Mosaics Psychology 6136: Categorical Data Analysis Mosaics


Text books and readings

Discrete Data Analysis with R Visualizing Categorical Data

Main texts

The main texts for this course are

Supplementary readings:

Topic schedule and lecture notes

Week Topic Readings RR files knitR
1 Overview [slides] [4up] DDAR: Ch1, Ch2; Agresti: Ch1 R-into.R [knitR]
2 Discrete distributions [slides] [4up] DDAR: Ch3 R-data.R [knitR]
binomial.R [knitR]
3 Two-Way Tables: Independence and Association [slides] [4up] DDAR: Ch4; Agresti: Ch2 berk-4fold.R [knitR]
vision-sieve.R [knitR]
4 Two-Way Tables: Ordinal Data and Dependent Samples DDAR: Ch4; Agresti: Ch2 msdiag-agree.R [knitR]
5 Loglinear Models and Mosaic Displays [slides] [4up]
[Tutorial] on loglin models
DDAR: Ch5; Agresti: 2.7, Ch. 7 berkeley-glm.R [knitR]
titanic-loglin.R [knitR]
6 Correspondence Analysis [slides] [4up] DDAR: Ch6 mental-ca.R [knitR]
7 Logistic Regression I [slides] [4up] DDAR: 7.1-7.3; Agresti: 3.1-3.2; Ch 4 arthritis-logistic.R [knitR]
cowles-logistic.R [knitR]
Arrests-logistic.R [knitR]
8 Logistic Regression II [slides] [4up] DDAR: 7.3-7.4; Agresti: Ch 4-5 cowles-effect.R [knitR]
Arrests-effect.R [knitR]
berkeley-diag.R [knitR]
9 Multinomial Logistic Regression [slides] [4up] DDAR: 7.5-7.6; Agresti: Ch 6 arthritis-propodds.R [knitR]
wlf-nested.R [knitR]
wlf-glogit.R [knitR]
10 Log-Linear Models I [slides] [4up] DDAR: 8.1-8.4; Agresti: Ch 7
11 Log-Linear Models II [slides] [4up] DDAR: 8.5-8.11; Agresti: Ch 8
12 Generalized Linear Models: Poisson Regression DDAR: 9.1-9.4; Agresti 3.3-3.5

Assignments

There will be occasional short assignments posted here and announced in class. These assignments are ungraded, unless a graded assignment is announced in advance. Details regarding a useful way of formatting R exercises are described in Assignment 1. See Compiling Notebooks, which describes how to compile HTML, PDF, or MS Word notebooks from R scripts for further details.

Please submit your assignments to me by email, as a PDF, Word, or HTML attachment (together with the associated R file), with a Subject: line "PSYC 6136: Assignment XX". To help me keep them straight, it would be most convenient to name them something like "YourName-AssignXX.{pdf,docx,html}".

Evaluation

There are three components to your evaluation in the course: two take-home projects (each worth 40%) that will involve analysis of one or more data sets together with a research report describing the background, your analyses, results and conclusions. For these, you can use any software you like, although R is strongly encouraged.

  1. Project 1 : a selection of data sets for the material up to and including logistic regression. Due date: Oct. 29

  2. Project 2: a selection of data sets for the material from logistic regression to the end of the course. Due date: Dec. 15

  3. The remaining 20% can be earned either as
    1. an assignment portfolio, containing a selection of your best work (possibly edited/enhanced) on a selection of assignment questions, or by
    2. reading and discussing a journal article related to theory or application of categorical data analysis. For the latter, you can volunteer to give a brief (~15 min) presentation to the class (sometime in Nov.) to earn bonus marks. Due date: Dec. 29

Resources

Statistical software

In lectures and lab sessions I will be using R software nearly exclusively, together with the R Studio user interface for R. You are well-advised to download and install these to your computer so you can follow along.
  • R software guides


    © 2014-- Michael Friendly
    Canonical URL for this course is: http://www.yorku.ca/friendly/psy6136