<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Hi Fernando,<div class=""><br class=""></div><div class=""> Thanks for your interest in BaSTA and for sticking to it despite the convergence problems. Sometimes the problems might have to do with the specification of times of birth and death, or issues with the covariates. Maybe if you want, send me a sample of your data and I’ll try to find out what could be affecting convergence. In general the Siler model should converge. About running the sexes in separate models, that would give you roughly the same answer, so you can separate the sexes. </div><div class=""><br class=""></div><div class=""> Best,</div><div class=""><br class=""></div><div class=""> Fernando</div><div class=""><div class="">
<div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div dir="auto" style="caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0); letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div>_________________________________<br class=""><br class="">Fernando Colchero<br class="">Associate Professor<br class="">Department of Mathematics and Computer Science<br class="">Interdisciplinary Center on Population Dynamics<br class=""><br class="">Tlf. +45 65 50 23 24<br class=""><a href="mailto:colchero@imada.sdu.dk" class="">Email colchero@imada.sdu.dk</a><br class="">Web www.sdu.dk/staff/colchero<br class="">Pers. web www.colchero.com<br class="">Adr. Campusvej 55, 5230, Odense, Dk<br class=""><br class="">University of Southern Denmark<br class="">_________________________________</div></div></div>
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<div><br class=""><blockquote type="cite" class=""><div class="">On 29 Apr 2019, at 13:38, Fernando Arce Gonzalez <<a href="mailto:fernando.arcegonzalez@utas.edu.au" class="">fernando.arcegonzalez@utas.edu.au</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div id="divtagdefaultwrapper" dir="ltr" style="font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; font-size: 12pt; font-family: Calibri, Helvetica, sans-serif;" class=""><div style="margin-top: 0px; margin-bottom: 0px;" class="">Good afternoon:</div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><br class=""></div><div style="margin-top: 0px; margin-bottom: 0px;" class="">I ran roughly a year ago some models with BaSTA to some data I have around. I was using a subset of the data (truncated to not include all years that spans 1984:2017). </div><div style="margin-top: 0px; margin-bottom: 0px;" class="">The model specs were: model: GO, shape: bathtub, covs structure: fussed, and the only covariate was sex.</div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><br class=""></div><div style="margin-top: 0px; margin-bottom: 0px;" class="">This configuration never converged, but the results were consistent with the species ecology, pointing the big differences in the mortality hazzard rate curve shapes between sexes.</div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><br class=""></div><div style="margin-top: 0px; margin-bottom: 0px;" class="">I initially though it could be just a matter of running longer chains, but some months ago I guy I know told me that he tried to fit a Siler model in a similar dataset (same species, less data, using his own code), but it was not very stable. So recently I have gone back to it and check some models output</div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div style="margin-top: 0px; margin-bottom: 0px;" class=""><span style="font-size: 12pt;" class=""> Settings</span><br class=""></div><p style="margin-top: 0px; margin-bottom: 0px;" class=""></p><div class=""> mod1 mod2 mod3</div><div class="">niter 200000 400000 1000000</div><div class="">burnin 197000 397000 990000</div><div class="">thinning 20 20 20</div><div class="">nsim 2 2 2</div><div class=""><br class=""></div><div class="">and this is the potential scale reduction factor <span style="font-size: 12pt;" class="">(for clarity</span><span style="font-size: 12pt;" class="">, I split it into Females and males</span><span style="font-size: 12pt;" class="">):</span></div><div class=""><br class=""></div><div class="">females:</div><div class=""><div class=""> mod1 mod2 mod3</div><div class="">a0.f 0.9982876 1.1061251 0.9999246</div><div class="">a1.f 1.5281294 1.1214647 0.9992686</div><div class="">c.f 0.9971761 4.7816934 1.1177012</div><div class="">b0.f 1.6284601 2.8833760 1.1821780</div><div class="">b1.f 3.8061436 2.4666239 1.2615155</div><div class=""><br class=""></div><div class="">Males</div><div class=""> mod1 mod2 mod3</div><div class="">a0.m 1.0421634 1.0033493 1.1864240</div><div class="">a1.m 1.0001222 0.9985314 1.3429417</div><div class="">c.m 1.0671795 0.9966957 2.4725801</div><div class="">b0.m 1.0945299 0.9968903 2.5274575</div><div class="">b1.m 1.0742245 0.9966948 2.3528640</div><div class=""><br class=""></div><span style="font-family: Calibri, Helvetica, sans-serif, EmojiFont, "Apple Color Emoji", "Segoe UI Emoji", NotoColorEmoji, "Segoe UI Symbol", "Android Emoji", EmojiSymbols; font-size: 16px;" class="">for the females, 400.000 iterations gave worse convergence than 200.000 (unexpected). 1 million iterations, as expected, gets the better values and close to converge. On the other side, for males, 400.000 better than 200.000 (but both offered convergence), but 1 million goes pretty bad. To add some 'fun', I have checked a model with the same structured with 20.000 iterations and very similar data (I included those individuals of unknown sex as a third sex category, very very few) and it converged as a champ.</span><br class=""></div><div class=""><span style="font-family: Calibri, Helvetica, sans-serif, EmojiFont, "Apple Color Emoji", "Segoe UI Emoji", NotoColorEmoji, "Segoe UI Symbol", "Android Emoji", EmojiSymbols; font-size: 16px;" class=""><br class=""></span></div><div class="">To add some info of the dataset, it is roughly 20.000 individuals, and I have only used data from 1984 to 2009. It is pretty well balanced in terms of covariates (close to 50% males 50% females). <span style="font-size: 12pt;" class="">Roughly 1/3 of the animals had never been re-observed, which is fair as they are marked when they are weaned</span><span style="font-size: 12pt;" class=""> and nobody will check for them till next breeding season, when they still may be or not at the colony as they won't became adults until at least age 3 (females). Also, that cames from DataCheck:</span></div><div class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div class=""><span style="font-size: 12pt;" class=""><div class="">*DataSummary*</div><div class="">- Number of individuals = 20,504 </div><div class="">- Number with known birth year = 18,261 </div><div class="">- Number with known death year = 0 </div><div class="">- Number with known birth</div><div class=""> AND death years = 0 </div><div class=""><br class=""></div><div class="">- Total number of detections</div><div class=""> in recapture matrix = 38,362 </div><div class=""><br class=""></div><div class="">- Earliest detection time = 1985 </div><div class="">- Latest detection time = 2009 </div><div class="">- Earliest recorded birth year = 1985 </div><div class="">- Latest recorded birth year = 2005 </div><br class=""></span></div><div class=""><span style="font-size: 12pt;" class="">and this too (I have modify DataCheck locally to avoid printing row numbers, I just want the number of rows with issues, not to have printed >1800 row numbers):</span></div><div class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div class=""><span style="font-size: 12pt;" class=""><div class="">17 rows have observations that occur before the year of birth</div><div class="">Observations that pre-date year of birth have been removed.</div><div class="">18320 rows have a one in the recapture matrix in the birth year</div><div class="">135 rows have caterogical covariates adding to 0</div><div class="">These records have been removed from the Dataframe</div><br class=""></span></div><div class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div class=""><span style="font-size: 12pt;" class="">Given that, any suggestion? I did try to run more chains, shorter, with no luck either. I cannot find those model objects</span><span style="font-size: 12pt;" class="">. So I wonder it has to do with the data or with the model itself? Now I have more computer free time to run these models (they take a lot of time) so I want to give it another go. I was wondering about splittiing the dataset by sex before modelling, and run different, independent modelling for each sex. I think that would be sensible, as males and females are quite different and are expected to have different curves of mortality<span class="Apple-converted-space"> </span></span>hazard<span style="font-size: 12pt;" class=""> rates, but not sure still, specially looking to the males pattern (1 million iters being </span><span style="font-size: 12pt;" class="">worse than 200.000</span><span style="font-size: 12pt;" class="">)</span></div><div class=""><span style="font-size: 12pt;" class=""><span class=""><br class=""></span></span></div><div class=""><span style="font-size: 12pt;" class=""><span class="">Best regards and thanks in advance:</span></span></div><div class=""><span style="font-size: 12pt;" class=""><span class="">Fer</span></span></div><div class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div class=""><span style="font-size: 12pt;" class="">This is how the table of the factors looks from R</span></div><div class=""><span style="font-size: 12pt;" class=""><br class=""></span></div><div class=""><span style="font-size: 12pt;" class=""><div class="">> scaleRedFactor</div><div class=""> mod1 mod2 mod3</div><div class="">a0.f 0.9982876 1.1061251 0.9999246</div><div class="">a0.m 1.0421634 1.0033493 1.1864240</div><div class="">a1.f 1.5281294 1.1214647 0.9992686</div><div class="">a1.m 1.0001222 0.9985314 1.3429417</div><div class="">c.f 0.9971761 4.7816934 1.1177012</div><div class="">c.m 1.0671795 0.9966957 2.4725801</div><div class="">b0.f 1.6284601 2.8833760 1.1821780</div><div class="">b0.m 1.0945299 0.9968903 2.5274575</div><div class="">b1.f 3.8061436 2.4666239 1.2615155</div><div class="">b1.m 1.0742245 0.9966948 2.3528640</div><div class="">pi.1984 0.9989825 0.9969165 0.9991626</div><br class=""></span></div><br class=""><p style="margin-top: 0px; margin-bottom: 0px;" class=""></p></div><div style="margin-top: 0px; margin-bottom: 0px; caret-color: rgb(0, 0, 0); font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration: none; font-size: 10pt; line-height: 10pt; font-family: Calibri, sans-serif;" class=""><br class=""><br class="">University of Tasmania Electronic Communications Policy (December, 2014).<span class="Apple-converted-space"> </span><br class="">This email is confidential, and is for the intended recipient only. 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