Displaying 20 results from an estimated 600 matches similar to: "variograms and kriging"
2012 Feb 23
1
using shapefiles in adehabitat/ converting shapefile to spatial pixel data frame
Hello
I wonder if anybody can help,
I am using the package adehabitatHR to estimate the potential distribution of a species using the command "domain"
In the example given in the AdehabitatHS manual a map containing elevation information is loaded (class= spatial pixels data frame) as well as the GPS points of the animals being tracked, these are then plotted on each other and
2012 Dec 17
1
How to make Ordinary Kriging using gstat predict?
Hi,
I am new in R and trying to implement an algorithm which makes ordinary kriging by using gstat library and the predict method.
I use the predict method as following:
First, I create an object g:
g <- gstat(id="tec", formula=TEC ~ 1, data=data) ## Create gstat object called g
And then I use this object in the predict.
p <- predict.gstat(g, model=mod, newdata=predGrid,
2008 Aug 07
0
3d kriging et al
R Users:
...been working with the sp and gstat packages for the past couple of days in an effort to analyze a set of ~ 200 soil samples collected from various eastings, northings, and depths and containing a wide range of measured hydrocarbon concentrations.
Thus far, I've managed to import the data, log-transform the concentrations, assign coordinates, generate and fit a variogram model and
2009 Aug 16
1
How to use your own data in gstat and sp?
This seems pretty basic, but I can't get any data to work except for the
documented examples. When the goal is to get to SpatialPixels, here is what
I see...
> x <- runif(10,1,10)
> y <- runif(10,1,10)
> z <- rnorm(10,0,1)
> MyData <- as.data.frame(cbind(x,y,z))
> library(gstat)
> coordinates(MyData) <- ~x + y
> gridded(MyData) <- TRUE
suggested
2002 Dec 17
2
Cross-correlograms or cross-variograms in R?
Hello group,
For my PhD I'm working on a spatial sampling grid. I do have two data sets
which I'd like to compare using cross-correlograms or cross-variograms.
Is this an option in one of the R-packages? I've been searching the R-help
archive and the available package-documentations, but I can't find how to do
this.
Thanks in advance,
Ren?.
2009 Jun 10
2
plot two variograms on a same graph
Hi,
I would know how to plot two variograms on a same graph. I can plot one by one but I would draw both on the same one.
Is it possible? Do i need any special package?
Thanks!
Cordialement
Damien Landais
2005 Nov 09
2
Variograms and large distances
Hello R list,
I need to compute empirical variograms using data from a large
geographic area (~10^6 km2). Although I could not find a specific
reference, I assume that both geoR and gstat calculate distances among
data points assuming points are on a flat surface (using the Pythagorean
Theorem). Because the location of my data is large and located near the
pole, assuming that latitude and
2003 Jun 26
1
krige in gstat() package
HI,
I wonder does anyone have experience with doing sequential gaussian
simulation with krige() function in gstat?
I find it VERY slow compared to use krige() to achieve kriging function
itself.. I wonder why, is that because it has to model the variogram, and
do the kriging separately for each point to be simulated?
so it would be N times slower to achieve the simulation than the kriging
if
2002 Oct 16
0
Ordinary and simple kriging
I'm performing ordinary and simple kriging from a set of non-negative
values:
>krige.control.sk<-krige.control(type.krige="sk",obj.model=my.variogram.model,beta=my.variogram.model$beta)
>sk<-krige.conv(cadiz.geo,data=amostragem.cadiz$dia1.std,locations=as.matrix(cadiz.polygrid[,c(1,2)]),krige=krige.control.sk)
krige.conv: model with constant mean
krige.conv: Kriging
2002 Oct 16
0
[Fwd: Ordinary and simple kriging]
Juan Zwolinski wrote:
>
> I'm performing ordinary and simple kriging from a set of non-negative
> values:
>
>
> >krige.control.sk<-krige.control(type.krige="sk",obj.model=my.variogram.model,beta=my.variogram.model$beta)
>
>
2012 Nov 05
1
Logistic Regression with Offset value
Dear R friends.
I´m trying to fit a Logistic Regression using glm( family='binomial').
Here is the model:
*model<-glm(f_ocur~altitud+UTM_X+UTM_Y+j_sin+j_cos+temp_res+pp,
offset=(log(1/off)), data=mydata, family='binomial')*
mydata has 76820 observations.
The response variable f_ocur) is a 0-1.
This data is a SAMPLE of a bigger dataset, so the idea of setting the
offset is to
2011 Jan 05
1
Prediction error for Ordinary Kriging
Hi ALL,
Can you please help me on how to determine the prediction error for ordinary
kriging?Below are all the commands i used to generate the OK plot:
rsa2 <- readShapeSpatial("residentialsa", CRS("+proj=tmerc
+lat_0=39.66666666666666 +lon_0=-8.131906111111112 +k=1 +x_0=0 +y_0=0
+ellps=intl +units=m +no_defs"))
x2 <- readShapeSpatial("ptna2",
2012 Nov 14
2
Jackknife in Logistic Regression
Dear R friends
I´m interested into apply a Jackknife analysis to in order to quantify the
uncertainty of my coefficients estimated by the logistic regression. I´m
using a glm(family=’binomial’) because my independent variable is in 0 - 1
format.
My dataset has 76000 obs, and I´m using 7 independent variables plus an
offset. The idea involves to split the data in let’s say 5 random subsets
and
2011 Jan 06
1
Cross validation for Ordinary Kriging
ear ALL,
The last part of my thesis analysis is the cross validation. Right now I am
having difficulty using the cross validation of gstat. Below are my commands
with the tsport_ace as the variable:
nfold <- 3
part <- sample(1:nfold, 69, replace = TRUE)
sel <- (part != 1)
m.model <- x2[sel, ]
m.valid <- x2[-sel, ]
t<- fit.variogram(v,vgm(0.0437, "Exp", 26, 0))
cv69
2003 Apr 17
0
kriging in R
Hi
If you read the description of "varcov.spatial" you'll see that it is
used to *predict* a covariance matrix, based on the parameters of the
covariance function. So you don't need the oebserved data, you need
parameters for the covariance function.
Regards
EJ
On Thu, 2003-04-17 at 05:43, Yan Yu wrote:
> THanks for the suggestion.
> I have a Q when trying to use it..
2013 Feb 17
0
What value to put in range when I make kriging interpolation?
Hi, I am trying to make an automatic kriging interpolation algorithm.When I use the fit.variogram function what would it be a good startingvalue for the range?
ThanksDimitris
[[alternative HTML version deleted]]
2010 Jun 21
2
ctree
Hello,
This is a re-submittal of question I submitted last week, but haven't rec'd
any responses.
I need to extract the probabilities used to construct the barplots
displayed as part of the graph produced by plot("ctree").
For example,
library(party)
iris.ct <- ctree(Species ~ . , data = iris)
plot(iris.ct)
Instead of a simple example with only 4 terminal nodes, my
2013 Apr 04
5
help with kriging interpolation
All,
I am new to using R and know some basics. I wish to use kriging in R to
do the following:
given data Y =f(X1,X2,X3,.....,Xn) --1000+ irregular measured data set.
I would like to be able to get a single value y given sinle input set
(x1,x2,x3,...xn)
A google search on this takes me lierally to the same example on involving
analysis with soil sampling and I cannot figure out how to
2011 Nov 16
0
calculating variograms (gstat) with large data sets
Dear all,
I am aiming to calculate variograms using variogram() from gstat.
The problem is that some of my data-sets are very large (> 400000 points).
Running the command takes some hours of time and does not give any
error-message.
Nevertheless the result seem not to be appropriate - the first few bins
are ok (up to a distance of about 300) but then it gains lags which are
much larger than
2004 Aug 17
0
Cross-variograms
Jacques, provided that X and Y are colocated (i.e., have
exactly the same observation locations), you get the
cross variogram right; the definition of this cross
variogram is however:
gamma(h)= E[(X(s)-X(s+h))*(Y(s)-Y(s+h))]
also, where you select:
cv <- v$gamma[1:14]
you may be better off using the more general
v$gamma[v$id == "X.Y"]
Best regards,
--
Edzer