[Biomod-commits] Need help with some basic questions
Ashley Brooks
ashleycbrooks84 at gmail.com
Wed Feb 22 18:18:16 CET 2012
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 storylines 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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