We are leveraging too far on speculation, at least from what I can see. PLEASE do read the posting guide! "http://www.R-project.org/posting-guide.html". In particular, try the simplest example you can find that illustrates your question, and explain your concerns to us in terms of a short series of R commands and the resulting output.
With counts, especially if there were only a few zeros, I'd start by taking logarithms (after replacing 0's by something like 0.5 or by adding something like 0.5 to avoid sending 0's to (-Inf)) and use "lme", if that seemed appropriate. Then if I got drastically different answers from other software, I would suspect a problem. Other possibilities for count data are the following: * "lmer" library(lme4) [see Douglas Bates. Fitting linear mixed models in R. R News, 5(1):27-30, May 2005, www.r-project.org -> Newsletter -> "Volume 5/1, May 2005: PDF". * "glmmPQL" in library(MASS). * "glmmML" in library(glmmML) However, I don't know if any of these as the capability now to handle short time series like you described. You might also consider the IEKS package by Bjarke Mirner Klein (http://www.stat.sdu.dk/publications/monographs/m001/KleinPhdThesis.pdf and http://genetics.agrsci.dk/~bmk/IEKS.R). spencer graves Brett Gordon wrote: > Thanks for the suggestion. Is such a model appropriate for count data? > The library you reference seems to just be form standard regressions > (ie those with continuous dependent variables). > > Thanks, > Brett > > On 7/16/05, Spencer Graves <[EMAIL PROTECTED]> wrote: > >> Have you considered "lme" in library(nlme)? If you want to go this >>route, I recommend Pinheiro and Bates (2000) Mixed-Effect Models in S >>and S-Plus (Springer). >> >> spencer graves >> >>Brett Gordon wrote: >> >> >>>Hello, >>> >>>I'm trying to model the entry of certain firms into a larger number of >>>distinct markets over time. I have a short time series, but a large >>>cross section (small T, big N). >>> >>>I have both time varying and non-time varying variables. Additionally, >>>since I'm modeling entry of firms, it seems like the number of >>>existing firms in the market at time t should depend on the number of >>>firms at (t-1), so I would like to include the lagged cumulative count. >>> >>>My basic question is whether it is appropriate (in a statistical >>>sense) to include both the time varying variables and the lagged >>>cumulative count variable. The lagged count aside, I know there are >>>standard extensions to count models to handle time series. However, >>>I'm not sure if anything changes when lagged values of the cumulative >>>dependent variable are added (i.e. are the regular standard errors >>>correct, are estimates consistent, etc....). >>> >>>Can I still use one of the time series count models while including >>>this lagged cumulative value? >>> >>>I would greatly appreciate it if anyone can direct me to relevant >>>material on this. As a note, I have already looked at Cameron and >>>Trivedi's book. >>> >>>Many thanks, >>> >>>Brett >>> >>>______________________________________________ >>>R-help@stat.math.ethz.ch mailing list >>>https://stat.ethz.ch/mailman/listinfo/r-help >>>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >> >>-- >>Spencer Graves, PhD >>Senior Development Engineer >>PDF Solutions, Inc. >>333 West San Carlos Street Suite 700 >>San Jose, CA 95110, USA >> >>[EMAIL PROTECTED] >>www.pdf.com <http://www.pdf.com> >>Tel: 408-938-4420 >>Fax: 408-280-7915 >> > > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA [EMAIL PROTECTED] www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html