[R] Model building ...
j.logsdon at lancaster.ac.uk
Thu May 6 18:20:15 CEST 1999
Are there any functions that de-convolute data into a given number of
clusters, rather like the NPMLE GLIM macros from Murray Aitkin and Brian
Francis? Basically I would like to code into R the same approach but
include the possiblility of some data being censored. In principle the
formulae are the same (just replace the likelihood function) but I haven't
managed to get my head round the model building problems.
I am looking for an elegant way of doing the following:
Consider a model where the variables are partitioned into two groups -
call them fixed and random. I have these as data frames - each can be a
mixture in principle of vectors and factors. Then I want to fit:
where Y is the response (possibly including the censoring indicator), F is
a factor. The response, the factor F and the random and fixed data frames
are stacked N long for N mass points. F indexes the mass point.
I have been trying to build up the model and use gnlr() except the
complexities are beating me at the moment. Should I use a list or a model
frame? Can anyone advise?
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