Ok,
May I ask you another question?
When I use parameter number for stplot, how does it decide to split plots by
number?
The orange mark on the top of each plot shows me that it isn't by equal time
intervals. I guess it is by equal object count?
Is there a way to indicate time periods on top of
Hi,
I want to do maps of changes for NDVI for several days. When I use a map 1
for day 1, map 2 for day 2, map 3 for day 3 etc… in a same page, differences
between maps were not visible. For this reason, I want to do maps of
changes using differences between pixels value (or NDVI value) . Thus
Hi Komine,
By coordinates of the image, do you mean coordinates of bottom left - top
right coordinates?
Or do you mean spatial locations within a certain extent that you call
image?
In the first case, you could use raster crop function to work on a subset of
the total image that covers all the c
Mathieu,
you may want to look into cut methods for Date, e.g. in your example:
xyplot(y_sref~x_sref|cut(time,4), as.data.frame(ciotat.t))
On 08/29/2011 10:31 AM, Mathieu Rajerison wrote:
> Ok,
>
> May I ask you another question?
>
> When I use parameter number for stplot, how does it decide
Thank you for the answer, Roger! Sys.getenv("GISRC") let to a tmp file, but
that was a copy of the .grassrc6 in my home directory. Editing .grassrc6 fixed
it and now GRASS and R can talk to each other again.
Again, thank you for the help!
Martin
On 28 Aug 2011, at 6:16 , Roger Bivand wrote
great!
could you in short describe what you mean by rescaling?
is this rescaling also needed when comparing this 'modified-pcf' relative to an
envelope of 'modified-pcfs' which resulted from simulations of an appropriate
nullmodel (i.e. random non-overlapping circles)?
I have indeed to be caut
Thank you Mathieu,
I'm sorry to respond late to your feedback. I take into account your
suggestions and links provided to answer you again this weekend because I am
a long journey and I will not have internet.
Komine
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How are you?
I am working on research using gstat package fitting universal kriging
models using fit.variogram() with one covariate.
I have been looking for features in gstat to get estimates of beta,
coefficients of covariate (intercept and a coefficient of covariate).
Since I could not find
