title: How to assess AIC of a training model on a testing dataset using the coefficients of the training model?
body: <p>Let's say I have a lognormal model like this (an example) where I have already split my training and testing datasets:</p>
  
  <p>glm(log(Death)~Age+Medication+IQ,data=death.rate.train,link=gaussian(link="identity"))</p>
  
  <p>How would I create another GLM model with the beta coefficients from the training dataset in order to see the overall goodness of fit using AIC or BIC, etc.? Could I possibly do something like this, where the variable.train would be the coefficients from the training model?</p>
  
  <p>AIC(glm(log(Death)~offset(Age.train+Medication.train+IQ.train),data=death.rate.test,link=gaussian(link="identity")))?</p>
  
  <p>Is there an easier way? (Please tell me there is!! I have a lot more variables with multiple factor levels.)</p>