search for: rpoispp

Dear All, I was searching through the spatstat manual in order to find a function to simulate a Poisson pattern only within a fixed radius (circular moving window) around individual points. If points are distributed heterogeneously over a large area this may help to only assess deviation from CSR within the window and thus does not require additional information on a covariate. I could not find

Hi, I am trying to use the R package 'spatstat' for generating spatial poisson point process graphs. I can create a point pattern using the following commands: pp <- rpoispp(.01, win=owin(c(0,100),c(0,100))) and also view the resulting graph by: plot(pp) But how can I export the generated point pattern to an external file so that I could use it as input for some network simulation programs ? Also, it seems that the generated graph is different on each invocatio...

Imagine that I have a large number of points (given by coordinates x and y) that vary in density per space. For the purpose of demonstration it could be generated like this: s <- data.frame(x=runif(10000,0,900),y=runif(10000,0,900)); plot(s) I want to create polygons around the points where point density is greater than a selected threshold (for example, by using krieging or equivalent

Hello all, I'm attempting to conduct spatial analysis of trees within a plot. I want to see if the trees are spatially correlated to soil characteristics, say pH, or moisture content. I think one way to do it is with mpl, however, my soil characteristics were not taken at exactly the same locations as my trees and further, the vectors aren't the same length. I'm getting the

Hi R users, This query is regarding the use of the 'envelope' function in Spatstat. My data can be represented as a point process with CONTINUOUS marks: points <- ppp(x=x,y=y, marks=m, window= wind) However the marks are alignments (lines), and so have to be treated differently to normal scalar marks. Hence to create a mcf object with the appropriate test function for alignment

I am trying to generate two dimensional random coordinates. For randomly distributed data I have simply used >xy<-cbind(runif(100),runif(100)) However I also want to generate coordinates that are more uniformly distributed, and coordinates that are more contagiously distributed than the above. Can anyone make any suggestions Thanks. Dr Terry Beutel Rangeland Scientist Animal