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Hi all,<br>
<br>
I guess Ensemble.Forecasting and Ensemble.Forecasting.raster
functions work well now (v308). Let me know if not !<br>
<br>
All the best,<br>
<br>
Damien<br>
<br>
On 30/05/2012 16:34, Visser, V, Dr <a class="moz-txt-link-rfc2396E" href="mailto:vernonvisser@sun.ac.za"><vernonvisser@sun.ac.za></a>
wrote:
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<p class="MsoNormal">I am getting an error while trying to run
Ensemble.Forecasting:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span
style="font-size:10.0pt;font-family:"Lucida
Console";color:black;mso-fareast-language:EN-ZA">Error
in array(STATS, dims[perm]) : attempt to set an attribute on
NULL<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:10.0pt;font-family:"Lucida
Console";color:black;mso-fareast-language:EN-ZA">In
addition: Warning messages:<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:10.0pt;font-family:"Lucida
Console";color:black;mso-fareast-language:EN-ZA">1: In
is.na(x) : is.na() applied to non-(list or vector) of type
'NULL'<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:10.0pt;font-family:"Lucida
Console";color:black;mso-fareast-language:EN-ZA">2: In
max(cumDim[cumDim <= lstats]) :<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-size:10.0pt;font-family:"Lucida
Console";color:black;mso-fareast-language:EN-ZA"> no
non-missing arguments to max; returning -Inf<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I have used both my own data and the
example data that comes with BIOMOD and still get this error.
My code is as follows using the BIOMOD data:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">data(Sp.Env)<o:p></o:p></p>
<p class="MsoNormal">data(CoorXY)<o:p></o:p></p>
<p class="MsoNormal">Initial.State(Response = Sp.Env[,13],
Explanatory = Sp.Env[,4:10], sp.name="sp1")<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Models(GAM = T,Spline = 4,<o:p></o:p></p>
<p class="MsoNormal"> NbRunEval = 2,<o:p></o:p></p>
<p class="MsoNormal"> DataSplit = 70,<o:p></o:p></p>
<p class="MsoNormal"> NbRepPA=1,<o:p></o:p></p>
<p class="MsoNormal"> strategy="random",<o:p></o:p></p>
<p class="MsoNormal"> coor=alldata2[,12:13],<o:p></o:p></p>
<p class="MsoNormal"> nb.absences=5000,<o:p></o:p></p>
<p class="MsoNormal"> Roc = T,<o:p></o:p></p>
<p class="MsoNormal"> VarImport=T<o:p></o:p></p>
<p class="MsoNormal"> )<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">data(Future1)<o:p></o:p></p>
<p class="MsoNormal">Projection(Proj=Future1[,4:10],
Proj.name="sp1", ANN=F,CTA=F,GAM=T,GBM=F,GLM=F,MARS=F,<o:p></o:p></p>
<p class="MsoNormal">
FDA=F,RF=F,SRE=F,repetition.models=T, compress="xz",FiltRoc=T)<o:p></o:p></p>
<p class="MsoNormal">Ensemble.Forecasting(Proj.name="sp1",ANN=F,CTA=F,GAM=T,GBM=F,GLM=F,MARS=F,<o:p></o:p></p>
<p class="MsoNormal"> FDA=F,RF=F,SRE=F,
weight.method='Roc',decay=1.6,binary=T,bin.method="Roc",Test=T,repetition.models=T)<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">traceback() gives the following
information:<o:p></o:p></p>
<pre><span style="font-family:"Lucida Console";color:black">7: array(STATS, dims[perm])<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">6: aperm(array(STATS, dims[perm]), order(perm))<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">5: apply((x > y), 2, as.integer)<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">4: FUN(x, aperm(array(STATS, dims[perm]), order(perm)), ...)<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">3: sweep(data.matrix(ProbData), 2, CutOffdata, FUN2)<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">2: BinaryTransformation(ARRAY.tot[, i, "median"], median(ths[[3]][TotalModels]))<o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black">1: Ensemble.Forecasting(Proj.name = paste("clunum", c(x * 10 + y + <o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black"> 1), sep = ""), ANN = F, CTA = F, GAM = T, GBM = F, GLM = F, <o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black"> MARS = F, FDA = F, RF = F, SRE = F, weight.method = "Roc", <o:p></o:p></span></pre>
<pre><span style="font-family:"Lucida Console";color:black"> decay = 1.6, binary = T, bin.method = "Roc", Test = T, repetition.models = T)<o:p></o:p></span></pre>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span
style="font-family:"Arial","sans-serif";color:gray;mso-fareast-language:EN-ZA">Vernon
Visser<br>
Post-doctoral Research Associate<br>
Centre of Excellence for Invasion Biology<br>
Department of Botany and Zoology<br>
Natural Sciences Building<br>
Private Bag X1<br>
University of Stellenbosch<br>
Matieland <br>
7602<br>
South Africa<o:p></o:p></span></p>
<p class="MsoNormal"><span
style="font-family:"Arial","sans-serif";mso-fareast-language:EN-ZA"><a
moz-do-not-send="true"
href="https://sites.google.com/site/vernonvisserpostdoc/"><span
style="color:blue">https://sites.google.com/site/vernonvisserpostdoc/</span></a></span><span
style="mso-fareast-language:EN-ZA"><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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