Download Mixed Effects Models and Extensions in Ecology with R by Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. PDF

By Alain F. Zuur, Elena N. Ieno, Neil Walker, Anatoly A. Saveliev, Graham M. Smith

Building at the winning Analysing Ecological Data (2007) by means of Zuur, Ieno and Smith, the authors now supply an accelerated creation to utilizing regression and its extensions in analysing ecological information. As with the sooner e-book, actual info units from postgraduate ecological stories or study tasks are used all through.

The first a part of the e-book is a mostly non-mathematical advent to linear combined results modelling, GLM and GAM, 0 inflated versions, GEE, GLMM and GAMM. the second one half offers ten case stories that variety from koalas to deep sea learn. those chapters supply a useful perception into analysing complicated ecological datasets, together with comparisons of other ways to a similar challenge. by means of matching ecological questions and information constitution to a case research, those chapters offer a good start line to analysing your personal facts.

Data and R code from all chapters can be found from www.highstat.com

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Extra info for Mixed Effects Models and Extensions in Ecology with R (Statistics for Biology and Health)

Example text

The ‘but. ’ refers to the following. Always apply the simplest statistical technique on your data, but ensure it is applied correctly! And here is a crucial problem. In ecology, the data are seldom modelled adequately by linear regression models. If they are, you are lucky. If you apply a linear regression model on your data, then you are implicitly assuming a whole series of assumptions, and once the results are obtained, you need to verify all of them. This is called the model validation process.

11 shows an xyplot from the lattice package. Each panel represents a station. The underlying questions are (i) how to model the bioluminescent–depth relationship and (ii) how to deal with the data of difference stations. The following code was used to read the data and make the lattice panel. grid(h = -1, v = 2) I1 <- order(x) llines(x[I1], y[I1], col = 1)}) You can see this code is slightly more complicated than used for Fig. 4. In this code, we used a panel function that automatically splits up the data by station.

Chapter 8 contains an explanation of the Poisson, negative binomial, Bernoulli, binomial, and zero-truncated distributions. GLM and GAM models are discussed in Chapters 9, 10, and 11, and finally, Chapters 12 and 13 contain GEE, GLMM, and GAMM. Associated case studies are printed outside the triangle. 4 Where to Go from Here 33 modelling approach, and if combined with GLM and GAM, to generalised linear mixed modelling (GLMM) and generalised additive mixed modelling (GAMM). Chapter 4 shows how we can deal with heterogeneity in linear regression and smoothing models, random effects for nested data are introduced in Chapter 5, and temporal and spatial correlation structures are discussed in Chapters 6 and 7.

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