Extensions of GLMs: nonlinear and mixed models
Applied Biological Data Analysis
Preface
Statistics in Modern Biology
1
Statistics in biology
Introduction to R
2
Introduction to R
3
R data types and objects
4
Data import and export
5
Data manipulation with R
Exploratory data analysis with R
6
Basic descriptive statistics
7
Summarizing biological data
8
Visualizing biological data
9
Probability distributions for biologists
10
Comparing data to distributions
11
Data transformation and rescaling
12
Common problems in biological data
13
Planning your data analysis
Introductory statistics with R
14
Introductory statistics with R
15
Tests for differences in mean or location
16
Tests for continous patterns
17
Tests for contingency
Generalized linear models (GLM)
18
Introduction to generalized linear models (GLM)
19
GLM for continuous response data
20
GLM for count data
21
GLM for categorical and proportional responses
Extensions of GLMs: nonlinear and mixed models
22
Nonlinear methods for biology
23
Random effects and mixed models
Multivariate data analysis
24
Introduction to multivariate data and data analysis
25
Dissimilarity and distance
26
Clustering for biologists
27
Ordination
Alternatives to *p*-values: information-theoretic and Bayesian methods
28
Multivariate hypothesis testing
29
Information theoretic model-based inference
Bonus material
30
Introduction to Bayesian inference for biologists
31
Advanced R graphics
32
Classification and regression trees
Appendices
A
Appendix A
Extensions of GLMs: nonlinear and mixed models
Even our curves have curves.
21
GLM for categorical and proportional responses
22
Nonlinear methods for biology