> Suppose that you have a logistic equation regression (binary model) from an
> old model that you estimated some years ago. Therefore you know the
> parameters âk (k = 1, 2, ..., p) because they were estimated in the
> past.
> But you don't have the data that were used to fit the model.
>
> My question is: can I introduce this old estimated logistic model in R as
> an object (corresponding to a logistic regression model)?
You have not said how you fitted the model, so you could have anything from an
lm() object to glm, passing through various max likelihood and optimisation
routines on the way, so the answer is properly 'maybe'.
Having said that, for most practical purposes I think the answer is in practice
'no'. All the predict methods would require you to construct an object with the
right class, containing as a minimum the parts of the object used by the
relevant predict() method. For example, if you look at the code in predict.glm,
one of the more common logistic modelling tools, that passes the fitted object
to predict.lm and then uses an inverse link function to bring the predictions
back into the binomial family space. So you will at least need to create enough
of an object to keep predict.lm happy. That in turn will need things like the
terms object, the contrasts, the call offset and so on. It also uses the
object's residuals (for their length, if nothing else) and of course if you
want standard errors on fit or prediction you'll need other things. Without the
data that will all be very problematic and very tedious to invent.
It would be far quicker to write a simple function that does the prediction for
you from the parameters.
S Ellison
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