You can create a glm fit with only an offset created from the coefficients
that you have, then use the regular predict function with that.  For
example using the iris data (first fitting a model on the real data, then
fitting a new model using dummy data and the coefficients from the first
fit):

fit1 <- glm( I(Species=='versicolor') ~ Petal.Length + Petal.Width,
   data=iris, family=binomial )
coef(fit1)

dummydata <- data.frame( Petal.Length = rnorm(10), Petal.Width=rnorm(10),
Species = rep(c('versicolor','other'), each=5) )

fit2 <- glm( I(Species=='versicolor') ~ 0 +
  offset(-2.863708 + 1.563076*Petal.Length - 3.153165*Petal.Width),
data=dummydata, family=binomial )

pred1 <- predict(fit1, newdata=iris)
pred2 <- predict(fit2, newdata=iris)
plot(pred1,pred2)
abline(0,1, col='green')



On Wed, Oct 9, 2013 at 2:46 AM, Pedro Carmona Ibáñez <pedro.carmona@uv.es>wrote:

> I have a problem that I am trying to resolve with no success. More than two
> days searching and I didn't get a single clue. Sorry if the answer is out
> there and I didn't find it.
>
> 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 āk (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)?
>
> I would like to use the "predict" function to prove this logistic
> regression with a new set of data (present data) and then check the
> validity of this old model standing the test of time. And to use this
> function you need the object of the logistic regression model.
>
> Thank you very much in advance.
>
>         [[alternative HTML version deleted]]
>
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>


-- 
Gregory (Greg) L. Snow Ph.D.
538280@gmail.com

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