Dear Clement,
Clément Calenge-2 wrote:
>
> It is hard to find the solution of the problem without any reproducible
> example...
> It seems the the values of the UD are bounded to 100, but I cannot find
> why without sample data and corresponding code.
> Some key questions would allow to find out what happens here: what is
> the resolution of the map? does the problem occur if the resolution of
> the map changes? does the problem still occur if only the part of the
> trajectory where there is a problem is kept for the analysis? etc...
> Could you send a sample burst where the problem occurs, as well as your
> script?
>
I have isolated some of the problematic points and tested at different
resolutions, and different smoothing parameters. The results do not seem
to be affected by any of these changes, and even when running the analysis
with only 2 points in question, the errors still occur.
I have attached two .csv files with data from 2 different animals. Each
file has 3 bursts with 2 points in each, between which the problematic
bridges were created. The file B611 contains data from the image I attached
in my previous post. The script I have used is copied below. (I apologize
for the image quality of the output, but I am not sure how to create better
images in R, and usually export the ASCII files to view the data).
####
data<-read.csv("B611.csv")
x<-data$x
y<-data$y
xy<-cbind(x,y)
datetime<-data$datetime
id<-data$id
brst<-burst
da<-as.POSIXct(strptime(as.character(data$datetime), tz="GMT", "%d/%m/%Y
%T"))
trj<-as.ltraj(xy,da,id,burst=brst,typeII=T,slsp="remove")
sig2<-5
liker(trj, sig2, byburst=T,rangesig1=c(0, 30),plotit=T)
##sig1 values as calculated from the original full data set are 3.87 for the
file B589 and 5.86 for B611
sig1<-5.86
xybox <- data.frame(x=c(min(na.omit(data$x))-2500,
max(na.omit(data$x))+2500),
y=c(min(na.omit(data$y))-2500,
max(na.omit(data$y))+2500))
## My standard resolution is 25 x 25 m pixels
http://www.nabble.com/file/p21985869/B589.csv B589.csv
http://www.nabble.com/file/p21985869/B611.csv B611.csv
asc <- ascgen(xybox, cellsize=25)
bb<-kernelbb(trj, sig1, sig2, byburst=T, grid=asc)
images<-image(bb[[1]]$UD)
images<-image(bb[[2]]$UD)
images<-image(bb[[3]]$UD)
####
Best regards,
Andrea
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