I want to bring up 2 modelling problems using LinMark (old and new versions)
1) Deviance issues between windows and Linus platforms
I am running Huggin’s robust design models to estimate survival probabilities in a squirrel population. Using a previous version of LinMark (“Old LinMark”, Sept 2012), I ran both random and Markovian temporary emigration robust design models (see details and deviance scores below):
S(~time*sex*birth_seas*age) Gamma(~random season) p(.) c(session)
S(~time*sex*birth_seas*age) Gamma(~markovian season) p(.) c(session)
As markovian model (the more parameterized model) fits better, lower deviance score is expected. Surprisingly, the markovian model’s deviance value is higher (deviance=6409.324) than the random one (deviance=6355.731).
Using the windows platform (same versions of R and RMark package), the deviance changed to 6340.959.
I ran the same models using the latest version of the Linux-compiled Mark.exe (“New LinMark”, Dec 25). The deviance problem was solved with i) identical deviance score recorded between windows and Linux platforms (deviance of the markovian model=6340.959) and ii) lower deviances of markovian emigration models than random ones.
- Code: Select all
Model: S(~time*sex*birth_seas*age) Gamma''(~randseason) p(.)c(session)
Platform AICc deviance npar
Windows 8392.73 6355.731 266
Old LinMark 8392.73 6355.731 266
New LinMark 8392.73 6355.731 266
Model: S(~time*sex*birth_seas*age)Gamma''(~markovian season)p(.)c(session)
Platform AICc deviance npar
Windows 8386.007 6340.959 269
Old LinMark 8454.372 6409.324 269
New LinMark 8386.007 6340.959 269
I am wondering why the older version of LinMark provided wrong deviance score?
2) The latest LinMark version on a cluster : slow run
According to this previous error, I’ve tried to start again my model selection (the same) from my starting model (strongly parameterized model). But the running time was huge (a week and over, and even some models never ended)!!
The problem occurred with the latest version of LinMark (version of Dec 25). However a recent post of Ewan Cooch indicates an updated version posted December 28 could solve the problem. But I can’t find this version on the website (linux version).
To save up time, I’m using LinMark on a computer cluster. My running jobs were killed since the new mark.exe takes cpus-1 to do so. By setting “threads=1”, the problem is solved (see also viewtopic.php?f=2&t=2692 and viewtopic.php?f=2&t=2743).
But the running time exceeds 46 hours!
Here an example: same model and dataset, different Linux versions
Old version:
-2logL(saturated) = 1421.1968
Effective Sample Size = 1962
Number of function evaluations was 1391 for 793 parameters.
Time for numerical optimization was 6315.33 seconds.
New version:
-2logL(saturated) = 1421.1968
Effective Sample Size = 1962
Number of function evaluations was 291 for 793 parameters.
Time for numerical optimization was 148482.50 seconds
How can I manage this problem?
At last, I’m totally aware of strongly parameterized models at the starting point of my selection. From a conceptual point of view, what’s your opinion on setting a strongly parameterized starting model (makes more biological sense) versus a less parameterized model (makes more mathematical sense?) on maximum likelihood computation and efficiency of model selection?
Thank you for your help!
C. Le Coeur