similar to: Compare predict and experimental values

Hi everybody: I just generate interpolation maps with differents methods, like IDW and kriging, and now i want to compare the predict values versus real, and i don't who is the commant to do it. I want for example, if I pass from console the coordinates of a place, return me the predict value. Could anybody tell me how should do?. Thanks in advance --

Hi everybody: Could anybody help how I solve the next problem?. I'm doing interpolation maps of tropospheric ozone of my region, and after create it using IDW, and kriging methods, I want from shapefiles (*.shx, *.shp, *.dbf, *.sbx & *.sbn ) create contour over the interpolation maps. Could anybody tell me how do it?. Thanks in advance. ###################################### Antonio

Hello everyone, When looking for information about clustering of spatial data in R I was directed towards DBSCAN. I've read some docs about it and theb new questions have arisen. DBSCAN requires some parameters, one of them is "distance". As my data are three dimensional, longitude, latitude and temperature, which "distance" should I use? which dimension is related to

Hi, I need help! I am trying to iterate an iterative process to do cross vadation and store the results each time. I have a Spatial data.frame, called Tmese > str(Tmese) Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots ..@ data :'data.frame': 14 obs. of 17 variables: .. ..$ ID : int [1:14] 73 68 49 62 51 79 69 77 57 53 ... .. ..$

Hi everyone As I couldn't succeed with manual installation of Rmpi I decided to start again from the beginning. I removed R and MPICH in my Ubuntu Hardy installation. Then, to avoid any dependencies problems I have installed MPICH and R from synaptic, not from sources. But now I can't install Rmpi. An error message appears when trying to install Rmpi, you can find in

Hi R-users I'm a beginner with R and statistics, so I need some help to start my data analysis. I've been reading some docs and tutorials on R and cluster analysis. I've got a large dataset (102000 points) with values of longitude, latitude and temperature and want to see if I can find groups (clusters). Following some tutorials I can look for principal components but get an error

Hi everyone I am new to R and have a question about missing values. I am trying to do a cluster analysis of monthly temperatures and my data are 14 columns with spatial coordinates (lat,lon) and 12 monthly values: /lat - lon - temp1 - //temp2 - temp3 - .... - //temp12/ If I omit missing values (my missing values are 99.00) with /mydata <- na.omit(mydata)/ every row with a

Hello again, There is any package which does Z aware (real 3D) interpolations? It can be any method (IDW, kriging or spline) but it should take into consideration not only x and y coordinates for interpolation, but also z coordinate. I looked into different packages but it seems i didn't find the right one. The ultimate goal is to import the output into a GIS (Geographical Information

Hi everyone I want to install Rmpi to use R in parallel mode in a Linux cluster (Ubuntu, Hardy Heron). It seems to be properly installed but a problem appears when loading Rmpi library. R version 2.11.1 (2010-05-31) > library("Rmpi") Error: package 'Rmpi' was built before R 2.10.0: please re-install it Should I remove R-2.11 and install R-2.10? I have tried to

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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

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Hola, # Hacemos el KED. Ver función "krige()": KED.rad <- krige( 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

Hello, I'm interested in doing Bayesian kriging using R. I see that the package geoR has a function that will allow one to do this (krige.bayes). However, my data are not in the form of points, but rather they are blocks that represent spatial averages (i.e., the number of fishing hooks per month in a given lat x long square). I am therefore interested in treating the data as

Sale plano sí. Ya se que sin tener los datos y el código es un poco difícil, pero es que mis datos ocupan mucho, es imposible. Seguiré mirando por internet. Muchas gracias Rubén. Un saludo, > To: r-help-es en r-project.org > From: rubenfcasal en gmail.com > Date: Thu, 6 Aug 2015 14:21:47 +0200 > Subject: Re: [R-es] Duda interpolación (package ' gstat ') > > Hola

Soon on CRAN a new version of package gstat will emerge, which has a few minor changes and possible incompatibilities w.r.t. the previous version(s). The new gstat (0.9-23) now: + depends on sp, and uses internally with Spatial* classes from sp if data are provided in the old-fashoned way (as data.frame) + has a vignette to get you started with the classes in sp + defines krige as a generic;

Soon on CRAN a new version of package gstat will emerge, which has a few minor changes and possible incompatibilities w.r.t. the previous version(s). The new gstat (0.9-23) now: + depends on sp, and uses internally with Spatial* classes from sp if data are provided in the old-fashoned way (as data.frame) + has a vignette to get you started with the classes in sp + defines krige as a generic;

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

Dear Aaron, I am really a tool user and not a tool maker (actually an ecologist doing some biostatistics)... so, I take the liberty of sending a copy of this e-mail to the r-help list where capable computer persons and true statisticians may provide more relevant information and also to Paulo Ribeiro and Peter Diggle, the authors of geoR.. I really feel that your huge matrix cannot be

Hi All, I am a PhD student in forestry science and I am working with LiDAR data set (huge data set). I am a brand-new in R and geostatistic (SORRY, my background it?s in forestry) but I wish improve my skill for improve myself. I wish to develop a methodology to processing a large data-set of points (typical in LiDAR) but there is a problem with memory. I had created a subsample data-base but