Ecology 562 Readings

Readings for Ecology 562, Spring 2012

Week	Date	Topic	Readings
1	Jan 9, 11, 13	Multiple regression	Multiple regression in general Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 5, "Multiple linear regression", pp. 125–149. UNC e-book Weisberg, Sanford. 2005. Applied Linear Regression. Wiley, Hoboken, NJ. Chapter 3, "Multiple regression", pp. 47–68. UNC e-book Categorical predictors in regression Weisberg, Sanford. 2005. Applied Linear Regression. Wiley, Hoboken, NJ. Section 6.2, "Factors", pp. 122–130. UNC e-book DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 4, "Multiple regression with categorical predictors: ANOVA and ANCOVA models", pp. 126–154. UNC e-book Introduction to R Allerhand, Mike. 2011. A Tiny Handbook of R. Springer, New York. UNC e-book Zuur, Alain F., Elena N. Ieno, and Erik H. W. G. Meesters. 2009. A Beginner's Guide to R. Springer, New York. UNC e-book For Friday lab session RIKZ data: Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 5, "Linear regression", pp. 49–77. Chapter 27, "Univariate and multivariate analysis applied on a Dutch sandy beach community", pp. 485–489. UNC e-book
2	Jan 18, 20	Multiple regression (continued)	R² and confidence intervals King, Gary. 1986. How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science 30: 666–687. Available online Read pp. 675–678. Payton, M. E., Greenstone, M. H., and Schenker N. 2003. Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? 6pp. Journal of Insect Science 3: 34. Available online
3	Jan 23, 25, 27	Likelihood theory	Maximum likelihood estimation DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 1, "Appendix: Statistical review", pp. 17–37. UNC e-book Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 2, "The maximum likelihood approach to ordinary regression", pp. 21–26. UNC e-book Myung, In Jae. 2003. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47: 90–100. UNC online journal AIC Mazerolle, Marc J. 2006. Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27: 169–180. UNC online journal Anderson, D. R., K. P. Burnham and W. L. Thompson. 2000. Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management 64: 912–923. UNC online journal
4–5	Jan 30, Feb 1, 3, 6, 8, 10	Generalized linear models	GLMs in general Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 12, "The generalization to non-normal distributions", pp. 190–196. UNC e-book Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 7, "Logistic regression and generalised linear models", pp. 117–138. UNC e-book Vittinghoff, Eric, Stephen C. Shiboski, David V. Glidden and Charles E. McCulloch. 2005. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer, New York. Chapter 9, "Generalized linear models", 291–303. UNC e-book Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 6, "Generalised linear modelling", pp. 79–96. Chapter 21, "Analysing presence-absence data for flatfish distribution in the Tagus estuary, Portugal", pp. 389–402. UNC e-book Logistic regression Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 8, "Logistic regression", pp. 263–303. UNC e-book Kleinbaum, David G. 2010. Logistic Regression: A Self-learning Text. Springer, New York. Chapters 1–3, pp. 1–101. UNC e-book Poisson regression Curran, James Michael. 2011. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, Boca Raton, FL. Chapter 6, "Modeling count and proportion data", 211–256. UNC e-book DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Chapter 10, "Regression models for an event count", pp. 348–378. UNC e-book
6	Feb 13, 15, 17	Analysis of temporally correlated data	Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 9, "Serially Correlated Errors", pp. 305–329. UNC e-book Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 16, "Time series analysis—Introduction", pp. 265–288. UNC e-book Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 6, "Violation of independence—Part I", pp. 143–160. UNC e-book Fox, John. 2010. Time-series regression and generalized least squares in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/Appendix-Timeseries-Regression.pdf
7	Feb 20, 22, 24	Mixed effects models	Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 10, "Mixed Models", pp. 331–369. UNC e-book Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 12, "Analyzing longitudinal data I", pp. 213–230. UNC e-book Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 8, "Introduction to mixed modelling", pp. 125–142. UNC e-book Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 5, "Mixed effects modelling for nested data", pp. 101–142. UNC e-book Robinson, Andrew P., and Jeff D. Hamann. 2011. Forest Analytics with R: An Introduction. Springer, New York. Chapter 7, "Fitting linear hierarchical models", pp. 219–273. UNC e-book
8	Feb 27, 29, Mar 2	Generalized estimating equations	General discussion Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 15, "Modeling correlated outcomes with generalized estimating equations", pp. 263–267. UNC e-book Fieberg, John, Randall H. Rieger, Michael C. Zicus, and Jonathan S. Schildcrout. 2009. Regression modelling of correlated data in ecology: subject-specific and population averaged response patterns. Journal of Applied Ecology 46(5): 1018–1025. UNC online journal Vittinghoff, Eric, Stephen C. Shiboski, David V. Glidden and Charles E. McCulloch. 2005. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer, New York. Section 8.4, "Generalized estimating equations", 266–273. UNC e-book Ziegler, A. and M. Vens. 2010. Generalized estimating equations: Notes on the choice of the working correlation matrix. Methods of Information in Medicine 49: 421–425. UNC online journal R implementation Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 13, "Analyzing longitudinal data II", pp. 231–252. UNC e-book Halekoh, Ulrich, Søren Højsgaard, and Jun Yan. 2006. The R package geepack for generalized estimating equations. Journal of Statistical Software 15(2). http://www.jstatsoft.org/v15/i02/paper Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 12, "Generalised estimating equations", pp. 295–321. UNC e-book
9	Mar 5, 7, 9	Spring Break	No Class
10	Mar 12, 14, 16	Generalized additive models	Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 7, "Additive and generalized additive modelling", pp. 97–124. UNC e-book Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 3, "Things are not always linear; additive modelling", pp. 35–69. UNC e-book Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 9, "Smoothers and generalised additive models", pp. 177–196. UNC e-book Fox, John. 2010. Non-parametric regression in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/Appendix-Nonparametric-Regression.pdf Crawley, Michael J. 2002. Statistical Computing: An Introduction to Data Analysis Using S-Plus. Wiley, New York. Extra chapters. Chapter 34: Generalized additive models. http://www.bio.ic.ac.uk/research/mjcraw/statcomp/chapter34.pdf
11	Mar 19, 21, 23	Survival analysis	Kleinbaum, David G. and Mitchel Klein. 2005. Survival Analysis: A Self-learning Text. Springer, New York. Chapter 1, "Introduction to survival analysis", pp. 1–43. Chapter 3, "The Cox proportional hazards model and its characteristics", pp. 83–129. Chapter 7, "Parametric survival models", pp. 257–329. UNC e-book DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Chapter 11, "Introduction to survival analysis", pp. 382–401. UNC e-book Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 11, "Survival analysis", pp. 197–212. UNC e-book Fox, John. 2010. Cox-proportional-hazards regression for survival data in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/Appendix-Cox-Regression.pdf
12	Mar 26, 28, 30	Spatial statistics	Bivand, Roger. 2008. Applied Spatial Data Analysis with R. Springer, New York. Chapter 8, "Interpolation and geostatistics", pp. 191–209. UNC e-book Härdle, Wolfgang. 2007. Statistical Methods for Biostatistics and Related Fields. Springer, New York. Chapter 8, "Spatial statistics", 285–297. UNC e-book Schabenberger, Oliver and Carol A. Gotway. 2005. Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton, FL. Chapter 1, "Introduction", pp. 1–37. UNC e-book
13	Apr 2, 4	Spatio-temporal correlation	Gumpertz, M. L., C.-T. Wu, and J. M. Pye. 2000. Logistic regression for southern pine beetle outbreaks with spatial and temporal autocorrelation. Forest Science 46: 95–107. http://www.srs.fs.usda.gov/econ/pubs/misc/mlg001.pdf Schabenberger, Oliver and Carol A. Gotway. 2005. Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton, FL. Chapter 9, "Spatio-Temporal Process", pp. 431–435. UNC e-book
14	Apr 9, 11, 13	Machine learning methods	Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 9, "Univariate tree models", pp. 143–161. UNC e-book Berk, Richard A. 2008. Statistical Learning from a Regression Perspective. Springer, New York. Chapter 3, "Classification and regression trees", 103–167. UNC e-book Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York. Chapter 9, "Additive models, trees, and related methods", pp. 305–317. UNC e-book Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 9, "Recursive partitioning", pp. 161–176. UNC e-book
15	Apr 16, 18, 20	Models for multinomial data	DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 8, "Multinomial models", pp. 294–308. UNC e-book Kleinbaum, David G. 2010. Logistic Regression: A Self-learning Text. Springer, New York. Chapters 12–13, pp. 429–488. UNC e-book Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Section 9.4, "A detailed example: Ditch data", pp. 152–161. UNC e-book Guisan, Antoine and Frank E. Harrell. 2000. Ordinal response models in ecology. Journal of Vegetation Science 11(5): 617–626. UNC online journal Thompson, Laura. 2009. An S Manual to Accompany Agresti's Categorical Data Analysis, 2nd ed. (2002). pp 35–39, 117–147. https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf
16	Apr 23, 25	Survey sampling	Köhl, Michael, S.S. Magnussen, M. Marchetti. 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Springer, Berlin. Chapter 3, "Sampling in forest surveys", 71–112. UNC e-book Robinson, Andrew P., and Jeff D. Hamann. 2011. Forest Analytics with R: An Introduction. Springer, New York. Chapter 3, "Data analysis for common inventory methods", pp. 94–106. UNC e-book Lumley, Thomas. 2007. Survey Analysis in R. http://faculty.washington.edu/tlumley/survey/ This web site contains a wealth of documentation about the R survey package. Included are help pages, textbook examples, and slides from tutorials. Particularly useful are specifying a survey design, simple summary statistics, regression models, and the slides from a tutorial presented at useR 2006.
	Apr 30		Final Exam, Monday, 2 pm