R help - Feb 2009 - indicator or deviation contrasts in log-linear modelling

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

edit: this might just be me using the wrong terminology! if idicator and
deviation contrasts come by a different name, I would love to know it! tnx
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On Wed, 18 Feb 2009, 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,
Depends on what you mean by 'uses'.
>From ?loglin
QUOTE:

Details

The Iterative Proportional Fitting algorithm as presented in Haberman 
(1972) is used for fitting the model. At most iter iterations are 
performed, convergence is taken to occur when the maximum deviation 
between observed and fitted margins is less than eps. All internal 
computations are done in double precision; there is no limit on the number 
of factors (the dimension of the table) in the model.

END QUOTE

There are no explicit contrasts in IPF. The $param component returned when 
'param=TRUE' is used is derived from the estimated cell frequencies. You
can transform these to other basis vectors. If there are no structural 
zeros,

 	lm( as.vector( loglin(...,fit=TRUE)$fit ) ~ < your favored contrasts > )

will give you estimates under your favored scheme.

Then too there is the surrogate Poisson approach, which will do this too.

  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).
Yep, glm(). See McCullagh P. and Nelder, J. A. (1989) Generalized Linear 
Models. London: Chapman and Hall. for details on surrogate Poisson 
modelling.
>
> 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!
>
I can think of three:

1) IPF doesn't need the parameters to do its work and do tests based on
    loglinear models. The canonical reference is Bishop, Fienberg, and
    Holland's Discrete Multivariate Analysis, 1975.

2) In many applications, direct inspection of the cell frequencies or
    their estimates is quite natural.

3) Often there are higher order effects (a four way table with 3 way
    interactions, say) , so the lower order parameter values are not easily
    interpreted anyway.

HTH,

Chuck


> Thanks for your help!
>
> Maja
> -- 
> View this message in context:
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> Sent from the R help mailing list archive at Nabble.com.
>
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>
Charles C. Berry                            (858) 534-2098
                                             Dept of Family/Preventive Medicine
E mailto:cberry at tajo.ucsd.edu	            UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/  La Jolla, San Diego 92093-0901

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
Toronto, ONT  M3J 1P3 CANADA