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.