I have a set of data where the response is a proportion. For each event in the experiment, a system will correctly tag
Y items. In the end, I will have something like 100 different results. I have been trying to determine what is the most correct method of fitting the response variable. So far I have uncovered 4 number of options.
- R's built-in
glmusing one line for each of the
Yitems in each experimental event
- R's built-in
glmusing a proportion (
X/Y) with the weight set to
Yfor each experimental event
betaregfit method from the R
- Quasi-likliehood method proposed by Papke and Wooldridge (pdf)
I am unsure how to rank these different methods to know which is the most appropriate. Here are some specific questions:
- How does the likelihood change for these different options (especially between options 1 and 2)?
- What are the pros and cons of the different methods?
- Do these methods have limitations on the types of factors that can be built into the model?
- For the
betaregfunction, are the weights specified the same way as the
- Most people would tell me to use option 2 listed above, but I have seen a lot of pull for option 3. Why should I choose option 3 over option 2?
- What do you do if you do not know the denominator of the proportion (i.e., you cannot properly weight the data for option 2)?
As you can see these questions are quite general and could be the elements of a full academic text. I was just not sure where to go to get the answers to these questions.