Pradel Lambda models do not converge

Hi all,
I've just started working with MARK and seem to have walked immediately into a wall. I promise I have attempted to figure things out myself by (continuing to) RTFM and searching the forums, but it looks like I've missed something pretty crucial.
My dataset consists of Robust Design style mark recapture data of a single mouse species across 3 sites and 12 primary sessions (each with 4 or 5 secondary sessions), totaling ~4k captures of ~700 individuals over 3 years.
I'm primarily interested in examining differences in survival between sexes and grids, and differences in fecundity between grids.
I got as far as getting my data into RMark (file here, if anyone wants a play - https://www.dropbox.com/s/xyeaxwzkcmyo7ng/mouseb.txt) and tested it using a null 'Robust' model, which seems to work;
However, any of the Pradel models I've tried all seem to fail, with an "ERROR-- Numerical convergence never reached" message from Mark.
eg.
"RDPdGHuggins" will run, but all the beta estimates are identical with 0 error.
I had seen a suggestion to use initial values from simpler models, but given that the null model won't run I'm a bit stumped and suspect there's a deeper issue that I'm failing to appreciate.
I'm probably falling down simply because I'm not sufficiently up to speed on the theory, which I am trying to correct, but if anyone could spare a minute to have a look and perhaps point me in the right direction I'd be extremely grateful.
Cheers,
Tom
I've just started working with MARK and seem to have walked immediately into a wall. I promise I have attempted to figure things out myself by (continuing to) RTFM and searching the forums, but it looks like I've missed something pretty crucial.
My dataset consists of Robust Design style mark recapture data of a single mouse species across 3 sites and 12 primary sessions (each with 4 or 5 secondary sessions), totaling ~4k captures of ~700 individuals over 3 years.
I'm primarily interested in examining differences in survival between sexes and grids, and differences in fecundity between grids.
I got as far as getting my data into RMark (file here, if anyone wants a play - https://www.dropbox.com/s/xyeaxwzkcmyo7ng/mouseb.txt) and tested it using a null 'Robust' model, which seems to work;
- Code: Select all
mouseRMb=import.chdata("mouseb.txt", header=TRUE)
mouse.process=process.data(mouseRMb, model="Robust", begin.time=1, groups=c("grid","sex","agemarked."),
time.intervals=c(0,0,0,123,0,0,0,0,74,0,0,0,0,83,0,0,0,0,68,0,0,0,0,125,0,0,0,0,76,0,0,0,0,82,0,0,0,0,72,0,0,0,0,125,0,0,0,0,69,0,0,0,0,94,0,0,0,0))
mouse.dll=make.design.data(mouse.process)
test1=mark(mouse.process, mouse.dll)
However, any of the Pradel models I've tried all seem to fail, with an "ERROR-- Numerical convergence never reached" message from Mark.
eg.
- Code: Select all
mouse2.process=process.data(mouseRMb, model="RDPdfClosed", begin.time=1, groups=c("grid","sex","agemarked."),
time.intervals=c(0,0,0,123,0,0,0,0,74,0,0,0,0,83,0,0,0,0,68,0,0,0,0,125,0,0,0,0,76,0,0,0,0,82,0,0,0,0,72,0,0,0,0,125,0,0,0,0,69,0,0,0,0,94,0,0,0,0))
mouse2.dll=make.design.data(mouse2.process)
test2=mark(mouse2.process, mouse2.dll)
"RDPdGHuggins" will run, but all the beta estimates are identical with 0 error.
I had seen a suggestion to use initial values from simpler models, but given that the null model won't run I'm a bit stumped and suspect there's a deeper issue that I'm failing to appreciate.
I'm probably falling down simply because I'm not sufficiently up to speed on the theory, which I am trying to correct, but if anyone could spare a minute to have a look and perhaps point me in the right direction I'd be extremely grateful.
Cheers,
Tom