<html><body><div style="color:#000; background-color:#fff; font-family:arial, helvetica, sans-serif;font-size:10pt"><div><font face="'times new roman', 'new york', times, serif">Hi Wilfried,</font></div><div><font face="'times new roman', 'new york', times, serif"><br></font></div><div><font face="'times new roman', 'new york', times, serif">Here's my code:</font></div><div><font face="'times new roman', 'new york', times, serif"><br></font></div><div><font face="'times new roman', 'new york', times, serif"><div>library(BIOMOD)</div><div>bradypus<- read.csv("bradypus_swd.csv", header=TRUE)</div><div>background<- read.csv("background.csv", header=TRUE)</div><div>names(bradypus)[2]<-paste("x")</div><div>names(bradypus)[3]<-paste("y")</div><div>presence_absence<- matrix(nrow = (nrow(bradypus) + nrow(background)), ncol = 1)</div><div>for (i in 1:nrow(bradypus)) {presence_absence[i,1]<- 1}</div><div>for (i in
(nrow(bradypus)+1):nrow(presence_absence)) {presence_absence[i,1]<- 0}</div><div>colnames(presence_absence)<- c("Sp1")</div><div>Sp.Env<- rbind(bradypus, background)</div><div>Sp.Env<- cbind(Sp.Env, presence_absence)</div><div>Sp.Env$ecoreg = as.factor(Sp.Env$ecoreg)</div><div>Resp.Var<- Sp.Env[,18]</div><div>Resp.Var<- as.data.frame(Resp.Var)</div><div>Expl.Var <- Sp.Env[,4:17]</div><div>LatLong<- Sp.Env[, 2:3]</div><div>head(Sp.Env, 10)</div><div>summary(Sp.Env)</div><div>Initial.State(Response = Resp.Var, Explanatory = Expl.Var)</div><div><br></div></font></div><div style="font-family: arial, helvetica, sans-serif; "><div style="font-size: 12pt; font-family: 'times new roman', 'new york', times, serif; ">>>Perhaps you could copy paste the Initial.State call you made</div><div style="font-family: 'times new roman', 'new york', times, serif; font-size: 12pt; " class="yui_3_2_0_55_133044491230861"><br></div><div
style="font-family: 'times new roman', 'new york', times, serif; font-size: 12pt; " class="yui_3_2_0_55_133044491230861">See above<br><br>>>The first ten lines of your observed data (head(MyDate). </div><div style="font-family: 'times new roman', 'new york', times, serif; font-size: 12pt; " class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861"><span class="Apple-tab-span" style="white-space:pre"> </span> x y cld6190_ann dtr6190_ann ecoreg<br><div class="yui_3_2_0_55_133044491230861"> 1 -65.4000 <span class="Apple-tab-span" style="white-space:pre"> </span>-10.3833 76 <span class="Apple-tab-span" style="white-space:pre"> </span> 104 10</div><div
class="yui_3_2_0_55_133044491230861">2 -65.3833 <span class="Apple-tab-span" style="white-space:pre"> </span>-10.3833 76 104 10</div><div class="yui_3_2_0_55_133044491230861">3 -65.1333 <span class="Apple-tab-span" style="white-space:pre"> </span>-16.8000 57 114 10</div><div class="yui_3_2_0_55_133044491230861">4 -63.6667 <span class="Apple-tab-span" style="white-space:pre"> </span>-17.4500 57 112 10</div><div class="yui_3_2_0_55_133044491230861">5 -63.8500 <span class="Apple-tab-span" style="white-space:pre"> </span>-17.4000
57 113 10</div><div class="yui_3_2_0_55_133044491230861">6 -64.4167<span class="Apple-tab-span" style="white-space:pre"> </span>-16.0000 58 111 10</div><div class="yui_3_2_0_55_133044491230861">7 -63.1667 <span class="Apple-tab-span" style="white-space:pre"> </span>-17.8000 57 110 8</div><div class="yui_3_2_0_55_133044491230861">8 -56.7333 <span class="Apple-tab-span" style="white-space:pre"> </span> -2.6000 77
82 10</div><div class="yui_3_2_0_55_133044491230861">9 -59.1333 <span class="Apple-tab-span" style="white-space:pre"> </span> -3.7000 83 86 10</div><div class="yui_3_2_0_55_133044491230861">10 -60.0833 <span class="Apple-tab-span" style="white-space:pre"> </span> -3.1333 82 85 10</div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861"><div class="yui_3_2_0_55_133044491230861">frs6190_ann h_dem pre6190_ann pre6190_l10 pre6190_l1 pre6190_l4 pre6190_l7</div><div class="yui_3_2_0_55_133044491230861">1
2 121 46 41 84 54 3</div><div class="yui_3_2_0_55_133044491230861">2 2 121 46 40 84 54 3</div><div class="yui_3_2_0_55_133044491230861">3 1 211
65 56 129 58 34</div><div class="yui_3_2_0_55_133044491230861">4 3 363 36 33 71 27 13</div><div class="yui_3_2_0_55_133044491230861">5 3 303 39 35
77 29 15</div><div class="yui_3_2_0_55_133044491230861">6 0 166 54 48 107 45 23</div><div class="yui_3_2_0_55_133044491230861">7 0 430 33 30 61 29
15</div><div class="yui_3_2_0_55_133044491230861">8 0 12 60 24 69 96 42</div><div class="yui_3_2_0_55_133044491230861">9 0 23 58 30 96 95 25</div><div class="yui_3_2_0_55_133044491230861">10
0 32 62 39 84 98 29</div></div><div class="yui_3_2_0_55_133044491230861"><br></div><div><div> tmn6190_ann tmp6190_ann tmx6190_ann vap6190_ann Sp1</div><div>1 <span class="Apple-tab-span" style="white-space:pre"> </span>192 <span class="Apple-tab-span" style="white-space:pre"> </span>266 <span class="Apple-tab-span" style="white-space:pre"> </span>337 <span class="Apple-tab-span" style="white-space:pre"> </span>279 1</div><div>2
<span class="Apple-tab-span" style="white-space:pre"> </span>192 <span class="Apple-tab-span" style="white-space:pre"> </span>266 <span class="Apple-tab-span" style="white-space:pre"> </span>337 <span class="Apple-tab-span" style="white-space:pre"> </span>279 1</div><div>3 <span class="Apple-tab-span" style="white-space:pre"> </span>140 <span class="Apple-tab-span" style="white-space:pre"> </span>244 <span class="Apple-tab-span" style="white-space:pre"> </span>321 <span class="Apple-tab-span" style="white-space:pre"> </span>221 1</div><div>4 <span class="Apple-tab-span" style="white-space:pre"> </span>135 <span class="Apple-tab-span" style="white-space:pre"> </span>229
<span class="Apple-tab-span" style="white-space:pre"> </span><span class="Apple-tab-span" style="white-space:pre"> </span>307 <span class="Apple-tab-span" style="white-space:pre"> </span>202 1</div><div>5 <span class="Apple-tab-span" style="white-space:pre"> </span>134 <span class="Apple-tab-span" style="white-space:pre"> </span>229 <span class="Apple-tab-span" style="white-space:pre"> </span>306 <span class="Apple-tab-span" style="white-space:pre"> </span>202 1</div><div>6 <span class="Apple-tab-span" style="white-space:pre"> </span>156 <span class="Apple-tab-span" style="white-space:pre"> </span>252 <span class="Apple-tab-span" style="white-space:pre"> </span>326
<span class="Apple-tab-span" style="white-space:pre"> </span>235 1</div><div>7 <span class="Apple-tab-span" style="white-space:pre"> </span>153 <span class="Apple-tab-span" style="white-space:pre"> </span>245 <span class="Apple-tab-span" style="white-space:pre"> </span>326 <span class="Apple-tab-span" style="white-space:pre"> </span>217 1</div><div>8 <span class="Apple-tab-span" style="white-space:pre"> </span>229 <span class="Apple-tab-span" style="white-space:pre"> </span>275 <span class="Apple-tab-span" style="white-space:pre"> </span> 335 <span class="Apple-tab-span" style="white-space:pre"> </span>306 1</div><div>9 <span
class="Apple-tab-span" style="white-space:pre"> </span>220 <span class="Apple-tab-span" style="white-space:pre"> </span>271 <span class="Apple-tab-span" style="white-space:pre"> </span>328 <span class="Apple-tab-span" style="white-space:pre"> </span>301 1</div><div>10 <span class="Apple-tab-span" style="white-space:pre"> </span>224 <span class="Apple-tab-span" style="white-space:pre"> </span>272 <span class="Apple-tab-span" style="white-space:pre"> </span>328 <span class="Apple-tab-span" style="white-space:pre"> </span>300 1</div><div style="font-family: 'times new roman', 'new york', times, serif; font-size: 12pt; "><br></div></div><br><font size="3">>>summary( MyData)</font><br><font size="3">>>I doubt there are in the good
format.</font></div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861"><div class="yui_3_2_0_55_133044491230861"> x y cld6190_ann </div><div class="yui_3_2_0_55_133044491230861">Min. :-94.72 Min. :-55.025 Min. :32.00 </div><div class="yui_3_2_0_55_133044491230861">1st Qu.:-69.92 1st Qu.:-24.538 1st Qu.:54.00 </div><div class="yui_3_2_0_55_133044491230861">Median :-62.98 Median :-11.825 Median :64.00 </div><div class="yui_3_2_0_55_133044491230861">Mean :-62.05 Mean :-13.560 Mean :63.15 </div><div class="yui_3_2_0_55_133044491230861">3rd Qu.:-54.12 3rd Qu.: -2.425 3rd Qu.:74.00 </div><div class="yui_3_2_0_55_133044491230861">Max. :-34.92 Max.
: 23.125 Max. :84.00 </div><div class="yui_3_2_0_55_133044491230861"> </div><div class="yui_3_2_0_55_133044491230861"> dtr6190_ann ecoreg frs6190_ann h_dem </div><div class="yui_3_2_0_55_133044491230861"> Min. : 49.0 10 :4590 Min. : 0.00 Min. : 0 </div><div class="yui_3_2_0_55_133044491230861"> 1st Qu.: 98.0 9 :1781 1st Qu.: 0.00 1st Qu.: 101 </div><div class="yui_3_2_0_55_133044491230861"> Median :110.0 5
:1232 Median : 1.00 Median : 249 </div><div class="yui_3_2_0_55_133044491230861"> Mean :112.4 8 : 790 Mean : 21.04 Mean : 579 </div><div class="yui_3_2_0_55_133044491230861"> 3rd Qu.:125.0 2 : 620 3rd Qu.: 11.00 3rd Qu.: 598 </div><div class="yui_3_2_0_55_133044491230861"> Max. :178.0 12 : 495 Max. :235.00 Max. :5610 </div><div class="yui_3_2_0_55_133044491230861"> (Other): 608 </div><div class="yui_3_2_0_55_133044491230861"> pre6190_ann pre6190_l10 pre6190_l1 pre6190_l4
</div><div class="yui_3_2_0_55_133044491230861"> Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 </div><div class="yui_3_2_0_55_133044491230861"> 1st Qu.: 23.00 1st Qu.: 16.00 1st Qu.: 23.00 1st Qu.: 21.00 </div><div class="yui_3_2_0_55_133044491230861"> Median : 41.00 Median : 37.00 Median : 47.00 Median : 38.00 </div><div class="yui_3_2_0_55_133044491230861"> Mean : 41.43 Mean : 38.96 Mean : 50.99 Mean : 46.93 </div><div class="yui_3_2_0_55_133044491230861"> 3rd Qu.: 58.00 3rd Qu.: 55.00 3rd Qu.: 81.00 3rd Qu.: 73.00 </div><div class="yui_3_2_0_55_133044491230861"> Max. :204.00 Max. :250.00 Max. :185.00 Max. :188.00 </div><div
class="yui_3_2_0_55_133044491230861"> </div><div class="yui_3_2_0_55_133044491230861"> pre6190_l7 tmn6190_ann tmp6190_ann tmx6190_ann </div><div class="yui_3_2_0_55_133044491230861"> Min. : 0.00 Min. :-110.0 Min. : 1.0 Min. :101.0 </div><div class="yui_3_2_0_55_133044491230861"> 1st Qu.: 4.00 1st Qu.: 74.0 1st Qu.:183.0 1st Qu.:296.0 </div><div class="yui_3_2_0_55_133044491230861"> Median : 14.00 Median : 159.0 Median :246.0 Median :320.0 </div><div
class="yui_3_2_0_55_133044491230861"> Mean : 29.75 Mean : 128.3 Mean :214.9 Mean :301.6 </div><div class="yui_3_2_0_55_133044491230861"> 3rd Qu.: 45.00 3rd Qu.: 196.0 3rd Qu.:261.0 3rd Qu.:331.0 </div><div class="yui_3_2_0_55_133044491230861"> Max. :222.00 Max. : 229.0 Max. :282.0 Max. :362.0 </div><div class="yui_3_2_0_55_133044491230861"> </div><div class="yui_3_2_0_55_133044491230861"> vap6190_ann Sp1 </div><div class="yui_3_2_0_55_133044491230861"> Min. : 1.0 Min. :0.00000
</div><div class="yui_3_2_0_55_133044491230861"> 1st Qu.:155.0 1st Qu.:0.00000 </div><div class="yui_3_2_0_55_133044491230861"> Median :225.0 Median :0.00000 </div><div class="yui_3_2_0_55_133044491230861"> Mean :205.3 Mean :0.01147 </div><div class="yui_3_2_0_55_133044491230861"> 3rd Qu.:269.0 3rd Qu.:0.00000 </div><div class="yui_3_2_0_55_133044491230861"> Max. :310.0 Max. :1.00000 </div><br><font size="3">>If you also fear this is because of the categorical variable, try without it.</font></div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">But since some models in BIOMOD can handle categorical variables, then would it be wise to exclude it? Doesn't that alter the results?<br><br><font size="3">> First of all, does someone have to go through the whole procedure of typing the
code above for each model >and for each variable, and to use the predict() function each time? According to the tutorial ?once the models >are trained (i.e. calibrated), a standard prediction is made. Then, one of the variables is randomized and a >new prediction is made.? I thought that BIOMOD was following this procedure automatically. If yes, how? If >not, how can I load CTA, ANN (and all the rest of the models in general) the same way glm is loaded in the >code above (i.e. glm(Sp281 ~ Var1 + Var2 + Var3 + Var4 + Var5 + Var6 + Var7, data=Sp.Env))?</font><br><br><font size="3">>>Obviously not, or I will not have spent ten years coding BIOMOD?</font></div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">Ok, but this unfairly ironic reply does not answer my question: what should I do instead of following the procedure described above? You might have spent ten years coding BIOMOD but
how much time have you actually spent writing the manuals? I am afraid they are quite badly written! I first tried to learn BIOMOD a year ago by reading the 2008 manuals; I struggled for 2-3 weeks before giving up. I switched to WEKA and learned how to operate the software through the command line in one day by using the manual, even though I had never programmed in Java before. Three weeks ago I downloaded MaxEnt together with the manual and it just took me some days to learn how to analyse the outputs, not to mention that it was a piece of cake to run the models.</div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">BIOMOD is such a powerful and innovative tool, and I have always found very exciting the idea of mastering it so as to have a series of different models running my predictions silmuntaneously. I truly believe that it is much better than MaxEnt and WEKA. I have been following your
scientific work since the day I found out about BIOMOD but I still haven't managed to make it work properly. The 2012 manual is as bad as those of 2008... </div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">There is only one simple thing I'm asking for: if you aren't really willing to upgrade the manuals, then can I please ask you to be more supportive in the mailing list? I have received incomplete answers in the past as well and that does not help neither the users nor you.</div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">Cheers,</div><div class="yui_3_2_0_55_133044491230861"><br></div><div class="yui_3_2_0_55_133044491230861">Andreas<br><br> </div> </div> </div></body></html>