glm predict issue

Giovanni Azua <bravegag <at> gmail.com> writes:

Hello,

I have tried reading the documentation and googling for the answer but

reviewing the online matches I end up

more confused than before.

My problem is apparently simple. I fit a glm model (2^k experiment), and then

I would like to predict the

response variable (Throughput) for unseen factor levels.

When I try to predict I get the following error:

throughput.pred <- predict(throughput.fit,experiments,type="response")

Error in model.frame.default(Terms, newdata, na.action = na.action, xlev =

object$xlevels) : 

 factor 'No_databases' has new level(s) 200, 400, 600, 800, 1000

Of course these are new factor levels, it is exactly what I am trying to

achieve i.e. extrapolate the values

of Throughput.

Can anyone please advice? Below I include all details.

 Any predictors that you want to treat as continuous
(which would be the only way you can extrapolate to unobserved
values) should be numeric, not factor variables -- use 

mydata <- transform(mydata, var=as.numeric(var))

for example.