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Linear statistical models 2008 Count data, contingency tables and log-linear models Expected frequency: Log-linear models are linear models of the log.

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Presentation on theme: "Linear statistical models 2008 Count data, contingency tables and log-linear models Expected frequency: Log-linear models are linear models of the log."— Presentation transcript:

1 Linear statistical models 2008 Count data, contingency tables and log-linear models Expected frequency: Log-linear models are linear models of the log expected frequency (log is used as link function)

2 Linear statistical models 2008 A log-linear model for independence The last parameter of each kind can be set to zero

3 Linear statistical models 2008 The saturated log-linear model Independence can be tested by relating the difference in deviance D 2 – D 1 to a  2 distribution with df 2 – df 1 degrees of freedom. What is D 1 and df 1 for the saturated model?

4 Linear statistical models 2008 The multinomial distribution Consider a nominal random variable that takes k distinct values with probabilities p 1, p 2, …, p k Assume that have made n independent observations of that variable Then wher n j is the number of times the j th value is observed Note that n is fixed in a multinomial distribution. If the observations arrive randomly, a Poisson distribution is usually preferable.

5 Linear statistical models 2008 Analysis of example data proc genmod data=linear.snoring; class snore heart; model count = snore heart/link=log dist=Poisson; run; Can a Poisson distribution be justified?

6 Linear statistical models 2008 Higher order tables Consider the following data on drug use Model:

7 Linear statistical models 2008 Terminology A = alcoholC = cigaretteM = marijuana Model A C M: mutual independence model Model A C M A*C A*M C*M: homogenous association model Model A C M A*C A*M: Model in which C and M are mutually independent when controlling for A

8 Linear statistical models 2008 Contingency table with one response variable Consider the example data written in the following form proc genmod data=linear.snoring2; class snore; model heart/total = snore/link=logit dist=binomial; run;

9 Linear statistical models 2008 Poisson regression I Poisson distribution Log link where x is a covariate

10 Linear statistical models 2008 Poisson regression II Poisson distribution Log link where the parameters are row, column and treatment effects


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