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| Date | Topic(s) | Slides (html) | Source (Rmd) | Slides (pdf) | Background |
|---|---|---|---|---|---|
| 30 March |
Course overview |
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| 1 April |
Required software R / RStudio R Markdown GitHub |
R software RStudio Intro to R Markdown Video intro to GitHub |
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| 3 April |
Ecological data & distributions Detections Counts Survival |
Understanding
data Statistical distributions Distribution relationships |
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| 6 April |
Linear models Non-linear approximations Regression, ANOVA, ANCOVA |
Farraway
(2015) Chap 2 |
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| 8 April |
Linear models Models in matrix form Least squares Identifiability |
Farraway
(2015) Chap 2 |
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| 10 April |
Inference Variance partitioning F-tests for nested models Confidence intervals |
Farraway
(2015) Chap 3 Farraway (2015) Chap 4 Wasserstein et al. (2019) |
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| 13 April |
Model diagnostics Assumptions about errors Leverage Outliers |
Farraway
(2015) Chap 6 |
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| 15 April |
Problems with model errors Generalized least squares Weighted least squares Robust regression |
Farraway
(2015) Chap 8 |
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| 17 April |
Data transformations Box-Cox Powers/roots Logarithms |
Farraway
(2015) Chap 9 O’Hara & Kotze (2010) Xiao et al. (2011) |
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| 20 April |
Design matrices Models in matrix form Regression ANOVA / ANCOVA |
Venables
(2018) |
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| 22 April |
Review session All topics to date |
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| 24 April |
Maximum likelihood estimation Relationship to probability Estimation Characteristics of MLE |
Faraway
(2016) Appendix A |
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| 27 April |
Model selection Bias-Variance trade-offs In-sample selection AIC |
Burnham
& Anderson (2002) Hobbs & Hilborn (2006) |
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| 29 April |
Model selection Out-of-sample selection Cross-validation Multi-model inference |
Aho et al (2014)
Banner & Higgs (2017) Burnham & Anderson (2002) Cade (2015) Hooten & Hobbs (2015) Link & Barber (2006) |
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| 1 May |
Mixed effects models Fixed vs Random effects Shrinkage Costs/benefits |
Freeman’s
visualization Harrison et al (2018) Zuur et al (2009) Chap 5 |
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| 4 May |
Mixed effects models Inference Diagnostics Model selection |
Gurka (2006) Harrison et al (2018) |
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| 6 May |
Guest lecture: Dr. Staci Amburgey Using quantitative ecology for species conservation in the face of anthropogenic change |
Amburgey et al (2017) Amburgey et al (2019) Amburgey et al (2020) |
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| 8 May |
Introduction to GLMs Data distributions Link functions Linear predictors |
Nelder &
Wedderburn (1972) Faraway (2016) Chap 8 |
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| 11 May |
Modeling binary data Logistic regression Model selection Diagnostics Project plan due |
Faraway
(2016) Chap 2 |
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| 13 May |
Overdispersion in binary data Variance inflation Beta-binomial modes Quasi-likelihood |
Faraway
(2016) Chap 2 & 3 |
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| 15 May |
Modeling count data Poisson regression Leverage and influence Diagnostics |
Faraway
(2016) Chap 5 St-Pierre et al. (2017) |
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| 18 May |
Overdispersion in count data Variance inflation Quasi-likelihood Negative-binomial distribution |
Faraway
(2016) Chap 5 Ver Hoef & Boveng (2007) |
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| 20 May |
Zero-truncated & zero-inflated models Zero truncation Zero inflation Hurdle models Mixture models |
Faraway
(2016) Chap 5 Zuur et al (2009) Chap 11 Blasco-Moreno (2019) |
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| 22 May |
Working with GLMMs Computing likelihoods Diagnostics Goodness-of-fit |
Faraway
(2016) Chap 13 Zuur et al (2009) Chap 13 Bolker et al (2009) |
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| 25 May |
Memorial Day - No class |
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| 27 May |
Review of materials |
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| 29 May |
Course synthesis What did we learn? Where do we go from here? |
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| 1 June |
Presentations of class projects |
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| 3 June |
Presentations of class projects |
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| 5 June |
Presentations of class projects |
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