R-sig-ecology Digest, Vol 98, Issue 7
Dear, I think the the choice of the model should be based on the distribution assumption of fitness as this a the response variable, but not the trait. If i am,right trait variable is the explanatory variable. If fitness is a count data, you could choose poisson family but you should be carful to appreciate if there is overdispersion or not. In cas of overdispersion, just go you can choose negative binomial... Thank you
On 11 May 2016 at 11:00, <r-sig-ecology-request at r-project.org> wrote:
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https://stat.ethz.ch/mailman/listinfo/r-sig-ecology or, via email, send a message with subject or body 'help' to r-sig-ecology-request at r-project.org You can reach the person managing the list at r-sig-ecology-owner at r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-ecology digest..." Today's Topics: 1. linear model selection analysis (Harriet Jamieson) ---------------------------------------------------------------------- Message: 1 Date: Tue, 10 May 2016 14:33:57 +0100 From: Harriet Jamieson <harrietjamieson86 at gmail.com> To: r-sig-ecology at r-project.org Subject: [R-sig-eco] linear model selection analysis Message-ID: < CABPy4j57ZN_0KoshYMPtEH88L_4Yjj9G9DxuLWsmz8B_QxWChA at mail.gmail.com> Content-Type: text/plain; charset="UTF-8" Hello list, I am attempting a selection analysis sensu Lande and Arnold (1983), where the basic premise is to model relative fitness of individuals as a function of standardised trait values, where the relative fitness is relative to the mean (by dividing fitness values by the mean fitness value, so centred around 1) and the standardised trait values are calculated as z scores, i.e. = (trait - mean) / SD, so they are centred around zero. The idea is that, in its simplest form with a single trait of interest, the results of the model 'fitness ~ trait? should give a coefficient for ?trait? that is a selection gradient. My problem is that my ?trait? in this case is count data, and it doesn?t seem appropriate to transform Poisson data into the standardised trait variable as above. So my first question is whether this is an appropriate way to treat count data? I have considered three alternatives to work round this: 1. Plough ahead and transform the count data into the standardised trait scores and do the analysis. 2. Use the raw count data as the trait variable, unstandardised - this might be fine as a general linear model, but doesn?t give me sensible estimates for selection gradients on the traits. 3. I tried looking for published work where count data was used in a selection analysis - I?m sure I can?t be doing something that strange, but I could only find one other example, and here they did something different, where they actually turned the model around and modelled the trait as a function of fitness, so trait ~ fitness, where fitness was relativised but the trait was kept as count data and a Poisson distribution was specified. I suppose if anyone has any specific experience of selection analysis, this would be extremely helpful. More generally, however, I think I would like some thoughts on the following questions: 1. Is it appropriate to z score transform count data? Why/why not? 2. If the basic model Y ~ X gives a coefficient for X that is the gradient, then how is this interpretation of the coefficient affected if X is raw count data? 3. Does the example I found where the model was flipped round give the same information? I realise the problem is rather specific to a certain application of linear models, but I would be grateful for any insight anyone could offer on how these alternatives change the interpretation of the models. Thanks very much in advance, Harriet [[alternative HTML version deleted]] ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-ecology mailing list R-sig-ecology at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology ------------------------------ End of R-sig-ecology Digest, Vol 98, Issue 7 ********************************************
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