Thanks a lot! The authors of the RLadyBug package are also about to release functions to estimate a temporal marked point process with time-varying covariates and both time and space correlation (probably in a few weeks). They informed me by private e-mail but stated they were not opposed to my putting this piece of information here. Best regards, Mayeul
_____________________________________________________ Dr. Mayeul KAUFFMANN, Conflict Specialist European Commission, Joint Research Centre (JRC) Institute for the Protection and Security of the Citizen (IPSC) Global Security and Crisis Management - ISFEREA Via E. Fermi 2749 - I-21027 Ispra (VA), ITALY Phone: (+39) 033278 5071 http://isferea.jrc.ec.europa.eu/Staff/Pages/Kauffmann-Mayeul.aspx (Office: building 48c, 1st floor, room 123. TP: 483) -----Original Message----- From: adrian.badde...@csiro.au [mailto:adrian.badde...@csiro.au] Sent: Thursday, August 26, 2010 10:18 AM To: mayeul.kauffm...@jrc.ec.europa.eu; r-sig-geo@stat.math.ethz.ch Subject: RE: Temporal marked point process with time-varying covariates Mayeul KAUFFMANN wrote: > I am trying to model a temporal marked point process with time-varying > covariates and I am looking for the most appropriate function among several > ones. [ ...] I had a look at the following packages > spatstat > splancs > PtProcess > spatstat seems to have the correct object to handle my dependant variables (the > ppx class: 2D space + time) but if I'm correct the ppm() model fitting function > cannot handle this (it only works with ppp). Am I missing something? I saw at > http://www.spatstat.org/ that this branch is in development. Any news / > schedule on that? Yes, the class 'ppx' in spatstat will support space-time point pattern data with any number of space and time dimensions. Currently the model-fitting function will only handle two-dimensional point patterns (class 'ppp'). However ppm will soon be able to handle ppx objects. We have code, but it is not ready for release yet. Due to some bad experiences in the past, I am reluctant to release spatstat code that involves original research until the research papers have been published. Just to clarify something: 'spatstat' is **not** committed to a particular definition of the conditional intensity. If the points are in time or space-time, where time is one-dimensional, then the natural definition of the conditional intensity is one which looks at the 'past'. However if the points are in m-dimensional Euclidean space, then the most appropriate definition of the conditional intensity is something different (usually the Papangelou conditional intensity). In spatstat, the type of conditional intensity is determined by the 'interaction' argument to ppm (or actually by 'interaction$family'), and thus can be different from model to model. Currently ppm deals with two-dimensional point patterns and the interactions mostly use the Papangelou conditional intensity, but the package design does not make any such assumptions. [We also have code for fitting models that use the directed conditional intensity in two-dimensional time, and this will be released shortly.] Adrian Baddeley _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
