.9985311
> 0.9991299 0.9975960 0.9971582
> prob.mean.weighted 0.9995932 0.9970734 0.9957514 0.9991808 0.9993503
> 0.9987062 0.9987006 0.9979944
> median 0.9982740 0.9945085 0.9942486 0.9974350 0.9977938
> 0.9975819 0.9970339 0.9972599
> Roc.mean 0.9979492 0.9911780 0.9908023 0.9960763 0.9982514
> 0.9969859 0.9920226 0.9948672
> Kappa.mean 0.9991215 0.9951836 0.9917288 0.9977910 0.9988672
> 0.9991667 0.9926667 0.9934689
> TSS.mean 0.9987797 0.9946638 0.9928955 0.9974322 0.9986610
> 0.9993701 0.9954548 0.9931836
>
>
>
> 2) Also I am using two GCM (cccma and had) with two strylines each (A2A
> and B2B) and would like to average the two GCM's to have just two
> predictions (one for the A2A storyline and one for the B2B storyline). My
> question is how do I average the two GCM's? Can I do this in Biomod or do
> I do it in another program? What commands would I use?
>
>
> Any advice or help would be most appreciated!
>
> Thanks,
>
> Ashley
>
> _______________________________________________
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> Biomod-commits at lists.r-forge.r-project.org
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>
>
> --------------------------
> 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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