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How to obtain individual log-likelihood value from glm?

John Fox 
Dear John,

If you look at the code for logitreg() in the MASS text, you'll see that 
the casewise components of the log-likelihood are multiplied by the 
corresponding weights. As far as I can see, this only makes sense if the 
weights are binomial trials. Otherwise, while the coefficients 
themselves will be the same as obtained for proportionally similar 
integer weights (e.g., using your weights rather than weights/10), 
quantities such as the maximized log-likelihood, deviance, and 
coefficient standard errors will be uninterpretable.

logitreg() is simply another way to compute the MLE, using a 
general-purpose optimizer rather than than iteratively weighted 
least-squares, which is what glm() uses. That the two functions provide 
the same answer within rounding error is unsurprising -- they're solving 
the same problem. A difference between the two functions is that glm() 
issues a warning about non-integer weights, while logitreg() doesn't. As 
I understand it, the motivation for writing logitreg() is to provide a 
function that could easily be modified, e.g., to impose parameter 
constraints on the solution.

I think that this discussion has gotten unproductive. If you feel that 
proceeding with noninteger weights makes sense, for a reason that I 
don't understand, then you should go ahead.

Best,
  John
On 2020-08-29 1:23 p.m., John Smith wrote:

Thread (6 messages)

John Fox How to obtain individual log-likelihood value from glm? Aug 28 John Smith How to obtain individual log-likelihood value from glm? Aug 28 John Fox How to obtain individual log-likelihood value from glm? Aug 29 John Smith How to obtain individual log-likelihood value from glm? Aug 29 John Smith How to obtain individual log-likelihood value from glm? Aug 29 John Fox How to obtain individual log-likelihood value from glm? Aug 29