On Thu, 15 Nov 2012, peleve wrote:
Thanks Robert,
I was able to do it with the following using the 'gpclib" package:
For completeness, please use rgeos rather than gpclib, which we are
advising against strongly, because of the unclear and very restrictive
licence of its included C code. rgeo
Thanks Robert,
I was able to do it with the following using the 'gpclib" package:
p2 <- as(xy, "gpc.poly")
p1 <- as(maparea, "gpc.poly")
plot(setdiff(p1, p2), poly.args = list(col = 3), add = TRUE)
Did exactly what I was looking for. Pretty simple, actually.
Thanks for inspiring me.
Pete
-
It is hard to help since your code is not reproducible, but there is a way
to do this in the trip package with as.trip.SpatialLinesDataFrame, though
going that route might be unattractive. Please provide the example data
using something like dput(head(lid, n = 10)) rather than a straight dump
like
After some digging, I realized the problem is that the log-likelihood is
simply not exported by one of the internal functions (splm:::sperrorlm).
To fix this simply modify that function. After loading splm type:
fixInNamespace("sperrorlm", "splm")
And manually add "ll=LL" as an element to the "r
Hi,
I have a csv containing animal movement data which is broken into different
behavioral classes (under the heading line_id). I want to plot each of the
trajectories as a spatial line segment. I am trying to do this in a loop, but I
am running in to problems creating a list of Line objects. I
If you have a polygon for the entire area and one for the area of interest
within it; then I think you could use rgeos::gDifference to create the
polygon you need.
Robert
On Thu, Nov 15, 2012 at 6:19 AM, peleve wrote:
> Thank you very much. That worked, mostly!?! But I see the methodology
> n
André,
This looks like a bug. It would be appropriate to contact the author of the
package to report this.
To see the problem, there is no need for Notepad or similar, you can compare
raster("d:/layer.asc")
# with
r
# or
dim(raster("d:/layer.asc"))
dim(r)
To work around it there are probably m
Thank you very much. That worked, mostly!?! But I see the methodology now.
I had been thinking about masks, but I was asking the wrong question I now
realize.
In my real application, using your approach, I still have some unwanted bits
left outside of the polygon. I traced it as a left over piec
Thanks Robert for your comments,
Below the adjusted code which I tested for different extents of the study area.
The problem seems to be with the dataframe2asc part.
The ncols, nrows and cell size of the .asc files created should be identical,
but they are not.
Can anyone try the code and commen
