Hi,
I am running gllamm on a data file in which "scene" is nested inside
"idno" (please see code below). Each idno has responded to 5 or less
scenarios.
At idno level, I have 2 latent (reflective) factors made up of 2 and
3 indicator (observed) items respectively.
At the scene level, I have 1 latent (reflective) factor made up of 2
indicator (observed) items.
The scene level latent factor is regressed on the two idno level
latent factors.
I have omitted the random effect from idno to scene for now. The
code is:
gen scene=_n
reshape long y,
i(idno scene) j(item)
tab item, gen(d)
eq load1: d1 d2
eq load2: d3 d4
d5
eq load3: d6 d7
matrix bm1
=(0,1,1\0,0,0\0,0,0)
eq f1: x1 x2 x3
gllamm y
d2-d7,
i(scene idno) l(oprob) bmat(bm1) nrf(1 2) eqs(load1 load2 load3)
geqs(f1)
trace nip(5) nocorrel
The problem is that the lambdas for d4 and d5 are large positive
numbers (around 200), while the regression weight of the factor made
up of d3, d4, and d5 on the scene factor is becoming a large
negative number (around -300). The variance of the both factors are
reasonable. Is this a convergence problem? The "adapt" option is not
helping either. Any suggestions?
Thanks
Nirup
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