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Latest news

Starting in June, July and August 2020: Online courses with on-demand video and live Zoom meetings:

  • Data Exploration, Regression, GLM & GAM with an introduction to R.
  • Linear Mixed-Effects Models and GLMM with R-INLA.
  • Introduction to Regression Models with Spatial Correlation using R-INLA.
  • Introduction to Zero Inflated Models using R-INLA.
  • Zero-inflated GAMs and GAMMs for the analysis of spatial and spatial-temporal correlated data using R-INLA. 

See highstat.com for details.

Beginner's Guide to Generalized Additive Models with R (2012)

Zuur, AF

A Beginner’s Guide to Generalized Additive Models with R is, as the title implies, a practical handbook for the non-statistician. The author’s philosophy is that the shortest path to comprehension of a statistical technique without delving into extensive mathematical detail is through programming its basic principles in, for example, R.

Not a series of cookbook exercises, the author uses data from biological studies to go beyond theory and immerse the reader in real-world analysis with its inherent untidiness and challenges.

The book begins with a review of multiple linear regression using research on human crania size and ambient light levels and continues with an introduction to additive models based on deep sea fishery data. Research on pelagic bioluminescent organisms demonstrates simple linear regression techniques to program a smoother.

In Chapter 4 the deep sea fishery study is revisited for a discussion of generalized additive models.

The remaining chapters present detailed case studies illustrating the application of Gaussian, Poisson, negative binomial, zero-inflated Poisson, and binomial generalized additive models using seabird, squid, and fish parasite studies.

Visit the website of this book to download data, R code, the table of content, and information how to purchase this book.