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Have a look at the bio.infer package Regards Mike -----Original Message----- From: r-sig-ecology-bounces at r-project.org [mailto:r-sig-ecology-bounces at r-project.org] On Behalf Of Yong Zhang Sent: 14 March 2011 09...

Dear Yong The simplest approach would be to calculate a phi value for each stream, and then apply conventional anova. There is some background on phi here: http://en.wikipedia.org/wiki/Particle_size_%28grain_size%29 There is an...

Fernando You have transect in both the fixed and random part of the model. Will the model fit when transect is not in the fixed part? Will it fit when you just call lmer with the full model without dredge...

Hi Jeff I don't know about any specific test for synchrony as there are many ways of looking at the problem. But there's plenty of literature looking at modelling of synchrony in the aquatic environment which would hopefully...

Hi Bex Did you mean the dredge function from the MuMIn package? I'm not really sure what you mean by "it does not take into account correlates or significance". The point about dredge is that it is part of...

Hi Rob As it stands your model won't really do what you are expecting. Start with a model like this: fish.mod1<-lmer(fishsize ~ pH + landuse + conductivity + (1 | subcatchment), data=mydata) You don't say whether you have multiple...

Hi Tim The problem you have is firstly that you only have three levels for your random effect, leading to considerable uncertainties, and secondly as you've essentially got a dendritic network, chur is linked to both vord and hint...

Dear Cliff Using a mixed-effects model (also variously termed multilevel or hierarchical model) could well be what you are looking for. These are implemented in several R packages. In your study, in mixed model parlance, patient identifier would be...

Hi Chris lme (LMMs only) can handle unequal variances of residuals at the lowest level in your hierarchy using variance functions: this isn't really what you describe below. This is described in Pinhero and Bates, but for beginners, best...

Hi Tim You haven't really explained where your group variable in the glmm has come from. Moving from glm to glmm you've changed two things, adding the grouping and the autocorrelation as well. You have to be very...

Dear Peter As Thierry said, it is much easier to create the factors in your data frame and then use them in the model specification i.e. Dist_start <- as.factor(Dist_start) ..... lme(Cyperaceae~Site+Ditch_block+Tree_cut...

Chris is unlikely to need a binomial model if the response variable is biomass. glm is generalised linear model: are you sure you don't just want lm (linear model with normal residuals) Chris? You're new to stats, but...

Hi Doug Your first model can't be right, as AM/PM is clearly a fixed effect: it has just two levels and you are only interested in making inferences about those two levels. So you can't have AMPM...

Dear Petter It is indeed difficult to read your model because you are converting to factors on-the-fly within the model. Regarding the nesting, I have never ever seen explicit nesting in the fixed part of the model. Have...

Edgar I agree with what Chris says (1 + BOAT | fYEAR) makes no sense It is probably (1|BOAT) + (1|fYEAR) that you want Providing you have enough boats and years to estimate variance components for these two factors: please feel...

Hi Rob A couple of suggestions 1. You can use the glht command in the multcomp to get predictions for different factor levels with simultaneous se's / ci's, you just need to formulate the correct contrast(s) 2. You...

Hi Rachel Do you accidentally have nlme and lme4 loaded at the same time? If so then try unloading nlme. regards Mike ________________________________________ From: r-help-bounces at r-project.org [r-help-bounces at r-project.org] On Behalf Of...