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
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