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...to work out what may be going wrong - does anyone have any suggestions!? Many thanks Lauren --_______________________________________ Lauren Gough - Postgraduate Research Student University of Nottingham, School of Geography, Nottingham, NG7 2RD Tel: +44 (0)115 8467052 Email: lgxlg1@nottingham.ac.uk <mailto:lgxkl2@nottingham.ac.uk> P Consider the environment. Please don't print this e-mail unless you really need to. This message has been checked for viruses but the contents of an attachment may still contain software viruses which could damage your computer sy...

Hi all, I'm using spatstat to investigate the spatial structure of an arid shrub population. The first-order intensity of my data does not appear to be homogenous, so I would like to use inhomogeneous techniques. I realise there is a inhomogeneous K-function available in spatstat, but there doesn't not appear to be one for the pair-correlation function (O-ring statistic). As such I was

Dear all, My question concerns using repetitions and simulations (loops?) in R. I am very new R user, so any help that can be offered would be greatly appreciated! I am using fitdistr() to determine the distribution of empirical univariate datasets, and ks.test to assess the goodness of fit. Because the null distribution of the KS statistic is not known when the distribution parameters are

Hi all, I am using quadratcount in spatstat to divide a window containing a point pattern into a grid of quadrats containing the intensity of points in each quadrat. My data is in UTM co-ordinates. My window is defined as follows: >p15<-ppp(x,y,window=owin(c(341710,342100),c(3126465,3126780)),marks=NUL L, checks=TRUE) Giving me a distance of 390m in the 'x' direction and 315m in

Hi, I have a numeric pixel image which I would like to divide into factors for analysis in Spatstat. I have found that I can use cut.im() function to divide the range of pixel values into a series of equal length intervals (e.g. if my pixels values range from 0 to 60, cut.im(X.im,breaks=2) will produce two factors one containing pixel values 0-30 and one containing pixel values of 30 - 60, or

Dear All, I have two questions regarding distribution fitting. I have several datasets, all left-truncated at x=1, that I am attempting to fit distributions to (lognormal, weibull and exponential). I had been using fitdistr in the MASS package as follows: fitdistr<-(x,"weibull") However, this does not take into consideration the truncation at x=1. I read another posting in this