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I have photographs of plots that look like so: http://r.789695.n4.nabble.com/file/n4631960/Untitled.jpg I need to divide it up so each circle has an equal area surrounding it. So into 20 equal segments, each of which contains a circle. Quadratcount is not sufficient because if I divide it up into 36 equal quadrats, some quadrats do not contain one of the circles. I'm not even sure how to

I normally use the following code to create a figure displaying the mark correlation function for the point pattern process "A": M<-markcorr(A) plot(M) I have now started to use the following code to perform 1000 Monte Carlo simulations of Complete Spatial Randomness (CSR). It is a Monte Carlo test based on envelopes of the Mark correlation function obtained from simulated point

Hi all, I have just started to use R for my PhD project and have no previous experience in programming. I am having trouble importing data to R. This is the output: > mydata <- read.table("Lv2.8.txt") Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file 'Lv2.8.txt': No such

I have the following data from an image analysis program, in which the x and y co-ordinates are locations of the centroids of shapes on a 2 dimensional plot. The Y co-ordinates were positive, but I changed them to negative as the resulting scatterplot was upside down (the image analysis program reads from the top of the image to the bottom, so it seems) I now need these as point pattern data, as I

I have presence/absence data in the following format: 1 2 3 4 5 6 7 8 9 10 11 12 1 1 1 1 1 1 1 1 1 1 1 1 0 2 1 1 1 1 0 1 1 1 1 1 1 0 3 1 1 1 1 1 1 1 1 1 1 0 1 4 1 1 1 1 1 0 1 1 1 1 0 1 5 1 1 1 1 1 1 1 0 0 1 1 1 6 1 1 1 0 0 1 0 0 1 1 1 1 7 1 1 1 1 0 1 1 0 0 0 1 1 8 1 1 1 1 1 1 1 0 0 0 1 0 9 1 1 1 1 0 0 1 0 0 0 1 1 10 1 1 1 1 1 1 1 0 0 0 0 1 11 1 1 1 1 1 1 1 1 1 0 0 1 12 1 1 1 1 1 0 0 0 1 1 1 1

I have plotted Ripley's K function for a spatial point pattern for 12 plots, with 39 Monte Carlo simulations for complete spatial randomness (CSR). I would like to analyse these data as follows: I would like to know for which plots the Ripley's K function deviates from CSR at a number of confidence intervals, namely 90%, 95% and 99%. I realise that simulation envelope I have produced