similar to: spatstat ppm models with covariates

A new version 1.9-5 of the package `spatstat' has been uploaded to CRAN. What it is: ---------- spatstat is a package for analysing spatial data, mainly Spatial Point Patterns. What's in it: ------------ Functions for exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns,

A new version 1.9-5 of the package `spatstat' has been uploaded to CRAN. What it is: ---------- spatstat is a package for analysing spatial data, mainly Spatial Point Patterns. What's in it: ------------ Functions for exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns,

Dear all, I'm working with two point patterns (ppp) in spatstat. I turned one of them into a spatial covariate (im) object. After that, I used this im object to fit a Poisson model for the second point pattern, using the covariate layer from the first one. In R, the whole thing looks somewhat like this: my_first.im <- as.im(my_first.ppp) test.ppm <- ppm (my_second.ppp, ~my_first,

spatstat 1.11-0 Version 1.11-0 of package 'spatstat' is now available. Spatstat is a package for the analysis of spatial data, mostly point pattern data. See <www.spatstat.org> Important changes in version 1.11-0: New format for ppm objects (objects in old format are still handled). More stringent checking of function arguments. Improved handling of pixellation effects.

Hello, I have fitted a Poisson Process model in spatstat using >fit1<-ppm(points, ~elevation, covariates=list(elevation=elevation.im)) This far, everything went well, but I try to simulate the fitted model using the function: >sim1<-rmh(fit1) But I get the error message: "Extracting model information...Evaluating trend...done. Checking arguments..determining simulation

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

Dear r-help, I would like to use the 'ppm' function of the 'spatstat' package to fit a Strauss inhibition model. I understand that I can specify a parametric model for the "background" trend, but how would I specify a trend which is estimated using a Kernel density smoother? In particular, I would like to use the 'kde' function of the 'ks' package to

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

Dear all, Several days ago, I posted How to write a bmp file pixel by pixel. Instead of bmp, I succeeded in writing a PPM file by using the pixmap package. Thanks for the hint generously provided by Uwe Ligges. Now I have a new question. How to convert a PPM file to BMP file in R? I know I can do this in photoshop or by some other softwares, but I think if I Can do this in R, that

Dear R-help I want to display an image file in a new plot frame. SVG is my preferred format, but I can also consider PNG, GIF, JPEG, TIFF, and PPM (from ImageMagick). By way of background (although not material to this posting), the image file is generated by a call to 'dot' (part of the 'Graphviz' suite) after the required .dot file has been produced using hand-crafted R code.

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

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 all, I am using "spatstat" to investigate the spatial structure of some plant populations, and I want to detect these patters with IPP and a Thomas process based on pair-correlation function. I know the function "pcfinhom" is available to characterize the IPP, but I have no idea about how to use the pcf with Thomas process? Additionally, generating simulation envelopes

Hello, I would like to use the spatstat package to generate realizations from a spatial birth death process. The death rate is dependent on the pairwise interaction function. 1 - I'd like to know how to define my own interaction potential function 2 - I'm not sure which model to use for a birth death process (my parameters are the birth and death rates) Any help would be greatly

Hello everyone, My data consists of marked points and several covariates, whereby the marks are the time points of the observations. The problem is, that one of the covariates is hard to handle as an image. This covariate represents the type of roads. As there aren't roads at every location of the map, one cannot specify the value of the covariate at any point on a grid, which is necessary to

Hello I am currently studying spatial distribution of lizards in Spain and Germany. For this I am using the spatstat package of R to make a Kolmogorov-Smirnov test of CSR of unmarked points data. I have problems transforming the data to ppp class, and I would like to know if you can help me. I have already made the test with the data of 'japanesepines' that comes with the package, and

Recently Brian Ripley had occasion to mock my inability to see a comment in the code for arima(), in the stats package. After considerable dredging around in the r-news archives I found reference to keep.source() and keep.source.pkgs(), which I conjectured just ***might*** possibly be the ``obvious'' resource to which Prof. Ripley thought I ought to have resorted. However, after

Dear R Users, I have CO2 mixing ratio(ppm) time series data during 1991-2000. I would like to calculate CO2 growth rate /ppm.a-1 (is it derivative dt/dco2 ??) Kindly can any one advise how to calculate above. My data file looks like; time, year, month, day, hour, min, sec, lat, long, height, CO2 1991.476722 1991 6 24 0 5 0 -38.93 145.15 4270 353.680 1991.476741 1991 6 24 0 15 0 -39.20 145.22

Hi all, When I detect the spatial point pattern, I want to use the Cramer-von Mises statistic to assess the curve-wise significance of deviations from null hypotheses. Who can tell me which function in R package "Spatstat" can do this work? Thanks a lot Jeff [[alternative HTML version deleted]]

Dear all, I am currently working with the spatstat package, using windows and pixel images. First: My aim is to transform a shapefile (see attached) into a pixel image. My idea is to start transforming the shapefile into a Spatial Polygon file: x <- readShapeSpatial("200001441.shp") y <- as(x, "SpatialPolygons") z <- as.owin(y) Given z, I want to identify each