st: RE: GLLAMM -- soprobit link

[Reinhardt Jan Dietrich <jan.reinhardt@paranet.ch>]

You could try to use the starting values from the oprobit version for your soprobit model by adding from (e(b)) to the soprobit code (after having run the other model). Maybe that helps. 
I do not know if you can use the usual maximization options with gllamm but if so try the following options: difficult (uses another stepping algorythm); technique (nr dfp) switches between Newton-Raphson and the Davidon-Fletcher-Powell algorythm) ...
Best
Jan

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of George Bouliotis
Sent: Freitag, 17. Februar 2012 13:05
To: statalist@hsphsun2.harvard.edu
Subject: st: GLLAMM -- soprobit link

Dear Statalist members,

I am using a dataset of 30 rates evaluating 34 items in an attempt to implement a polytomous ordered probit model with rater-specific measurement errors and thresholds, as it is described in the section 7.12 , book "Multilevel and Longitudinal Modeling Using Stata; Hesketh and Skrondal".

Unfortunately, and despite numerous model modifications (e.g. nip(40), matrices, initial values etc)  , the Newton Raphson algorithm fails to converge although adaptive quadrature is reasonably stable. The usual error message "flat or discontinuous range" emerges any time I use the link "soprobit" whereas full convergence is achieved with "oprobit" links. Please see below some Stata output.

My questions are:
1.	Is there any other trick/way for achieving full convergence? Would it make any difference if I omit a particular rater or raters from the model? Note that I do not have missing values.
2.	Is there any other way for taking rater-specific thresholds with GLLAMM or other command without using "soprobit"? 
3.	Finally, could I base inference on only adaptive quadrature convergence?

Thank you in advance
George


Dr. George Bouliotis

Research Fellow in Biostatistics
MRC-MHTMR (Midland Hub for Trials Methodology Research) 
University of Birmingham


----------------MODEL SPECIFICATION WITH GLLAMM
gllamm   cat rtr1-rtr29, i(item) l(oprob) f(binom) adapt 
estimates store GLLAMM1
**COND.NO: 20.61

gllamm   cat rtr1-rtr29, i(item) l(oprob) f(binom)  
estimates store GLLAMM3
**COND.NO: 26.68

gllamm   cat rtr1-rtr29, i(item) l(oprob) f(binom) adapt iter(0)
estimates store GLLAMM5
***COND.NO: 28.12
*********Adaptive quadrature has not converged


***********
***********
***********


gllamm   cat rtr1-rtr29, i(item) l(soprob) f(binom) adapt 
estimates store GLLAMM2
**COND.NO:1191.92

gllamm   cat rtr1-rtr29, i(item) l(soprob) f(binom)  
estimates store GLLAMM4
**COND.NO: 4706.49

gllamm   cat rtr1-rtr29, i(item) l(soprob) f(binom)  from(M1) skip
estimates store GLLAMM6
***Condition Number = 3273.9524


***********
*********** HETEROSCEDASTIC ERRORS 
***********


eq het: rtr1-rtr16 rtr18-rtr30 //measurement error
gllamm   cat rtr1-rtr16 rtr18-rtr30, i(item) l(soprob) ///
fam(binom) adapt s(het) nip(20) iterate(20) from(M3) skip init

estimates store GLLAMM8
***NO convergence for adaptive ---Con number: NO




***********
*********** THRESHOLDS 
***********
eq thr: rtr1-rtr29 //thresholds
gllamm   cat rtr1-rtr29, i(item) l(oprob)  thres(thr) from(M3) skip  adapt
***Adaptive converged , Newton Raphson NO convergence ---Con number: NO

eq thr: rtr1-rtr16 rtr18-rtr30  //thresholds RATER17 ref-guy
gllamm   cat rtr1-rtr16 rtr18-rtr30 , i(item) l(oprob) f(binom) thres(thr) adapt init
***Adaptive converged , Newton Raphson NO convergence ---Con number: NO

eq thr: rtr1-rtr16 rtr18-rtr30  //thresholds RATER17 ref-guy
gllamm   cat rtr1-rtr16 rtr18-rtr30 , i(item) l(oprob) f(binom) thres(thr) adapt init
***Adaptive converged , Newton Raphson NO convergence ---Con number: NO




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