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  1. So I was playing around with this yesterday, and perhaps I'm walking myself back a bit from my suggestion. I'm analyzing data on a grassland bird species that spends most of its time on the ground (maybe my thoughts would be different if I were analyzing 'cleaner' data from a bird that perches in the open (e.g., a kestrel). Bottom line: my data aren't neat and pretty. If I measure the great-circle distance between the median lat/lon estimate for every twilight that falls between when the bird left the winter grounds and arrived on the breeding grounds, then I get a distance estimate that seems
  2. Sorry, I wasn't clear there--yes you are right--one median. If you had an MCMC model that estimated lat/long at discrete time steps, and you saved 3000 iterations, then from the posteriors (out$sims.list) you could draw a sample of those 3000 estimates of each lat/long (or just use all 3000 of them, of course). I'll see what I can do. Thanks, Jason
  3. Thanks Eldar, Hmm, I appreciate that this is a challenging concept, especially given your point about the distance accumulating even when the animal is stationary (due to the particles mixing around). This is tough. I'm just not familiar enough with particle models. If I was running a standard MCMC Bayesian analysis in JAGS then I'd simply monitor the latitude & longitude median value for each twilight (so that the median for each twilight would have its own distribution of uncertainty). But we certainly don't want 1 million estimates of each median lat/long for each twilight! But if
  4. I would like to estimate (with uncertainty) the cumulative distance traveled by an organism between two specific time periods. Let's say that I used the find.times.distribution function to identify that a tagged bird most likely commenced spring migration on 1 April 2016 and most likely reached its breeding grounds on 23 April 2016. I would like to sum the distance traveled by that bird between every consecutive twilight from 1 April to 23 April. The ideal final result would be something like, "The cumulative spring distance traveled between 1 April and 23 April was median=1034.2 km (95%
  5. Eldar, Definitely not a fault with FLightR but thank you very much for the offer of help. I think this is a Grid x ggmap x ggplot2 problem that I have worked around somehow. I have found that manually loading the grid graphics engine (library(grid)), before running the ggmap command, often makes these errors go away. I have no idea why. Problem solved for now.
  6. I'm getting a new-to-me error message now with the plot_util_distr and map.FLightR.ggmap commands using the newly-updated version of FLightR: devtools::install_github('eldarrak/FLightR') plot_util_distr(Result) or map.FLightR.ggmap(Result) produce the same error Error in get("f", environment(CoordMap$train)) : object 'f' not found Tried installing newest version of ggplot and ggmap devtools::install_github("dkahle/ggmap",force=T) devtools::install_github("hadley/ggplot2",force=T) Tried installing development version of those packages using library(devtools) dev_mode(on
  7. #Package FLightR version 0.4.6 I have been using FlightR to analyze geolocator data for Grasshopper Sparrows, and I have been really pleased. I am wondering, however, if there is a way to export the kernel density results from the plot_util_distr command into ArcGIS. Exporting the median (w/ 95% CRI) lat/long values for use in ArcGIS (via "add XY data") is easy enough: write.csv(run.particle.filter.Results$Results$Quantiles,"GIS_import.csv") But what about exporting the utilization distributions to ArcGIS for replotting and further manipulation? plot_util_distr(particle.
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