Hi All,
I am constructing "gravity" (spatial interaction) models in R for ecological application. I have linearized the unconstrained model and solved using OLS. However, I would like to do some further, more in-depth, analyses. 1) Is there an R package actually designed for gravity models? I have done several searches on the CRAN site with no luck. My preference would be to use R for this manuscript versus commercially available software. 2) I would like to estimate my linearized unconstrained model using maximum likelihood. This may be a simple solution, but I have yet to figure out how to do this (my apologies if this is something I should have been able to figure out on my own). 3) For a production (origin) constrained model, I can linearize the equation as in the unconstrained model. The models are similar, accept I need to estimate a constant (k) for each origin, but with only one estimate of the remaining parameters. Any ideas on how to operationalize this? I have tried exploring options with multiple regression, but am not finding a solution with the appropriate mechanics. This would also be an ML estimate. 4) Any feedback/experience from people who have used gravity models in R and/or ecological application would be appreciated. 5) I have tried to keep this posting as brief as possible. Please let me know if you need further information to answer my questions. Thank you all in advance Melanie Murphy EPA-STAR Fellow School of Biological Sciences Washington State University e-mail: [hidden email] [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology |
Melanie Murphy wrote:
> Hi All, > > I am constructing "gravity" (spatial interaction) models in R for > ecological application. I have linearized the unconstrained model and > solved using OLS. However, I would like to do some further, more > in-depth, analyses. > > 1) Is there an R package actually designed for gravity models? I > have done several searches on the CRAN site with no luck. My preference > would be to use R for this manuscript versus commercially available > software. > 2) I would like to estimate my linearized unconstrained model using > maximum likelihood. This may be a simple solution, but I have yet to > figure out how to do this (my apologies if this is something I should > have been able to figure out on my own). if you can write a function for the (negative log-)likelihood, these functions will minimize it and provide wrappers for computing likelihood ratio tests, profile confidence intervals, etc. etc.. > 3) For a production (origin) constrained model, I can linearize the > equation as in the unconstrained model. The models are similar, accept > I need to estimate a constant (k) for each origin, but with only one > estimate of the remaining parameters. Any ideas on how to > operationalize this? I have tried exploring options with multiple > regression, but am not finding a solution with the appropriate > mechanics. This would also be an ML estimate. Don't know quite enough about this to answer, but wouldn't this just be equivalent to ANCOVA with parallel slopes? (i.e., something like lm(z~origin+distance), where distance is distance from (any) source? > 4) Any feedback/experience from people who have used gravity models > in R and/or ecological application would be appreciated. > 5) I have tried to keep this posting as brief as possible. Please > let me know if you need further information to answer my questions. More detail might not hurt for those of us who aren't terribly familiar with gravity models. The functions mentioned above (mle, mle2) are very general, but NOT necessarily efficient or stable or avoiding multiple minima for particular kinds of complex problems -- that's why the special-purpose gravity model tools might work better (if they were available) ... Some of the people who work on gravity models in epidemiology (Bjornstad, Ferrari, et al) use R a lot, but I don't know if they have developed special-purpose tools. Ben Bolker _______________________________________________ R-sig-ecology mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology signature.asc (260 bytes) Download Attachment |
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