Thanks so much for creating this package, and for giving up so much of your time to this group.
I’ve read in your help files and messages to other people that gaps in the data weren’t too much of a problem when working with continuous time, but I was wondering if there was any limit to this. I’m working with domestic cats, tracking each for a minimum of three weeks. While for some cats this was continuous, for others this 21 days of tracking was spread out over two or three calendar months. Due to fieldwork limitations, some of my cats were tracked for two weeks, then there was a gap of a month, then they were tracked again for another two or three weeks. I suspect this has led to higher levels of variation in the latter sections of my variograms, and makes my default variogram.fit() a little high (I’m reluctant to manually decrease them as I’d like to keep the process as similar as possible between all my cats).
1. Is this level of gap acceptable for ctmm?
2.
Variogram.fit sometimes only plots the
initial part of the tracking (see the variogram.fit() left vs plot(variogram) right). I can't tell if variogram.fit() is just operating on that first section, or the entire tracking period?
3.
With the ctmm.select() function, I’ve noticed in
some of your help files that you recommend anisotrophic models over their
isotrophic counterparts. I was wondering whether this has a limit? At what
difference in AIC should I revert to choosing isotrophic models, if at all?
Thanks so much in advance for your help,
Nell
> summary(akde.1iso)
$DOF
area bandwidth
511.9064 561.1849
$CI
low est high
area (square meters) 4121.442 4503.138 4901.494
> summary(akde.1aniso)
$DOF
area bandwidth
510.2839 561.0077
$CI
low est high
area (square meters) 4116.126 4497.976 4896.521