David Maxwell (Cefas) wrote:
> Hi,
>
> Tom and Thierry, Thank you for your advice, the lecture notes are very
> useful. We will try geoRglm but for now regression kriging using the working
> residuals gives sensible answers even though there are some issues with using
> working residuals, i.e. n
I think there may be an issue in your faigcell function for your lines:
Sr1 <- Polygon(cbind(c(nwx,nex,sex,swx,nwx), c(nwy,ney,sey,swy,nwy)))
Sr1 <- Polygons(list(Sr1), datafr[i,1])
lista[i] <- Sr1
try instead:
Sr1 <- list(Polygons(list(Polygon(cbind(c(nwx,nex,sex,swx,nwx),
c(nwy
I think that (at least part of) my problem comes
from being confused about Polygons and SpatialPolygons.
(and [ and [[ !!!)
I have an object with all my polygons:
> class(absUTMpolys)
[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"
> str(absUTMpolys,max.level=2)
Formal class 'SpatialP
On Tue, 8 Apr 2008, Hans Gardfjell wrote:
> Dear R-SIG readers,
>
> The European Union is promoting the use of open source software and
> standards. As a R-user that's certainly positive, but it also creates
> new challenges. One open format that is used by EU is GML, a
> "geographically XML" f
Dear R-SIG readers,
The European Union is promoting the use of open source software and standards.
As a R-user that's certainly positive, but it also creates new challenges. One
open format that is used by EU is GML, a "geographically XML" format. It's
possible to import (or export) a GML-file
Hi,
Tom and Thierry, Thank you for your advice, the lecture notes are very useful.
We will try geoRglm but for now regression kriging using the working residuals
gives sensible answers even though there are some issues with using working
residuals, i.e. not Normally distributed, occasional very
Hi Mathieu,
As a default autoKrige deals with duplicate measurements. This is done
by deleting one of them. It gives a warning message to the user that
observations have been removed. This behavior can be suppressed by
setting 'remove.duplicates = FALSE' in the call to autoKrige, now gstat
wil
Try this code from my dataset:
## Read in var crown circular model
crown.shp <-
"C:/Niccolai/01_PhD/Papers/Paper003/GIS_LAYERS/CB_CRLOCS_ADJ_ALL_PREDINT99_B
uffer.shp"
crown.poly <- readShapePoly(crown.shp)
crown.data <-
read.dbf("C:/Niccolai/01_PhD/Papers/Paper003/GIS_LAYERS/CB_CRLOCS_ADJ_ALL_PR
Dear list,
I want to distribute a set of N circles according to a random distribution
within a set of polygons (N circles within each polygon).
I have an object of class SpatialPolygonsDataFrame with the polygons.
My idea is to use something like:
for (i in 1:length([EMAIL PROTECTED])){
delme
Facundo,
your explenation is very clear.
When I once tried, In addition I found different numbers of point pairs
across the two packages; this could be due to classification of point
pairs with distances exactly on the bin boundary, but I didn't come to a
conclusive feeling about it, back then
Oehler, Friderike (AGPP) wrote:
> Thanks Edzer, I shall try again:
>
> 1) I would like to use spplot to map my factor "TYP" as dots of different
> colours, however the resulting plot uses the same colours for the first and
> last value (10,40). I guess that my use of the "cuts" argument is wrong, b
Hi Pedro,
perhaps if you could paste some sample code, we could talk about
something more concrete...
I understand that you were able to plot empirical variograms using both
variog (geoR) and variogram (gstat).
In both of them you control the maximum distance:
in geoR: using max.dist, or uv
Thanks Edzer, I shall try again:
1) I would like to use spplot to map my factor "TYP" as dots of different
colours, however the resulting plot uses the same colours for the first and
last value (10,40). I guess that my use of the "cuts" argument is wrong, but
I can't find any better:
LAT <-c(-6.
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