# Introduction to Generalized Linear Models with R (2nd edition)

Date: Mon, Feb 2 - Thu, Feb 5 2015

Hour: 09:30

Speakers: Dae-Jin Lee, BCAM

This is the second edition of the course given in November 2014.

This time an optional pre-course on R basics will be held on Monday 2nd of February from 9:30-11:30.

The Introductory course on GLM's will be from Tuesday to Thursday according to the following dates:

Tuesday (3rd February): 9:30-13:30 (4 hours)
Wednesday (4rd February): 9:30-12:30 (3 hours)
Thursday (5th February): 9:30-12:30 (3 hours)

Registration: Closed

This course is oriented to postgraduate students, researchers and data analysts who need to move beyond standard linear models for modeling data that are not normally distributed. This short course provides an overview of generalized linear models (GLM's) using the R software. GLM's are most commonly used to model binary or count data. These types of data are very common in many research areas such as Biology, Medicine, Engineering, Business, Economics, and many other fields. In this course we will focus on real applications and examples with an emphasis on model validation, estimation and interpretation of the parameters and variable selection and goodness-of-fit.

PREREQUISITES
You must bring your own laptop with R software installed, basic knowledge of R and linear models.

COURSE MATERIAL
Here

CONTENT
1. Introduction to Generalized Linear Models (GLM's)
1.1 Short review of linear regression.
1.2 What is a GLM and why to use them?
1.3 Components of a GLM
2. Models for binary data
2.1 Logistic regression
2.2 Estimation and interpretation of the parameters
2.3 Some examples
3. Multinomial regression
3.1 Function multinom() in R
3.2 Interpretation of the parameters
3.3 Variable selection
3.4 Some examples
4. Ordinal regression
4.1 Proportional odds model
4.2 Function polr() in R
4.3 Some examples
5. Poisson regression
5.1 Poisson distribution
5.2 Poisson regression for incidence rates
5.3 Some examples

BCAM

## Confirmed speakers:

Dae-Jin Lee, BCAM

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