similar to: still have problem with krige and border option

Dear all, I'm new on R and this mailing list. We work on spatial rainfall estimation with R and Grass. We have a problem with the krige function from the sgeostat package. We would like to limit the estimated area with a polygon limit. I use a 50 points polygon to describe my work area. The krige function work quiet well without limit. But if I use this option I have the following error

> both "_ "and "<-" are assignment operators well, I've changed it in my script, but the result is the same. > what do you get if you call > > in.polygon(grid2.point$x,grid2.point$y,polygon$x,polygon$y) That's it ! In fact all my points in grid2.point where xy inverted. And I had no points in polygon. It was really a stupid mistake. Thanks for all !

DeaR list, I'm running an external program that computes some electromagnetic response of a scattering body. The numerical scheme is based on a discretization with a characteristic mesh size "y". The smaller y is, the better the result (but obviously the computation will take longer). A convergence study showed the error between the computed values and the exact solution

Tried to post this last night, but it doesn't seem to have appeared. Using R 2.0.1 on Windows XP + SP2. I am traveling, away from my usual references. I'm trying to make a 4-dimensional plot: a levelplot with overlaid contours, with different response variables represented by (1) colors on the levelplot and (2) the contour lines. First try was filled.contour + contour but the key

Hello, This is what I am trying to do: I wrote a little function that takes addresses (coordinates) as input, and returns the road distance between every two points using Google Maps. Catch is, there are 2000 addresses, so I have to get around 2x10^6 addresses. On my first go, this is what I did: ######################################### getRoadDist = function(X,complete=F){ # X must be a

Hello R users, I have numerical data sampled on two grids, each one shifted by 0.5 from the other. For example: grid1 = expand.grid(x=0:3, y=0.5:2.5) grid2 = expand.grid(x=0.5:2.5, y=0:3) gridFinal = expand.grid(x=0.5:2.5, y=0.5:2.5) plot(gridFinal, xlim=c(0,3), ylim=c(0,3), col="black", pch=19) points(grid1, xlim=c(0,3), ylim=c(0,3), col="red", pch=19) points(grid2,

Dear R Users, Using the following R-script I created the first image > require(akima) > require(spatial) > dep <- interp(long, lat, depth, xo=seq(1,990,10), yo=seq(1,990,10), + extrap=FALSE, ncp=0,duplicate = "mean", dupfun = NULL) http://r.789695.n4.nabble.com/file/n3431391/Rpics.bmp Where "long" are x-coordinates between 1 and 1000, "lat" are

Dear R users, I want to do a fitted.contour plot of selected columns of a dataframe M with M$AM and M$Irradiance as x and y axes respectively. The level of the contour shall be determined by M$PR. Some words on my data first. Dataframe M looks like: head(M$Irradiance) [1] 293 350 412 419 477 509 head(M$AM) [1] 2.407 2.161 1.964 1.805 1.673 1.563 head(M$PR) [1] 70.102 72.600 75.097 80.167

Dear expeRts, The order in which the variables vary in expand.grid() is often unintuitive. I would like to suggest a 'method' slot for expand.grid() which requires only very little changes (100% backward compatible) and which allows one to control this order. Please find attached diffs against R-devel. Cheers, Marius ### ./src/library/base/R/expand.grid.R

Hello, I have a long list of x-, y- and z-data and try to generate a heatmap. Obviously there are several data with identical x- and y-values. I get the following error message: Error in interp.old(x, y, z, xo = xo, yo = yo, ncp = 0, extrap = extrap, : duplicate data points: need to set 'duplicate = ..' Unfortunately there seems no help screen on "duplicate". I'd prefer

Full_Name: Jonathan Lees Version: 2.0.1 OS: linux-gnu Submission from: (NULL) (152.2.75.65) there is a problem with calculating the convex hull in 2-D interpolation using the codes interp fromt eh akima package: x =c(0.6505304, -1.1821562, -0.2600792, 0.7913716) y = c(1.0424226, 0.1754048, -1.4523334, 0.2349112) z = c(0.000, 3.042, 0.370, 0.122) EX = seq(from=min(x), to=max(x),

Hi R-users, I'm attempting to fit a number of mixed models, all with the same structure, across a spatial grid with data points collected at various time points within each grid cell. I'm trying to use a 'for' loop to try the model fit on each grid cell. In some cells lme does not converge, giving me the error: Error message: In lme.formula(logarea ~ year + summ_d, data =

Dear List, I'm using interp() to prepare 3d data for plotting with the contour() function. If have x,y and z data. All are arrays. X and Y are sampled in an orderly fashion on a grid (a circular sub-area of a grid - see plot). I'm trying to use interp() to get x and y arrays and a z matrix that can be fed to contour(). This is the command:

Moved the node administration elements to a separate submenu. Created a new network administration menu. * users can define a network * users can create a network * users can destroy a network * users can undefine a network * users can list existing networks, including details Each new command is also available as a separate command line executable as well. Signed-off-by: Darryl L. Pierce

A majority of my data makes a kriged map perfectly using an exponential model for the semivariogram to fit my data and then going through the commands variofit() to define the model and then krige.conv() to use the model to predict values in a grid. But?one set of my data appears to be linearly correlated for the first 5000 meters and not correlated beyond that. I have been having problems

Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep getting the same error message. The bolded part is the part that doesn't work.

Greetings all, Ran into a strange problem with the krige function from geoR. The problem that I am having is that while the krige function seems to work well, the resulting predicted values are all NAs. Given the size of the datasets I am working with can't attach it, but I can provide snippets of the datasets. > casedata station year month day obs mpe bias type

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

I'm new at this, so please bear with me. I am trying to get Krig (actually tps) results into a data frame at a set of points. Frankly, all I really need is to query the Krig on a specific XY pair and return a result. I can carry the rest. I am using tps from the fields package. Here's the code I use to create the Krig object: =========== Snip! =============================== dCurr

I?m trying to make an estimation of sea-depth for 2000 animal locations using 2 million depth soundings. The result was: Cannot allocate vector of size 20351 Kb I've tried to overcome this problem by making the following adaptations. -Increased the max-mem-size to 1.5Gb (Thanks to Thomas Lumley for his help) -Use a max.dist (10km) for the function Krige -Run the Krige computation for each