Dear Ingmar, thank you for your email.
This means that a non-random latent class model for a 2x2x2 table (3
diagnostic tests) and 2 latent classes produces a saturated model. The
predicted frequencies equal the observed frequencies.
Is there any reason to introduce random effects in a saturated
use str(dentistry.lca2) to see all values of the output; among them a value
np for number of parameters, in this case 5*2 for the 5 binary items of 2
classes + 1 for the class proportions, total 11.
hth, Ingmar
On Mon, Aug 27, 2012 at 6:05 PM, Gabriele Accetta <
gabriele.acce...@gmail.com> wrote:
Can anybody, please, explain me how many parameter are estimated using
randomLCA?
For examples, model "dentistry.lca2random" estimate 1 scale (or
variance, b_j) parameter and 2 position parameters (a_cj)? Doesn't
it?
Do I need at least 4 diagnostic tests for such a model?
What happens if I
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