search for: kernelud

Hi,  I am looking to perform a kernel density analysis in R, following the script that a friend of mine is using successfully. However, unlike for her, when I enter >kud<-kernelUD(data, h="LSCV"), R crashes and closes every time. This does not happen when I perform a different kernel analysis (>kud<-kernelUD(data)).. I have tried to un-install and re-install R, but it still crashes.. The script I am using is as follows:  library(adehabitatHR) library(mapt...

...s corresponding CRS >Gael_WGS84_WorldM <- readShapePoints("900_Gael_WGS84_WorldM", proj4string=CRS("+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")) ># Remove all the columns except the name of the animal to use the kernelUD function. My data looks like this: > head(Gael_WGS84_WorldM[-c(2:25)]) coordinates Name 0 (-683614, 4459280) Gael 1 (-769563, 4516660) Gael 2 (-721607, 4431310) Gael 3 (-683613, 4459290) Gael 4 (-765266, 4502750) Gael 5 (-683602, 4459280) Gael Coordinate Reference System (CRS) arguments...

Hello, I am working through examples of generating Home Range using the adehabitatHR package. Everything is going fine until I load the code for the kernelUD function, it looks like this; > data(puechabonsp) > kud <- kernelUD(puechabonsp$relocs[,1], h="href") > kud I then get an error of; Error: could not find function "kernelUD" Any thoughts on what I am doing wrong to get this error, should that function be built...

Hello, I'm new to R and am trying to use the adehabitat package for home range and habitat selection analysis for some animal radiotelemetry data sets I have, but I can't get the home range functions (mcp & kernelUD) to work with my data. I think that my problem is that I don't properly understand how the package uses data frames and factors, and I may not be defining the id factor correctly. I've started out by trying to imitate the code in the homerange.r demo that comes with the ade package. The co...

...ne vignette per package). Both their use and the theory underlying these functions are described there. To access it, type: vignette("packagename") * the home range estimation methods have been homogenized and return classes compliant with the classes of the package sp: - the functions kernelUD, kernelbb, BRB and kernelkc all return objects of class "estUDm", which are lists of objects of class "estUD". The class estUD extends the class SpatialPixelsDataFrame. - the function clusthr and LoCoH return objects of class "MCHu", which are lists of SpatialPolygo...

...ne vignette per package). Both their use and the theory underlying these functions are described there. To access it, type: vignette("packagename") * the home range estimation methods have been homogenized and return classes compliant with the classes of the package sp: - the functions kernelUD, kernelbb, BRB and kernelkc all return objects of class "estUDm", which are lists of objects of class "estUD". The class estUD extends the class SpatialPixelsDataFrame. - the function clusthr and LoCoH return objects of class "MCHu", which are lists of SpatialPolygo...

...0, Lmin = 36, hmin = 100 ) result[ , i ] <- kernel.area( BRBdat, unout = "km2" ) } #> Warning in kernel.area(BRBdat, unout = "km2"): The grid is too small to allow the estimation of home-range #> for the following value of percent: 70,75,80,85,90,95. You should rerun kernelUD with a larger extent parameter #> Warning in kernel.area(BRBdat, unout = "km2"): The grid is too small to allow the estimation of home-range #> for the following value of percent: 30,35,40,45,50,55,60,65,70,75,80,85,90,95. You should rerun kernelUD with a larger extent parameter #&g...

Dear all, I have a data similar to this: myframe<- data.frame (ID=c("Ernie", "Ernie","Ernie","Ernie"), Timestamp=c("24.09.2012 08:00", "24.09.2012 09:00", "24.09.2012 10:00", "25.09.2012 10:00"), Longitude=c("8.481","8.482","8.483","8.481"),

...maximum likelihood approach (recommended in Horn et al., Ecology, in press). Examples of the help page demonstrate the use and interest of this function. Comparison between kernelbb and the Visual basic algorithm provided in the paper of Horn et al. returned consistent results. * The function kernelUD has also been improved. It now takes more than 50% less time than the previous version. In addition the "grid" argument of this function, now also allows a list of objects of class "asc" to be passed as grid where the UD should be estimated. Happy testing, Cl?ment Calenge....

...maximum likelihood approach (recommended in Horn et al., Ecology, in press). Examples of the help page demonstrate the use and interest of this function. Comparison between kernelbb and the Visual basic algorithm provided in the paper of Horn et al. returned consistent results. * The function kernelUD has also been improved. It now takes more than 50% less time than the previous version. In addition the "grid" argument of this function, now also allows a list of objects of class "asc" to be passed as grid where the UD should be estimated. Happy testing, Cl?ment Calenge....

...bers I am calculating kernel home-ranges and would like to visualize them with the function image(). The xy-values consist of longitude and latitude data. example:       x        y 36154.97 355143.0   R-code: xy<-hares[,c("x","y")] id<-hares[,"date"]   (ud1 <- kernelUD(xy, id)) ud2 <- getvolumeUD(ud1) image(ud2)   My problem is that the image of the contours is cut at the edges. I tried to include the largest and smalest xy values with xlim and ylim. The contours shift on the Graphics Device of R, but the contours are still cut.   Can anybody help with this pr...