RE: st: Assistance regarding a maximum likelihood model

[Cameron McIntosh <cnm100@hotmail.com>]

Hi Jeremy,
I don't exactly know what "ml check/search/maximize" is supposed to do, but it sounds like you are talking about a Poisson model. Is that what you're estimating below? Are the different activities highly correlated (i.e., those who contribute more hours to one activity tend to contribute more to all of them)? Or is there no such pattern?
If "initial values" means the same thing as starting values for the estimates, these should ideally be a best guess informed by theory and prior empirical work. But in my experience with different programs, it often doesn't matter and the default values work OK. You might want to try a range of plausible starting values and see if you always get convergence to the same optimum, and then you have some confidence that it's the global max.
Best,
Cam  
----------------------------------------
> From: jkea1937@uni.sydney.edu.au
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Assistance regarding a maximum likelihood model
> Date: Wed, 14 Sep 2011 01:23:05 +0000
>
> I am completing a study on contributions of volunteer hours in a not-for-profit organisation, factoring in a number of motives and also controls (i.e. time constraints). These hours have been disaggregated into different activities, with potentially different motives for the hours in each).
>
> I am not a statistics genius but I'm trying to get my head around the model. I'm looking at a maximum likelihood model in order to try to predict where someone would sit (in terms of hours contributed).
>
> I'm having some issues with the 'ml search' command. I understand helping the model find initial values would help, but I'm not sure what to look in terms of the initial values (code below for reference).
>
> Any assistance is greatly appreciated, and I can try clarify any questions others have.
>
> Many thanks.
> Jeremy
>
> Do-file:
>
> capture program drop mixing3
> program mixing3
> version 9.1
> args lj xb1 xb2 xb3 lo1 lo2 ls1 ls2 ls3
> tempvar f1 f2 f3 p p1 p2 p3 s1 s2 s3
> quietly {
> gen double `s1'=exp(`ls1')
> gen double `s2'=exp(`ls2')
> gen double `s3'=exp(`ls3')
> gen double `p'= 1 + exp(`lo1') + exp(`lo2')
> gen double `p1'=1/`p'
> gen double `p2'=exp(`lo1')/`p'
> gen double `p3'=exp(`lo2')/`p'
> gen double `f1' = normden($ML_y1, `xb1', `s1')
> gen double `f2' = normden($ML_y1, `xb2', `s2')
> gen double `f3' = normden($ML_y1, `xb3', `s3')
> replace `lj'=ln(`p1'*`f1' + `p2'*`f2' + `p3'*`f3')
> }
> end
> ml model lf mixing3 /*
> */(xb1: vol_hrs= x1 x2 x3 x4 x5 x6 ) /*
> */(xb2: vol_hrs= x1 x2 x3 x4 x5 x6 ) /*
> */(xb3: vol_hrs= x1 x2 x3 x4 x5 x6 ) /*
> */(lo1: motive1 motive2 motive3 motive4)/*
> */(lo2: motive1 motive2 motive3 motive4)/*
> */(lsd1: ) /*
> */(lsd2: ) /*
> */(lsd3: )
> ml check
> ml search
> ml maximize
>
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