Comments (2)
Hi @francisvolh thanks for attending the webinar. As I mentioned during the session the aniMotum::osar
function is computationally very demanding. Previously I have left my computer to run overnight before the function returns a result. Given the function hasn't crashed or returned an error, I would suggest giving it some more time.
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Thanks @jamesgrecian ! I loved the webinar and the package seems a lot more powerful since it evolved from foiegras. I need to fill some gaps in a few GPS tracks, so I was hoping to use aniMotum. The tutorial said it should take 10-20 times longer than fitting a ssm, which was 30 seconds, so I assumed only 10 minutes or maybe 20 or so. But I guess it was a lot longer. I wonder if how it would behave with my data. Thanks again for the response! and I will come back to you if after 10-20 hours nothing happened! Cheers!
EDIT: It actually took 3.3 hours! so all good now! thanks again!
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Related Issues (20)
- map fit_ssm HOT 1
- when none of the input locations from any of the tracks are on land, route_path fails HOT 1
- installation and library(aniMotum) on M2 MBP HOT 4
- Package `foieGras` archived on CRAN and will be removed from the Tracking CTV HOT 1
- error with map function HOT 1
- When running function fit_ssm : Error in MakeADFun. Is someone familiar? HOT 2
- errors when running fit_ssm in animotum HOT 8
- Issue in installing foieGras package in RStudio HOT 3
- Error in if (p.GL > 0.75) { : missing value where TRUE/FALSE needed HOT 5
- not sure if bug or system issue HOT 3
- Error with sim_fit() using different gradient raster HOT 9
- rep$pdHess : $ operator is invalid for atomic vector — Error when fitting ssm HOT 9
- Error in strsplit(names(map), "_") : non-character argument HOT 1
- Date/time variable not detected in data HOT 6
- Missing packages for Mac M1 binary? HOT 3
- Issue with grad object for sim_fit() HOT 1
- manual download binary version animotum HOT 1
- Error when fitting an example fit_ssm: Error in updateCholesky(L, hessian.random) HOT 3
- Issue when running the Unsupervised Segmentation when fitting the NP Bayesian Models. HOT 1
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