[R-sig-Geo] placing spdf back into adehabiatHR estUDm
Edzer Pebesma
edzer.pebesma at uni-muenster.de
Thu Sep 8 22:50:05 CEST 2011
Anthony, your script fails for me at the following point:
> ver <- getverticeshr(ud, percent=95, standardize = TRUE, whi = id(ltraj))
Error in getverticeshr.default(x[[i]], percent, ida = names(x)[i], unin, :
no method provided for this class
In general, it is easier for someone on the list to answer your question
if you send a minimal script that reproduces the problem -- I'm not sure
the 134 lines were all needed.
If you want to construct objects of a certain class at the low level,
using the slot() function, you should first fully understand how the
objects are organized. Documentation is sparse (as source code is
there), but
class?SpatialPixelsDataFrame
will help you as a start. A likely cause may be that you assume y
increases when you go along the pixels in the @data slot (as image()
assumes the first row is the most South-bound row), but this is not the
case for the sp grid classes.
On 09/08/2011 09:53 PM, Anthony Fischbach wrote:
> I have worked from the script that you provided in your previous posting to
> address the question,
> "Is there a means to convert the SpatialPixelDataFrame back into an estUDm?
> "
> My modifications to your script are noted by my initials, ***ASF**.
>
> I have extended your script to work with an estUDm created by the BRB
> function.
>
> In delving into the estUDm object I noticed two odd things.
> 1) estUD objects within the estUDm do not have names assigned when they are
> created by the BRB function.
> 2) each estUD object within the estUDm has a full listing of the D values
> from all animals listed in the estUDm.
>
> I was able to clip the estUD's using the habitat grid. By use of the slot()
> function I was able to placed the 'clipped' utilization distribution in the
> form of a spatial pixel data frame back into the estUDm class object.
> However, the resultant estUDm raster appears to have the y coordinates
> reversed.
>
> The modified script is listed below.
>
> ## Load the data
> library(adehabitatHR)
> library(adehabitatLT)
> data(puechabonsp)
>
> ## Store the relocations
> loc <- puechabonsp$relocs
>
> ## and the map
> elev <- puechabonsp$map
>
> ## have a look at the data
> head(as.data.frame(loc))
> ## the first column of this data frame is the ID
>
> ## Look at the map
> image(elev)
>
> ## Now we build a "fake" habitat map (1 = habitat and 0 = non habitat) from
> the elevation map
> fullgrid(elev) <- TRUE
> hab <- elev
> hab[[1]] <- as.numeric(!is.na(hab[[1]]))
>
> ## show this habitat map (yellow is habitat)
> image(hab)
>
> ## Estimation of UD for the four animals, using
> ## the map hab as the grid
> ud <- kernelUD(loc[,1], grid=hab)
> ###ASF###
> ###ASF### try with BRB
> ###ASF### first make ltraj object
> ###ASF### Cast the Dates as POSIXct dates
> loc$DT <-as.POSIXct(strptime(loc$Date, format='%y%m%d' ))
> ###ASF### Create an ltraj trajectory object.
> ltraj <- as.ltraj(coordinates(loc), loc$DT, id = loc$Name, burst = loc$Name,
> typeII = TRUE)
> ###ASF### Assigne parameter values for BRB
> ###ASF### Parameters for the Biased Random Bridge Kernel approach
> tmax <- 5*(24*60*60) + 1 ## set the maximum time between locations to be
> just more than 5 day
> lmin <- 0.1 ## locations less than 0.1 meters apart are considered inactive.
> ## Set very very low to ensure that the acvitivy variable is used.
> hMin <- 100 ## arbitrarily set to be same as hab grid cell resolution
>
> vv2<- BRB.likD(ltraj, Dr=c(1,50000), Tmax = tmax, Lmin = lmin, habitat =
> NULL) ##
> ud <-BRB(ltraj, D =vv2, Tmax =tmax, Lmin = lmin, hmin = hMin, habitat =
> NULL,
> grid = hab, b = TRUE, extent = 0, tau = 24*60*60 )
> ## ud is an object of the class estUDm
> ## get volumetric contours
> ver <- getverticeshr(ud, percent=95, standardize = TRUE, whi = id(ltraj))
> ###ASF### !! PROBLEM !!
> # > str(ud)
> # List of 4
> # $ :Formal class 'estUD' [package "adehabitatHR"] with 9 slots
> # .. ..@ h :List of 3
> # .. .. ..$ values:List of 2
> # .. .. .. ..$ hmin: num 100
> # .. .. .. ..$ D :List of 4
> # .. .. .. .. ..$ Brock: num 4.26
> # .. .. .. .. ..$ Calou: num 1
> # .. .. .. .. ..$ Chou : num 1.86
> # .. .. .. .. ..$ Jean : num 6.21
> # ... further str(ud) results omitted
> ###ASF### 2 Problems apparent here:
> ###ASF### 1) no names have been assigned to the items in the ud list;
> ###ASF### 2) the list of D values has been inflated such that a full listing
> of D values is given under each animal, rather than just one for each animal
> ###ASF### address names in ud by assigning them to be the same as the ids in
> ltraj
> names(ud) <- id(ltraj)
> ## Convert it to SpatialPixelsDataFrame:
> udspdf <- estUDm2spixdf(ud)
>
> ## udspdf is an object of class SpatialPixelsDataFrame
> ## have a look
> mimage(udspdf)
>
> ## Convert the original map to fullgrid (i.e. SpatialGridDataFrame)
> fullgrid(udspdf) <- TRUE
>
> ## and the same for the original habitat map (here, it is not needed,
> ## as the map hab is already fullgrid, but it might be required on you data)
> fullgrid(hab)<-TRUE
>
> ## The two maps have the same dimensions:
> ## > length(udspdf[[1]])
> ## [1] 6636
> ## > length(hab[[1]])
> ## [1] 6636
>
> ## Then, you just have to multiply each column of udspdf by the habitat
> variable:
> resu <- lapply(1:ncol(udspdf), function(i) {
> udspdf[[i]] * hab[[1]] / sum(udspdf[[i]] * hab[[1]])
> })
> resu <- as.data.frame(resu)
> names(resu) <- names(udspdf at data)
>
> ## and define it as data slot for udspdf
> udspdf at data <- resu
>
> ## Have a look at the data (after conversion to SpatialPixelsDataFrame):
> fullgrid(udspdf) <- FALSE
> mimage(udspdf)
>
> ## Note that Brock and Calou, the UD have sharp limits
> ###ASF### Yes I see this.
>
> ###ASF### [2011 August 05]: Questions for ClementCalenge:
> ###ASF### ???? How can we extract home-range contours now that the home
> range estimates are of class SpatialPixelsDataFrame?
> ###ASF### In an effort to return access the function getverticeshr, cast the
> 'clipped' udspdf back into the estUDm object
> ###ASF### Try stepping through each animal in the ud and replacing the @data
> element.
>
> ud2 <- ud ##make copy of the ud
> for (i in 1:length(ud2)){
> idStr <- id(ltraj[i])
> print(paste(i, "th animal is ", idStr, sep=""))
> temp <- udspdf[idStr]
> udf <- temp at data
> names(udf) = "dens"
> print(summary(slot(ud2[[i]], "data"))) ## print summary of the ud object
> print(summary(udf)) ## print summary of the udspdf object
> slot(ud2[[i]], "data", check = TRUE) <- udf ## Replaced the data slot with
> the clipped data
> }
> ver2 <- getverticeshr(ud2, percent=95, standardize = TRUE, whi = id(ltraj))
> ###ASF### !!PROBLEM!! the coordinates are twisted.
> ###ASF### Have a look at the resulting ud
> image(ud2)
>
>
>
>
> -----
> Tony Fischbach, Wildlife Biologist
> Walrus Research Program
> Alaska Science Center
> U.S. Geological Survey
> 4210 University Drive
> Anchorage, AK 99508-4650
>
> AFischbach at usgs.gov
> http://alaska.usgs.gov/science/biology/walrus
> --
> View this message in context: http://r-sig-geo.2731867.n2.nabble.com/Walruses-and-adehabitatHR-class-estUDm-exclusion-of-non-habitat-pixels-and-summary-over-all-animals-tp6497315p6773497.html
> Sent from the R-sig-geo mailing list archive at Nabble.com.
>
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--
Edzer Pebesma
Institute for Geoinformatics (ifgi), University of Münster
Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251
8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de
http://www.52north.org/geostatistics e.pebesma at wwu.de
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