(a side note: not italian, but *spanish* ;) )
Piero
On 24 February 2013 20:51, Edzer Pebesma edzer.pebe...@uni-muenster.dewrote:
Erin, see
https://stat.ethz.ch/**pipermail/r-sig-geo/2013-**February/017577.htmlhttps://stat.ethz.ch/pipermail/r-sig-geo/2013-February/017577.html
(where the
Hi Erin,
...did you load the library?
Piero
On 20 February 2013 01:30, Hodgess, Erin hodge...@uhd.edu wrote:
Hi!
I'm working with some spatio-temporal data and was trying to use vgmST.
However, it says that the function is not there. I have the spacetime
library running.
Any
01:53 PM, Piero Campalani wrote:
Dear list,
I was wondering if there is a way to automatically compute x-validation
with the new spacetime objects (as is in gstat::gstat.cv).
Looking through the available functions it does not look so, thus I bet I
just need to manually run it. ?
I
, unless you have duplicate
measurements.
On 01/10/2013 03:20 PM, Piero Campalani wrote:
Dear list,
I am predicting PM measurements on a spatiotemporal grid with monthly
intervals in time.
At modeling time, I am looking at the experimental 3D variograms
(`wireframes`) but I see that weird
Dear list,
I was wondering if there is a way to automatically compute x-validation
with the new spacetime objects (as is in gstat::gstat.cv).
Looking through the available functions it does not look so, thus I bet I
just need to manually run it. ?
Thanks,
Piero
[[alternative HTML
Dear list,
is there a way to have block-kriging predictions when using spatio-temporal
with separable covariance model, in particular by using the gstat/spacetime
suite. ?
I saw examples of block kriging when using a 3D metric kriging, with
rescaled time axis, so that a parallelepiped can be set
Dear Edzer,
thanks for the help.
Is block-kriging possible with this kind of covariance model, or is it just
not implemented in the `spacetime` package?
On 13 January 2013 18:31, Edzer Pebesma edzer.pebe...@uni-muenster.dewrote:
I saw examples of block kriging when using a 3D metric kriging,
Dear list,
I am predicting PM measurements on a spatiotemporal grid with monthly
intervals in time.
At modeling time, I am looking at the experimental 3D variograms
(`wireframes`) but I see that weird decreasing behavior in time (see
wireframes_2008-1.eps for January 2008): there is a peak at 0
Thank you Edzer,
comments inline
On 16 December 2012 19:19, Edzer Pebesma edzer.pebe...@uni-muenster.dewrote:
Yes, that works:
class(stMeuse)
[1] STFDF
attr(,package)
[1] spacetime
dim(stMeuse)
space time variables
10 2 4
I could see that a STFDF
Hi Julia,
initial variogram values just need to be sufficiently reasonable from where
to start fitting.
I know about some known rule-of-thumb:
+ sill: the total variance of your data;
+ range: half the diagonal of your bbox;
+ nugget: the measurement error.
You can also decide whether to
Dear list,
I have a set of ~130 ground stations measurements and several grid
covariates with which I am spatialising the ground point values.
I am using external drift kriging: I noticed discontinuities in the
predicted map when using a _localized_ prediction, hence restricting the
neighbors
Hi Romina,
looking at { http://spatialreference.org/ref/epsg/32721/ } I can see the
UTM zone 21H or also 21S for South spans longitudes in the range (-80,-60)
so I guess you just inverted easting and northing in your code:
So:
$ LatLong - data.frame(Y=-35.14189, X=-57.39206) # -- X is the
Thanks Giuseppe,
I actually can see that the Close Gaps with Spline is mentioned in the
documentation, but no further info is proposed.
Asking the SAGA CMD I can only get the usage (which I already knew from
`rsaga.get.usage(lib=grid_tools, module=7)`:
$ saga_cmd libgrid_tools.so Close Gaps
Dear Gianni,
The R Help mailing list should bemore suitable for these kinds of question:
http://r.789695.n4.nabble.com/R-help-f789696.html
You may consider reading some R book also, e.g. R In a Nutshell:
http://www.amazon.com/Nutshell-Desktop-Quick-Reference-OReilly/dp/059680170X
or at least the
Thank you.
So that means that e.g. with ordinary cokriging, the one condition sum of
all coefficients equal 1 is used, and not e.g. the (n+1) nonbias conditions
by which the coefficients of the target variable sum to 1, whereas the
coefficients of the n secondary variables sum to 0 ?
I'm sorry,
variables is 0.
On 04/06/2011 01:59 PM, Piero Campalani wrote:
Thank you.
So that means that e.g. with ordinary cokriging, the one condition sum
of
all coefficients equal 1 is used, and not e.g. the (n+1) nonbias
conditions
by which the coefficients of the target variable sum to 1, whereas
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