Verwandte Artikel zu A Beginner's Guide to Generalised Additive Mixed...
Zu dieser ISBN ist aktuell kein Angebot verfügbar.
Inhaltsangabe
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).
Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.
Críticas
This book is the foremost text on generalised additive mixed models. Presented from both the frequentist and Bayesian perspective using real ecological data, the theory is clearly explained, and full working code in R is provided so that an analyst can easily employ these methods into their own research. Professor Joseph M Hilbe. Arizona State University. Jet Propulsion Laboratory, Caltech
Reseña del editor
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).
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
(Keine Angebote verfügbar)
Buch Finden: Kaufgesuch aufgebenSie finden Ihr gewünschtes Buch nicht? Wir suchen weiter für Sie. Sobald einer unserer Buchverkäufer das Buch bei AbeBooks anbietet, werden wir Sie informieren!
Kaufgesuch aufgeben
