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 [] |