This page is under continuous revision, with new information added as the term proceeds.

Lectures (Tuesdays 1-3 pm) and workshops (Thursdays 1-3 pm) are held in Beaty Biodiversity 224.

Below is an approximate list of lecture topics and contents. Links to the lecture slides will be added before each lecture. Michael Price from SFU is taping each lecture, and I will provide those links too. Click the refresh button on your browser to make sure you are seeing the latest version of this page.

## Introduction

Lecture overheads

Lecture video

About the course

Course objectives

About the instructor

Why we use R

Organizing data for use in R

## Graphics

Lecture overheads

Lecture video

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

Lecture video

Plan your sample size

Experiments vs observational studies

Why do experiments

Clinical trials: experiments on people

Design experiments to minimize bias and effects of sampling error

Analysis follows design

What if you can’t do experiments

## Linear models

Lecture overheads

Lecture video (only the first half got recorded)

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

Related methods in R

## Mixed effects models

Lecture overheads

Lecture video

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

Assumptions of linear mixed-effects models

An example violating the assumptions, with solutions

## Likelihood

Lecture overheads

Lecture video

Probability and likelihood

Maximum likelihood estimation

Example: estimate a proportion

Likelihood works backward from probability

Likelihood-based confidence intervals

Example: estimate speciation and extinction rates

Log-likelihood ratio test

Example: test a proportion

## Generalized linear models

Lecture overheads

Lecture video

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

## Model selection

Lecture overheads

Lecture video

Example: polynomial regression

The problem of model selection

Choose among models using an explicit criterion

Goals of model selection

Criteria: Mallow’s *C _{p}* and AIC

Search strategies: All subsets; stepAIC

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

Lecture video

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

Lecture video

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

Lecture video

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

Publication bias

Make your results accessible to meta-analysis

Consider a mata-analysis for your first thesis chapter

Best practices

## Multivariate statistics

Lecture overheads

Lecture video

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

Lecture video

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

A method for discrete data (and issues)