similar to: spatial simulation

This should be theoretically very simple, but I dont get the elegant answer (without looping). I've got a long (thousands of rows) data frame: > E.coor[1:10,] east north dat 1 582650 4248850 0.8316848 2 582750 4248850 0.7230272 3 582850 4248850 0.3250818 4 582950 4248850 0.6144006 5 583050 4248850 0.8706312 6 583150 4248850 0.2149651 7 583250 4248850 0.1659519 8

Hi, I have two questions about spatial data. 1) I have this grid x <- rep(c(1:8),c(rep(6,8))) y <- rep(c(1:6),8) I need to make some others grids with same length and different numbers of points whit a random spatial pattern. How is the best method to make this in R. 2) I need to produce some grids with the same numbers of points in different degrees of aggregation, the level of

Hi, I have collected data on trees from 5 forest plots located within the same landscape. Data within the plots are spatially autocorrelated (calculated using Moran's I). I would like to do a ANCOVA type of analysis combining these five plots, but the assumption that there is no autocorrelation in the residuals is obviously violated. Does anyone have any ideas how to incorporate these spatial

Hi list, I am trying to fit arima model for a grid of 360x161x338 points, where 360x161 is the spatial dimension and 338 is the number of time steps I have, which is seasonal. For this purpose I used the auto.arima function in forecast package. After fitting residuals at each grid in space, the auto correlations are still significant ( but < 0.2). This make me think that the data

Dear all: Does anyone have any suggestions on how to make a spatially explicit landscape with spatial autocorrelation in R? In other words, a landscape where all cells have a spatial reference, and the environment values that are closer in space are more similar (positive spatial autocorrelation). Thank you, Laura

Hi all, I'm running into some computer issues when trying to run a binomial model for spatially correlated data using glmmPQL and was wondering if anyone could help me out. My whole dataset consists of about 300,000 points for which I have a suite of environmental variables (I'm trying to come up with a habitat model for a species of seal, using real (presence) and simulated dives

The NOAA National Centers for Coastal Ocean Science is hiring a Computational Ecologist, a statistical/computational ecologist with experience fitting advanced spatial models to marine wildlife survey data (e.g., seabirds and marine mammal transects, fisheries trawl surveys) in R and other statistical languages. This is a full-time, long-term stable contract position. We are looking for an

Hi R users I am trying to create a spatial join between two datasets. The first data set is large and contains descriptive data including x and y co-ordinates. The second dataset is small and has been selected spatially. The only data contained within the second dataset is the x and y coordinates only i.e. no descriptive data. The aim of a join made between the two datasets is to select

I want to create a dataset in R with spatial correlation (i.e. clustering) built in for a linear regression analysis. Any tips on how to do this? Thanks. -- View this message in context: http://www.nabble.com/Need-help-creating-spatial-correlation-for-MC-simulation-tp19542145p19542145.html Sent from the R help mailing list archive at Nabble.com.

Hi Everyone, I am doing a project based on "Spatially Lagged Predictor Variable Models", I would like to know which package in R would execute this model. Also, I am new to this field of spatial statistics. Any suggestions for a good book on spatial regression analysis would be appreciated. Thanks Again. Cheers Arun -- View this message in context:

Hi, I'm trying to get a plot that looks somewhat like the attached image (sketched in word). I think I need somthing called a rose diagram? but I can't get it to do what I want. I'm happy to use any library. Essentially, I want a circle with degree slices every 10 degrees with 0 at the top representing north, and 'tick marks' around the outside in 10 degree increments to

Hi. I'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Dear R-users, I use R to run spatial stuff and it takes up a lot of ram. Runs can take hours or days. I am thinking of getting a new desktop. Can R take advantage of the dual-core system? I have a dual-core computer at work. But it seems that right now R is using only one processor. The new computers feature quad core with 3GB of RAM. Can R take advantage of the 4 chips? Or am I better off

Greetings, May you please suggest a package or function to use for fitting a GLMM (generalized linear mixed model) with spatially correlated random effects? Thank you, Elijah DePalma [[alternative HTML version deleted]]

Hello, I have fitted a Log-Gaussian Cox Process on my data but when I try to use "simulate.kppm" of the spatstat package I get this error: Error in rLGCP(model = model, mu = mu, param = param, ..., win = win) : The spatial domain of the pixel image ?mu? does not cover the simulation window ?win? I've used covariates as im, changed the npixel value of the spatstat options to

Does anyone have any R code or suggestions on how to generate a population that follows a Neyman-Scott process? More specifically, I'd like to randomly generate both the parents and the children from Poisson distributions and the children locations from a bivariate normal onto an N by N grid. If the locations spill over outside the grid, then I'd like to reflect those locations back into

Hi List, does anyone know of an implementation of spatially constrained clustering in R? This is where there is a vector of measurements for points on a plane and only neighbors can be clustered together. I have tried implementint in myself -- but if someone has alkready done it ! I have searched on the obvios terms "spatially constrained clustering" without any luck.

Dear R users, I want to explain binomial data by a serie of fixed effects. My problem is that my binomial data are spatially correlated. Naively, I thought I could found something similar to gls to analyze such data. After some reading, I decided that lmer is probably to tool I need. The model I want to fit would look like lmer ( cbind(n.success,n.failure) ~ (x1 + x2 + ... + xn)^2 ,

Cordial saludo Tengo una duda sobre como poner el valor de un argumento de una funcion en el titulo de una grafica, es decir, mi funcion es: CO<-function(n,c) { Prob<-sapply(seq(0,0.15,0.01),pbinom,size=n,q=c) coor<-cbind(seq(0,0.15,0.01),Prob) plot(seq(0,0.15,0.01),Prob,type="b",xlab="Fracción defectuosa", ylab="Probabilidad",main="Probabilidad de

Hi, I have spatially autocorrelated data (with a binary response variable and continuous predictor variables). I believe I need to do an autologistic model, does anyone know a method for doing this in R? Many thanks C Bell