Chris, It is not random effect strictly speaking, but something like that. The problem is this: RSF models are often constructed as mixed models with random intercept. But it is known that the intercept is a function of the other parameters and the available (background) distribution. So a random intercept is then hard to justify, but still used quite a bit.
The matched design in RSF/RSPF is analogous to the matched case-control design in logistic regression. So one can specify a global availability (all pixels in a landscape are equally available for each individual), or local availability (used points and available points in the vicinity are matched for each individual separately). I hope this clarifies the issue. Cheers, Peter -- Péter Sólymos, Dept Biol Sci, Univ Alberta, T6G 2E9, Canada AB soly...@ualberta.ca, Ph 780.492.8534, http://psolymos.github.com Alberta Biodiversity Monitoring Institute, http://www.abmi.ca Boreal Avian Modelling Project, http://www.borealbirds.ca On Wed, Nov 27, 2013 at 4:50 PM, Chris Howden <ch...@trickysolutions.com.au>wrote: > Hi Peter, > > Does it have the ability to fit random effects? Or some other way to > address the pseudoreplication expected in RSF studies using GPS fix data > with little time between fixes ? (Just had a quick look at the rspf > package and I couldn't see any) > > > > Chris Howden B.Sc. (Hons) GStat. > Founding Partner > Evidence Based Strategic Development, IP Commercialisation and Innovation, > Data Analysis, Modelling and Training > (mobile) 0410 689 945 > (skype) chris.howden > ch...@trickysolutions.com.au > > > > > Disclaimer: The information in this email and any attachments to it are > confidential and may contain legally privileged information. If you are > not the named or intended recipient, please delete this communication and > contact us immediately. Please note you are not authorised to copy, use or > disclose this communication or any attachments without our consent. > Although this email has been checked by anti-virus software, there is a > risk that email messages may be corrupted or infected by viruses or other > interferences. No responsibility is accepted for such interference. Unless > expressly stated, the views of the writer are not those of the company. > Tricky Solutions always does our best to provide accurate forecasts and > analyses based on the data supplied, however it is possible that some > important predictors were not included in the data sent to us. Information > provided by us should not be solely relied upon when making decisions and > clients should use their own judgement. > > > -----Original Message----- > From: r-sig-ecology-boun...@r-project.org > [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of Peter Solymos > Sent: Thursday, 28 November 2013 10:33 AM > To: marieline gentes > Cc: r-sig-ecology@r-project.org > Subject: Re: [R-sig-eco] Logistic regression with repeated measures ? > > Marie, > > Your problem and data seems to me a resource selection problem with > matched use-availability design. Estimating procedure for that design is > discussed in Lele and Keim (2006, Ecology 87:3021--3028) and implemented > in the ResourceSelection package: rspf function, see description of > argument 'm' > for specifying matched points for individual birds. The output is a model > for probability of selection given the distribution of environmental > covariates available for these specific individuals. > > Cheers, > > Peter > > -- > PC)ter SC3lymos, Dept Biol Sci, Univ Alberta, T6G 2E9, Canada AB > soly...@ualberta.ca, Ph 780.492.8534, http://psolymos.github.com Alberta > Biodiversity Monitoring Institute, http://www.abmi.ca Boreal Avian > Modelling Project, http://www.borealbirds.ca > > > On Wed, Nov 27, 2013 at 2:29 PM, marieline gentes > <mlgent...@yahoo.com>wrote: > > > Dear list, > > > > I am a bit new to logistic regressions. I am working with GPS data > > from GPS-tracked birds. My objective is to investigate whether various > > covariates influence the probabilty of visiting specific habitats. > > Each bird has visited many habitats during the course of its GPS > tracking. > > > > Here is a small sample of the data: > > > > Bird.ID Year Sex body.index Recapt PrevWeek.Rain AgriYes AgriNo > > UrbanYes UrbanNo CAL 2010 M 21.99155 13-May-10 1.43 0 100 0 100 CAO > > 2011 F -19.91797 27-Apr-11 4.23 54 46 9 91 CFL 2010 F 25.61063 > > 12-May-10 2.16 31 69 2 98 CFP 2010 M -30.65814 13-May-10 1.43 60 40 0 > > 100 > > > > I understand that I have to use logistic regression, with a cbind > > code, because my response variable is not binary anymore (the response > > is a summary of the success vs failures). > > > > Based on R tutorials, I am thinking about codes that would look like > this: > > > > Agri.RainSex = glm(cbind(AgriYes, AgriNo) ~ PrevWeekRain + Sex + Year > > + Year*Sex,family=binomial (logit), data=mydata) However, contrary to > > the examples I see online, my data are from individual birds, not from > > groups of birds. If I had been using the raw binary data, each bird > > would have 100 hundred lines (I converted the percentages into > > success/failures)(all my % are weighted the same - that is not a > > problem here). Am I supposed to take into account some kind of > > repeated measure in my model ? > > > > Notes: > > For people who are thinking about overdispersed data: my data does not > > seem to be overdispersed. But I will inspect that after I am confident > > that my basic model is ok. So this question is about dealing with > > repeated measured, not about adding a random intercept for > overdispersion. > > > > For people who are working with habitat selection models: this is not > > the case here. We are not working on resource selection. We want to > > fit a simple logistic regression on this data as a part of data > > exploration. This ultimate goal is to link contaminant burden with the > > proportion of time spent in a given habitat. > > > > Thank you for your advice, > > > > Marie > > [[alternative HTML version deleted]] > > > > > > _______________________________________________ > > R-sig-ecology mailing list > > R-sig-ecology@r-project.org > > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > > > > > [[alternative HTML version deleted]] > > [[alternative HTML version deleted]]
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