[R] indicator or deviation contrasts in log-linear modelling

[Michael Friendly]

Maja,

The need to interpret parameters in log-linear models (and therefore, 
the need to understand how the model is parameterized) often vanishes
if you visualize the fitted model or the residuals in a mosaic display.

e.g., ucb1 asserts Admit is jointly independent of Gender and Dept ---
fits very badly, but the residuals show the *nature* of the association
not accounted for.
ucb2 - Admit and Gender conditionally independent, given Dept --- fits 
badly overall, but only in one department.

 > library(vcd)
 > ucb1 <- loglm(~Admit + Gender*Dept, data=UCBAdmissions)
 > ucb1
Call:
loglm(formula = ~Admit + Gender * Dept, data = UCBAdmissions)

Statistics:
                  X^2 df P(> X^2)
Likelihood Ratio 877 11        0
Pearson          798 11        0
 > plot(ucb1)
 > ucb2 <- loglm(~Admit*Dept + Gender*Dept, data=UCBAdmissions)
 > ucb2
Call:
loglm(formula = ~Admit * Dept + Gender * Dept, data = UCBAdmissions)

Statistics:
                  X^2 df P(> X^2)
Likelihood Ratio  22  6   0.0014
Pearson           20  6   0.0028
 > plot(ucb2)


maiya wrote:
> I am fairly new to log-linear modelling, so as opposed to trying to fit
> modells, I am still trying to figure out how it actually works - hence I am
> looking at the interpretation of parameters. Now it seems most people skip
> this part and go directly to measuring model fit, so I am finding very few
> references to actual parameters, and am of course clear on the fact that
> their choice is irelevant for the actual model fit. 
> 
> But here is my question: loglin uses deviation contrasts, so the
> coefficients in each term add up to zero.
> Another option are indicator contrasts, where a reference category is chosen
> in each term and set to zero, while the others are relative to it. My
> question is if there is a log-linear command equivalent to loglin that uses
> this secong "dummy coding" style of constraints (I know e.g. spss genlog
> does this). 
> 
> I hope this is not to basic a question!
> 
> And if anyone is up for answeing the wider question of why log-linear
> parameters are not something to be looked at - which might just be my
> impression of the literature - feel free to comment!
> 
> Thanks for your help!
> 
> Maja


-- 
Michael Friendly     Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University      Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street    http://www.math.yorku.ca/SCS/friendly.html
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