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A Beginner's Guide to Generalised Additive Mixed Models with R (1)

By: Ieno, Elena N. Saveliev, Anatoly A. Zuur, Alain F.

Part of the A Beginner's Guide to series
0957174152 / 9780957174153
Paperback / softback
Usually dispatched within 4 weeks
United Kingdom
156 x 233 mm 348 pages
Postgraduate, Research & Scholarly  Learn More

A Beginner's Guide to GAMM with R is the third in Highland Statistics' Beginner's Guide series, following the well-received A Beginner's Guide to Generalized Additive Models with R and A Beginner's Guide to GLM and GLMM with R.

In this book we take the reader on an exciting voyage into the world of generalised additive mixed effects models (GAMM).

Keywords are GAM, mgcv, gamm4, random effects, Poisson and negative binomial GAMM, gamma GAMM, binomial GAMM, NB-P models, GAMMs with generalised extreme value distributions, overdispersion, underdispersion, two-dimensional smoothers, zero-inflated GAMMs, spatial correlation, INLA, Markov chain Monte Carlo techniques, JAGS, and two-way nested GAMMs. The book includes three chapters on the analysis of zero-inflated data. Throughout the book frequentist approaches (gam, gamm, gamm4, lme4) are compared with Bayesian techniques (MCMC in JAGS and INLA).

Datasets on squid, polar bears, coral reefs, ruddy turnstones, parasites in anchovy, Common Guillemots, harbour porpoises, forestry, brood parasitism, maximum cod length, and Common Scoters are used in case studies. The R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage (either in the book or on the website for the book).


PBT Probability & statistics, UFM Mathematical & statistical software

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