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Below is an approximate list of lecture topics and contents. Links to the lecture slides will be added before each lecture.

Lecture overheads and videos from previous years are below.

Introduction

Lecture overheads
About the course
Course objectives
About the instructor
Why we use R
Organizing data for use in R

Graphics

Lecture overheads
The purpose of graphs
Principles of effective display
Types of graphs to achieve these principles
How some graphs fail, and what can be done
What about tables?

Design of experiments

Lecture overheads
Plan your sample size
Experiments vs observational studies
Why do experiments
Clinical trials: experiments on people
Design to minimize bias and effects of sampling error
Analysis Follows Design
What if you can’t do experiments

Linear models

Lecture overheads
What is a linear model
Several examples
Estimating parameters vs testing hypotheses
Model comparison: full vs reduced models
Sequential vs marginal testing of terms
The lure of model simplification
Perils of correcting for covariates
Assumptions of linear models
Other related methods in R

Mixed effects models

Lecture overheads
Random vs fixed effects
Two-factor ANOVA example
Why the calculations are different with random effects
Unbalanced designs with random effects
Examples of experiments with random effects
Linear mixed-effects models
Example: Estimating repeatability of a measurement
Other designs with random effects, briefly
Assumptions of linear mixed-effects models
An example violating an assumption, with a solution

Likelihood

Lecture overheads
Probability and likelihood
Maximum likelihood estimation
Example: estimate a proportion
Likelihood works backward from probability
Likelihood-based confidence intervals
Example: estimate survival rates
Log-likelihood ratio test
Example: test a proportion

Generalized linear models

Lecture overheads
What is a generalized linear model
Linear predictors and link functions
Example: estimate a proportion
Analysis of deviance table
Example: fit dose-response data using logistic regression
Example: fit count data using a log-linear model
Advantages and assumptions of glm
Quasi-likelihood modeling when there is excessive variance
Example: model contingency tables

Model selection

Lecture overheads
Example: polynomial regression
The problem of model selection
Choose among models using an explicit criterion
Goals of model selection
Search strategies: dredge(), stepAIC()
Criterion: AIC
Example: predicting ant species richness
Several models may fit about equally well
The science part: formulate a set of candidate models
Example: adaptive evolution in the fossil record

Bayesian data analysis

Lecture overheads
What is probability
Another definition of probability
Bayes Theorem
Prior probability and posterior probability
How Bayesian inference is different from what we usually do
Example: one species or two
Example: estimate a proportion
Credible intervals
Bayes factor
Bayesian model selection

Bootstrap and resampling

Lecture overheads
Estimation and hypothesis testing
Permutation test
Estimation
The sampling distribution
The bootstrap standard error
The bootstrap confidence interval
Comparing two groups

Meta-analysis

Lecture overheads
Meta-analysis compared with traditional review article
Quantitative summaries compared with vote-counting
How to carry out a meta-analysis
Effect size
Fixed and random effects meta-analysis
Correcting for publication bias
Make your own results accessible to meta-analysis
Consider a meta-analysis for your first thesis chapter
Current best practices

Multivariate statistics

Lecture overheads
Why do a multivariate analysis
Ordination, classification, model fitting
Principal component analysis
Discriminant analysis, quickly
Species presence/absence data
Distance data

Species as data points

Lecture overheads
Example: the problem with species data
Phylogenetic signal in ecological traits
Why phylogeny matters in comparative study
Phylogenetically independent contrasts
A linear model (general least squares) approach
Discrete data Phylogenetic methods have many applications R: An embarrassment of riches
Use R!

 

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