Hello,
I'm optimizing a log-likelihood function using the built-in optim()
function with the default method. The model is essentially a Weibull
distribution where the rate parameter changes based on a single covariate,
coded 1 or 0 (that is, when the indicator is 1, the rate parameter changes
to a different value, Beta, where this Beta is included as a model
parameter). I successfully programmed the model and it seems to work fine.
I've done extensive checking, and am confident the model is coded
correctly. But I was experimenting, and ran into something odd: if I set
the entire covariate vector to zero (meaning Beta is always being
multiplied by zero), but leave Beta as a variable to be optimized over,
optim() still changes the value of Beta (even though it's always multiplied
by zero), and furthermore gives different results for the other parameters,
as compared to when I force Beta to be zero and don't optimize over it.
When I first saw this, I imagined I had made a coding error somewhere else.
BUT, here's the kicker: I modified the code so that the log-likelihood
function still includes Beta in the vector of parameters, but draws it
(Beta <- parameters[4]) and then immediately after that sets it to zero. So
essentially, Beta is still in the parameters being optimized, but is
immediately set to zero after being called. When I run this model... it
does the same thing!! optim() changes the value of Beta, which then changes
the values of the other parameters. The ONLY DIFFERENCE between this model
and the fixed zero Beta is that the function being optimized still calls
Beta from the parameters vector (but, again, sets it to zero
immediately--Beta is always zero in any of the calculations).
I hope I've explained this clearly. Does anyone have any idea what could be
causing this phenomenon? Specifically, it troubles me that the other
parameters change.
Thanks in advance,
Ryan
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