[Biomod-commits] Need help with some basic questions
Ashley Brooks
ashleycbrooks84 at gmail.com
Thu Feb 23 17:22:51 CET 2012
Hey Wilfried and all,
So check to see if I did this correctly
> Ensemble.Forecasting(Proj.name="cccm80a2a", weight.method='Roc',
PCA.media=F, binary=T, bin.method='Roc', Test=T, decay="proportional",
repetition.models=T, final.model.out=TRUE)
> Ensemble.Forecasting(Proj.name="had80a2a", weight.method='Roc',
PCA.media=F, binary=T, bin.method='Roc', Test=T, decay="proportional",
repetition.models=T, final.model.out=TRUE)
> load("proj.cccm80a2a/Total_consensus_cccm80a2a_Bin")
> load("proj.had80a2a/Total_consensus_had80a2a_Bin")
>
MyAverageA2A=apply(cbind(Total_consensus_cccm80a2a_Bin[,1,"prob.mean.weighted"],
Total_consensus_had80a2a_Bin[,1,"prob.mean.weighted"]),1,mean)
> MyThresA2A=apply(cbind(consensus_cccm80a2a_results[[1]][[3]][3,2],
consensus_had80a2a_results[[1]][[3]][3,2]),1,mean)
> MyAverageBinA2A=BinaryTransformation(MyAverageA2A, MyThresA2A)
or would it be:
>
MyAverageA2A=apply(cbind(Total_consensus_cccm80a2a_Bin[,1,"prob.mean.weighted"],
Total_consensus_had80a2a_Bin[,1,"prob.mean.weighted"]),1,mean)
> MyThresA2A=mean(consensus_cccm80a2a_results[[1]][[3]][3,2])
> MyAverageBinA2A=BinaryTransformation(MyAverageA2A, MyThresA2A)
does it make a difference?
Also when I did the level.plot
> level.plot(MyAverageA2A, Coor) returns a visual and
> level.plot(MyAverageBinA2A, Coor) does not return a visual.....why would
this be?
Thank you for your time and consideration! It is most appreciated.
Ashley
On Thu, Feb 23, 2012 at 2:21 AM, Wilfried Thuiller <
wilfried.thuiller at ujf-grenoble.fr> wrote:
>
> 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)
>
>
> The total consensus projections are simply the average of the weighted
> probabilities means of all repetitions and pseudo-absence projections.
> Just do the same for the threshold:
>
> MyTre =
> mean(consensus_cccm80a2a_results $Cflorida$Cflorida$thresholds$prob.mean.weighted)
>
> then, simply run the BinaryTransformation function
>
> MyAverageBin = BinaryTransformation(MyAverage, MyTre)
>
> Something you might want to do as well is to remove the projections from
> the models made on 100% of the data. Here when you run the Ensemble
> Forecasting, you have the projections from the model trained on 100% of the
> data and and the ones trained on 70% of the data. Better to avoid mixing
> the two on the final outcome you want to look at.
>
> Just add one option into the Ensemble Forecasting option:
>
> Ensemble.Forecasting(Proj.name="cccm80a2a", weight.method='Roc',
> PCA.media=F, binary=T, bin.method='Roc', Test=T, decay="proportional",
> repetition.models=T, final.model.out=TRUE)
> ### note there was a mistake in the help file, it was said that
> final.model.out=TRUE keeps the final model in. This is the way around. I
> have just updated the help file.
>
> Additionnaly, I will also recommend to use the decay = "proportional". The
> weights are awarded for each method proportionaly to their evaluation
> scores. The advantage is that the discrimination is more fair than with the
> decay. In the latter case, close scores can strongly diverge in the weights
> they are awarded, when the proportional method will consider them as being
> fairly similar in prediction quality and award them a similar weight.
>
>
> For the level.plot, once you have made your averaging, you should have a
> vector of probability or binary values
> You should also have your coordinates from somewhere
>
> level.plot(MyAverage, MyCoordinates)
>
>
> Cheers,
> Wilfried
>
>
>
>
> 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
>>
>> _______________________________________________
>> 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
>>
>>
>>
>>
>>
>>
>>
>>
> _______________________________________________
> 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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