search for: inhomogeneity

Dear all I want to simulate a stochastic jump variance process where N is Bernoulli with intensity lambda0 + lambda1*Vt. lambda0 is constant and lambda1 can be interpreted as a regression coefficient on the current variance level Vt. J is a scaling factor How can I rewrite this avoiding the loop structure which is very time-consuming for long simulations? for (i in 1:N){ ... N <- rbinom(n=1,

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

As far as I can tell from reading The Fine Documentation (R Language Definition and Intro to R), matrices are supposed to be of homogeneous types. Yet giving matrix() an inhomogeneous list seems to work, although it produces a peculiar object: v = list(1:3,4,5,"a") m = matrix(v,nrow=2) m [,1] [,2] [1,] Integer,3 5 [2,] 4 "a" m[1,] [[1]] [1] 1 2 3 [[2]]

...b) programcode The program should act on the data, if the textfile is source()'ed into R. BOUNDARY CONDITION: I want the data written in the textfile in exactly the same format as I would use, if I had data in a separate textfile, to be read by read.table(). That is, with 'horizontal inhomogeneity' and 'vertical homogeneity' in the type of entries. I want to write something like Sex Respons Male 1 Male 2 Female 3 Female 4 In effect, I am asking if there is some way I can convince read.table(), that the data is contained in the following n...

Hello, for my meta-analysis I try to test if two varainces are equal without using the raw scores. I have is the SD's, N's and the Means. I want to test the variances from dependent and independend samples. I assume I can use the var.test procedure for the independent samples, but what about the dependent samples ? Has anyone an idea how to realise this with R ? Thanks in advance

I have a problem with file locking on different platforms. System environment: Samba Server 2.2.3.a on Red Hat 7.3 Some Windows 2000 Clients with MS-Office 2000 and OpenOffice 1.0.1 Some Red Hat 8.0 Clients with Open Office 1.0.1 The Linux Clients mounts the Samba Shares with smbmount: smbmount //<Server/shares> $HOME/path -o

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

Hi, I started using R about 3 months ago so please excuse my ignorance. I have two datasets. The first consists of 247 fixed position mosquito traps that were serviced weekly for 23 weeks. The second is dengue incidence data indexed to home address for the same time period. I would like to see if there is a space time relationship between mosquito catch rate and dengue incidence. So far I have

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 Everyone, I am trying to figure out the spatstat package for the first time and am having some trouble. Unfortunately, I can't post my data set but I'll hopefully post enough details for some help. I want to model the intensity of a spatial point process using 2 covariates from my data. After reading through the documentation, I have successfully created 2 "ppp" objects. The

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,