- For convienence, I provide lecture slides in both single page and 4-up formats.
- Readings from my book, Discrete Data Analysis with R (DDAR) give pre-publication copies of chapters. I do not allow them to be distributed further.
- Readings from Agresti refer to Agresti (2019), Introduction to Categorical Data Analysis, 3rd Ed.
- The R scripts show some of the examples from the lectures. They have been compiled with knitr, so you can see the results.
- Most
**Tutorials**are are provided in PDF form. Sometimes there is a linked R script you can use to save some typing. Another way to do them is to open the file in one window and copy & paste code lines into an R script in an RStudio editor window. - A tutorial in
`.Rmd`form is an interactive document. Save it to your computer, open it in an RStudio editor window and press "Run Document".

Week | Topic | Readings | R files |
---|---|---|---|

1 | Overview [slides]
[4up] [Working with R Studio] [4up] [intro-to-R.Rmd] |
DDAR:
Ch1,
Ch2; Agresti: Ch1 |
R-into.R [] |

2 | Discrete distributions [slides] [4up] | DDAR: Ch3 |
R-data.R
[] binomial.R [] |

3 | Two-Way Tables: Independence & Association [slides] [4up] | DDAR:
Ch4; Agresti: Ch2 |
berk-4fold.R
[] vision-sieve.R [] |

4 | Two-Way Tables: Ordinal Data and Dependent Samples [Tutorial] on two-way tables |
DDAR:
Ch4; Agresti: Ch2 |
msdiag-agree.R
[] haireye-spineplot.R [] |

5 | Loglinear Models and Mosaic Displays
[slides]
[4up] [Tutorial] on loglin models; [Mosaic display animation] |
DDAR:
Ch5; Agresti: 2.7, Ch. 7 |
berkeley-glm.R
[] titanic-loglin.R [] |

6 | Correspondence Analysis
[slides]
[4up] [Tutorial] on CA; |
DDAR: Ch6 |
mental-ca.R
[] mca-presex3.R [] |

7 | Logistic Regression I
[slides]
[4up] [Logistic regression tutorial] |
DDAR:
7.1-7.3; Agresti: 3.1-3.2; Ch 4 |
arthritis-logistic.R
[] cowles-logistic.R [] Arrests-logistic.R [] |

8 | Logistic Regression II [slides] [4up] | DDAR:
7.3-7.4; Agresti: Ch 4-5 |
cowles-effect.R
[] Arrests-effects.R [] berkeley-diag.R [] |

9 | Multinomial Logistic Regression [slides] [4up] | DDAR:
8.2-8.3; Agresti: Ch 6 |
arthritis-propodds.R
[] wlf-nested.R [] wlf-glogit.R [] |

10 | Log-Linear Models I [slides] [4up] | DDAR:
9.1-9.4; Agresti: Ch 7 |
berkely-logit.R [] |

11 | Log-Linear Models II
[slides]
[4up] [Ordinal factors tutorial] |
DDAR:
Ch 10; Agresti: Ch 8 |
mental-glm.R [] |

12 | Generalized Linear Models: Count Data
[slides]
[4up] [Count data GLMs tutorial] |
DDAR:
Ch 11; Agresti 3.3-3.5 |
phdpubs.R
[] quine.R [] |

13 | Generalized Linear Models: Further topics [slides] [4up] | CARME2015: slides | hospvisits-logodds.R [] |