search for: maxdist

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

...#39; variables being ignored when computing the distance). The size of the neighbourhood is controlled by &alpha; (set by span or enp.target). For &alpha; < 1, the neighbourhood includes proportion &alpha; of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3. For &alpha; > 1, all points are used, with the `maximum distance' assumed to be &alpha; times the actual maximum distance. </SNIP> I understand the situation when alpha is less than one, presuming it uses the same convention set forth in any number of Clevelend and...

...#39; variables being ignored when computing the distance). The size of the neighbourhood is controlled by &alpha; (set by span or enp.target). For &alpha; < 1, the neighbourhood includes proportion &alpha; of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3. For &alpha; > 1, all points are used, with the `maximum distance' assumed to be &alpha; times the actual maximum distance. </SNIP> I understand the situation when alpha is less than one, presuming it uses the same convention set forth in any number of Clevelend and...

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

...rige( formula=pluvPcp~layer, # covariable -> radar locations=lluvia.rad.pluv.spdf, newdata=radarGrid, # podría ser cualquier objeto Spatial model=v.fit, # modelo de semivariograma. maxdist=Inf ) Esta es la función que me interpola los datos de lluvia. El error que me da es: "solve.c", line 88: singular matrix in function Usolve() "lufactor.c", line 208: singular matrix in function m_inverse() Error in predict.gstat(g, newdata = newdata, block = block, n...

...oked at the first one, and for the strings "UndeclaredSoFar" and "_Imaginary" and a maxEditDistance of 5 (= the difference in their lengths, if that matters), the algorithm returns an edit distance of 2. One of the other tests (Parser/objc-quirks.m) is actually a crash. + // maxDistance (aka D) directly influences the width of the strip (2*D+1) + // we arbitrarily restrict it in case the caller did not see fit to do it + // (from clang\lib\Sema\SemaLookup.cpp in TypoCorrectionConsumer::FoundName) + unsigned const maxDistance = std::min(unsigned((otherLength+2)/3), +...

Dear all, I got point data of trees. I was wondering if anybody has experience in searching the neighbors within a specified distance efficiently. X Y Z 99 34 65 98 35 29 98 34 28 99 33 33 98 32 23 99 33 21 99 33 22 99 32 24 99 30 23 ... What I want to do is : searching for the neighbors with a distance R for each tree & the neighbor must have a bigger Z.

..._test.txt",header=FALSE) user>polygone<-list(x=border_limite[,1],y=border_limite[,2]) So now I have a list of x,y coordinates to limit my work area. If I call the function krige with extrap=FALSE like this user>grid2.krige<-krige(grid2.point,logtab.point,'lz', logtab.Exp,maxdist=NULL,extrap=FALSE) [ where grid2.point is a point object to store prediction, logtab.point a point object containing points data, 'lz' is the variable name in logtab.point, logtab.Exp is a variogram object ] all works quiet well, but I am limited to a convex hull inside the limite desi...

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

...t match I don't know what is wrong. The x2 data is a SpatialPointsdataframe that is why i did not specify the location (as it will take it from the data). Here is the usage of the function krige.cv: krige.cv(formula, locations, data, model = NULL, beta = NULL, nmax = Inf, nmin = 0, maxdist = Inf, nfold = nrow(data), verbose = TRUE, ...) I hope you can help me on this. Thanks a lot. Best regards, Pearl [[alternative HTML version deleted]]

...# covariable -> radar > > locations=lluvia.rad.pluv.spdf, > > newdata=radarGrid, # podr?a ser cualquier objeto Spatial > > model=v.fit, # modelo de semivariograma. > > maxdist=Inf > > ) > > > > Esta es la funci?n que me interpola los datos de lluvia. El error que me da es: > > > > "solve.c", line 88: singular matrix in function Usolve() > > > > "lufactor.c", line 208: singular matrix in function m_inv...

Hello Doug, *putting llvmdev in copy since they are concerned too* I've finally got around to finish a working implementation of the typical Levenshtein Distance with the diagonal optimization. I've tested it against the original llvm implementation and checked it on a set of ~18k by randomly generating a variation of each word and checking that both implementations would return the

...abline = 0) [[1]] coplot $show.given [1] FALSE (and a very blank graph screen). Also if I run the following (plot.variogram example) I get the following; > data(Ovary) > > fm1 <- lme(follicles ~ sin(2*pi*Time) + cos(2*pi*Time), Ovary) > plot(Variogram(fm1, form = ~ Time | Mare, maxDist = 0.7)) Error in FUN(X[[1]], ...) : couldn't find function "dist" Has anybody got this code to work. I suspect looking at the code and the calls to bwplot and xyplot (all of which are S-plus) that not all this code has been converted. I am new to R and thus am I being niave to expe...

...eCld <- 0 scr <- plotAll(Object, print.cal = print.cal, print.traj = print.traj, print.sub = FALSE, col = "black", type.mean = "n") if (length(startingCond) == 1) { if (startingCond == "allMethods") { startingCond <- c("maxDist", "randomAll", rep("randomK", nbRedrawing))[1:nbRedrawing] } else { startingCond <- rep(startingCond, nbRedrawing) } } else { } if (missing(distance)) { distance <- "euclidean" }...

...ging within the dataset on a fixed radius), but to my knowldege this cannot be done with geoR (unfortunately). The library gstat offers this option, but has much more limited possibilities than geoR considering other issues (variogram analysis, etc...). library(gstat) ?krige see argument 'maxdist' Hope this can help, Kind regards Patrick Giraudoux -- Department of Environmental Biology EA3184 usc INRA University of Franche-Comte 25030 Besancon Cedex (France) tel. +33 381 665 745 fax +33 381 665 797 http://lbe.univ-fcomte.fr Aaron Swoboda a 侀crit : > Dear Sir: > > >...

...verything seems working fine with the plotting function: > Variogram( fm1Wheat2, form = ~ latitude + longitude ) Error in as.data.frame.default(x[[i]], optional = TRUE) : can't coerce array into a data.frame > plot( Variogram(fm1Wheat2, form = ~ latitude + longitude, + maxDist = 32), xlim = c(0,32) ) Could anybody help me with this? Regards, Cristina -- Cristina Silva IPIMAR - Departamento de Recursos Marinhos Av. de Bras?lia, 1449-006 Lisboa Portugal Tel.: 351 21 3027096 Fax: 351 21 3015948 csilva at ipimar.pt -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-....

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

..... ..$ x : num [1:31837] 410683 410700 410720 410740 410324 ... .. .. ..$ y : num [1:31837] 43136 43126 43123 43125 42709 ... .. .. ..$ wz: num [1:31837] -101.1 -94.9 -93.3 -94.5 30.8 ... .. ..$ has.intercept: int 1 .. ..$ beta : num(0) .. ..$ nmax : num 500 .. ..$ maxdist : num Inf .. ..$ dummy : logi FALSE .. ..$ vfn : int 1 .. ..$ weights : NULL $ model:List of 1 ..$ rv:Classes variogram.model and `data.frame': 1 obs. of 9 variables: .. ..$ model: Factor w/ 16 levels "Nug","Exp","Sph",..:...

...ed? It does not model variograms on the way. It is slower than kriging because it uses the sequential simulation algorithm: for each node visited, a value is simulated, and this value is added to the conditioning data. For this reason you _have to_ limit the search neighbourhood (using the nmax or maxdist arguments) if you're simulating on more than say a few hundred nodes. Taking a very small nmax may yield fast simulations, at the expense of a good representation of the target spatial correlation structure. > > so it would be N times slower to achieve the simulation than the kriging &g...