Hi All!<div><br></div><div>I have a few questions that I'm a little confused about and need some clarification:</div><div><br></div><div>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:</div>
<div><br></div><div>> Initial.State(Response=my.data[1], Explanatory=my.data[,4:13], IndependentResponse=my.data[1], IndependentExplanatory=my.data[,4:13])</div><div><br></div><div>> 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)</div>
<div><br></div><div>> 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)</div>
<div><br></div><div><div>> Ensemble.Forecasting(Proj.name="cccm80a2a", weight.method='Roc', PCA.media=F, binary=T, bin.method='Roc', Test=T, decay=1.6, repetition.models=T)</div><div>Cflorida </div>
<div><br></div><div> consensus_cccm80a2a_results </div><div>$Cflorida</div><div>$Cflorida$weights</div><div> ANN CTA GAM GBM GLM MARS FDA RF SRE</div><div>PA1 0.0371 0.0232 0.1522 0.2434 0.0951 0.0594 0 0.3895 0</div>
<div>PA1_rep1 0.0371 0.0232 0.1522 0.0594 0.0951 0.3895 0 0.2434 0</div><div>PA1_rep2 0.0594 0.0232 0.1522 0.3895 0.0951 0.0371 0 0.2434 0</div><div>PA1_rep3 0.0232 0.0483 0.0951 0.2434 0.0483 0.1522 0 0.3895 0</div>
<div>PA2 0.0371 0.0232 0.0951 0.1522 0.0594 0.3165 0 0.3165 0</div><div>PA2_rep1 0.1022 0.0232 0.1022 0.2434 0.0371 0.3895 0 0.1022 0</div><div>PA2_rep2 0.0371 0.0232 0.0951 0.1522 0.0594 0.2434 0 0.3895 0</div>
<div>PA2_rep3 0.0302 0.0302 0.0951 0.1522 0.0594 0.3165 0 0.3165 0</div><div><br></div><div>$Cflorida$PCA.median</div><div> model.selected</div><div>PA1 NA</div><div>PA1_rep1 NA</div>
<div>PA1_rep2 NA</div><div>PA1_rep3 NA</div><div>PA2 NA</div><div>PA2_rep1 NA</div><div>PA2_rep2 NA</div><div>PA2_rep3 NA</div><div><br></div><div>
$Cflorida$thresholds</div><div> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1 PA2_rep2 PA2_rep3</div><div>prob.mean 552.4331 476.5176 476.5653 459.1243 485.8907 485.8569 428.8626 292.2260</div>
<div>prob.mean.weighted 567.7707 305.4160 432.0515 423.9614 447.9690 351.9328 297.7658 237.1679</div><div>median 599.1970 529.4070 492.7660 582.6600 594.6120 520.6430 497.5000 301.3960</div><div>Roc.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div>
<div>Kappa.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div><div>TSS.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div><div><br></div><div>
$Cflorida$test.results</div><div> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1 PA2_rep2</div><div>prob.mean 0.9986525 0.9958785 0.9951836 0.9978842 0.9985311 0.9991299 0.9975960</div>
<div>prob.mean.weighted 0.9995932 0.9970734 0.9957514 0.9991808 0.9993503 0.9987062 0.9987006</div><div>median 0.9982740 0.9945085 0.9942486 0.9974350 0.9977938 0.9975819 0.9970339</div><div>Roc.mean 0.9979492 0.9911780 0.9908023 0.9960763 0.9982514 0.9969859 0.9920226</div>
<div>Kappa.mean 0.9991215 0.9951836 0.9917288 0.9977910 0.9988672 0.9991667 0.9926667</div><div>TSS.mean 0.9987797 0.9946638 0.9928955 0.9974322 0.9986610 0.9993701 0.9954548</div><div> PA2_rep3</div>
<div>prob.mean 0.9971582</div><div>prob.mean.weighted 0.9979944</div><div>median 0.9972599</div><div>Roc.mean 0.9948672</div><div>Kappa.mean 0.9934689</div><div>TSS.mean 0.9931836</div>
</div><div> </div><div>And then I get the same exact results for other runs as well:</div><div><br></div><div>> 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)</div>
<div><br></div><div>> Ensemble.Forecasting(Proj.name="cccm80b2b", weight.method='Roc', PCA.media=F, binary=T, bin.method='Roc', Test=T, decay=1.6, repetition.models=T)</div><div>Cflorida </div>
<div><br></div><div> consensus_cccm80b2b_results </div><div>$Cflorida</div><div>$Cflorida$weights</div><div> ANN CTA GAM GBM GLM MARS FDA RF SRE</div><div>PA1 0.0371 0.0232 0.1522 0.2434 0.0951 0.0594 0 0.3895 0</div>
<div>PA1_rep1 0.0371 0.0232 0.1522 0.0594 0.0951 0.3895 0 0.2434 0</div><div>PA1_rep2 0.0594 0.0232 0.1522 0.3895 0.0951 0.0371 0 0.2434 0</div><div>PA1_rep3 0.0232 0.0483 0.0951 0.2434 0.0483 0.1522 0 0.3895 0</div>
<div>PA2 0.0371 0.0232 0.0951 0.1522 0.0594 0.3165 0 0.3165 0</div><div>PA2_rep1 0.1022 0.0232 0.1022 0.2434 0.0371 0.3895 0 0.1022 0</div><div>PA2_rep2 0.0371 0.0232 0.0951 0.1522 0.0594 0.2434 0 0.3895 0</div>
<div>PA2_rep3 0.0302 0.0302 0.0951 0.1522 0.0594 0.3165 0 0.3165 0</div><div><br></div><div>$Cflorida$PCA.median</div><div> model.selected</div><div>PA1 NA</div><div>PA1_rep1 NA</div>
<div>PA1_rep2 NA</div><div>PA1_rep3 NA</div><div>PA2 NA</div><div>PA2_rep1 NA</div><div>PA2_rep2 NA</div><div>PA2_rep3 NA</div><div><br></div><div>
$Cflorida$thresholds</div><div> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1 PA2_rep2 PA2_rep3</div><div>prob.mean 552.4331 476.5176 476.5653 459.1243 485.8907 485.8569 428.8626 292.2260</div>
<div>prob.mean.weighted 567.7707 305.4160 432.0515 423.9614 447.9690 351.9328 297.7658 237.1679</div><div>median 599.1970 529.4070 492.7660 582.6600 594.6120 520.6430 497.5000 301.3960</div><div>Roc.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div>
<div>Kappa.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div><div>TSS.mean 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000 500.0000</div><div><br></div><div>
$Cflorida$test.results</div><div> PA1 PA1_rep1 PA1_rep2 PA1_rep3 PA2 PA2_rep1 PA2_rep2 PA2_rep3</div><div>prob.mean 0.9986525 0.9958785 0.9951836 0.9978842 0.9985311 0.9991299 0.9975960 0.9971582</div>
<div>prob.mean.weighted 0.9995932 0.9970734 0.9957514 0.9991808 0.9993503 0.9987062 0.9987006 0.9979944</div><div>median 0.9982740 0.9945085 0.9942486 0.9974350 0.9977938 0.9975819 0.9970339 0.9972599</div><div>
Roc.mean 0.9979492 0.9911780 0.9908023 0.9960763 0.9982514 0.9969859 0.9920226 0.9948672</div><div>Kappa.mean 0.9991215 0.9951836 0.9917288 0.9977910 0.9988672 0.9991667 0.9926667 0.9934689</div><div>TSS.mean 0.9987797 0.9946638 0.9928955 0.9974322 0.9986610 0.9993701 0.9954548 0.9931836</div>
<div><br></div><div><br></div><div><p class="MsoNormal" style="margin-bottom:0.0001pt"><br></p><p class="MsoNormal" style="margin-bottom:0.0001pt">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?</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt"><br></p><p class="MsoNormal" style="margin-bottom:0.0001pt">Any advice or help would be most appreciated!</p><p class="MsoNormal" style="margin-bottom:0.0001pt">Thanks,</p>
<p class="MsoNormal" style="margin-bottom:0.0001pt">Ashley</p>
<p></p></div>