This course has no required text, but the following books might be useful. In each of the sections below the references are listed in order from basic to more advanced.
Whitlock, M. and D. Schluter. 2020. 3rd edition. The analysis of biological data. Greenwood Village, Colo., Roberts and Co. Publishers. Resources.
A readable introduction to analyzing data in biology. A good place to refresh your skills. Biol 501 assumes that you have already taken an undergraduate course in statistics and are familiar with basic principles covered in Chapters 1 through 17. Some of the material in the later chapters will be used in this course.
R commands to analyze the data for all examples presented in this 3rd edition are here.
Quinn, G. P. and M. J. Keough. 2002. Experimental design and data analysis for biologists. Cambridge, UK; New York, Cambridge University Press.
An excellent second course in statistics and biological data analysis. Emphasis of examples is ecology, especially marine intertidal ecology. Full of practical information on the best approaches to use in particular circumstances and the reasons why.
Borcard, D., F. Gillet, and P. Legendre. 2018. Numerical ecology with R. 2nd ed. Springer. UBC Online.
Based on the standard reference for ordination and multivariate methods in ecology.
Crawley, M. J. 2012, The R book. 2nd edition. Chichester, England; Hoboken, N.J.,Wiley. UBC Online.
A helpful reference for methods in R,including linear mixed modeling. Explanations not always straightforward. His approach to model simplification is outdated. We’ll be discussing alternative model selection approaches in class.
Dalgaard, P. 2008, Introductory statistics with R. 2nd. ed. New York,Springer. UBC Online.
A clear introduction to the basics of R and how to carry out the standard methods for analyzing data. Better for starters than Crawley’s but less comprehensive.
Fox, J., & S. Weisberg. 2019. An R companion to applied regression. Third Edition. Sage, Los Angeles.
A good go-to book for anything R on linear, mixed, and
generalized linear models. Goes with the car
package.
Galecki, Andrzej T. 2013. Linear mixed-effects models using R: a step-by-step approach. Springer New York. UBC Online.
Takes you through linear models and general least squares as well as linear mixed models.
Pinheiro, J. C. and D. M. Bates. 2000. Mixed-effects models in S and S-PLUS. New York, Springer. UBC online
The standard reference for linear mixed effects modeling
using R. Aimed at an advanced level. At least two of the chapters are
essential reading if you use the nlme library in R to
analyze your data.
Stevens, M. H. H. 2009. A primer of ecology with R. New York,Springer. UBC Online.
Using R to analyze models and data in population and community ecology.
Sarkar, D. 2008. Lattice: Multivariate data visualization with R. New York, Springer. UBC online.
The creator of the lattice graphics package
explains all.
Venables, W. N. and B. D. Ripley. 2002. Modern applied statistics with S-PLUS. 4th. New York, Springer. UBC online.
The standard reference for statistical analysis of data using R and S. Covers most of the methods you will every need.
Wickam, H. 2016. ggplot2: Elegant graphics for data analysis. 2nd edition. UBC online.
How to create graphs using the increasingly popular
ggplot2.
Zuur, A. F., E. N. Ieno and E. Meesters. 2009. A beginner’s guide to R. New York, Springer. UBC Online.
A readable, detailed introduction to data manipulation and plotting in R. Doesn’t get much farther with data analysis than tables and graphs.
Zuur, A. F., E. N. Ieno, N. J. Walker, A. A. Saveliev and G. M.Smith. 2009. Mixed effects models and extensions in ecology with R. New York, Springer. UBC Online.
A useful guide to advanced methods of data analysis in ecology as well as to carrying them out in R. Topics including nonlinear regression, additive modeling, mixed-effects models, nonindependent data, generalized least squares and generalized additive models.
Bolker, B. M. 2008. Ecological models and data in R. Princeton, NJ,Princeton University Press.
A good complement to Hilborn and Mangel, with the added practical information on how to implement the general approach using R. Has a good overview chapter on likelihood.
Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd.New York, Springer. UBC Online
The authoritative treatise on modern approaches to model selection.
Efron, B. and R. Tibshirani. 1998. An introduction to the bootstrap. Boca Raton, FL, Chapman & Hall/CRC Press.
An accessible introduction, at least the first few chapters.
Felsenstein, J. 2004. Inferring phylogenies. Sunderland,Mass, Sinauer.
The master’s voice. Chapter 25 is a clear and compact summary of comparative methods.
Gotelli, N. J. and A. M. Ellison. 2004. A primer of ecological statistics. Sunderland, Mass., Sinauer Associates Publishers.
A clear overview of basic principles and methods in analyzing ecological data. Not particularly rich with data or examples. The overview of multivariate methods is excellent.
Hilborn, R. and M. Mangel. 1997. The ecological detective: confronting models with data. Princeton, NJ, Princeton University Press.
A great overview of how to fit models to data using a likelihood and model selection approach.
Paradis, E. 2012. Analysis of phylogenetics and evolution with R. 2nd ed. New York, Springer. UBC online.
Explains how to carry out phylogenetic comparative analysis using the ape package.
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