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normlong
2010 Feb 24
0
Extracting individual parameter estimates from mmlcr
...number of classes that best describes my data. I am able
to find model
parameters such as degrees of freedom, loglikelihood, and BIC.
For example, here is a cubic 3-class model I am using.
mmlcr1 <- mmlcr(outer = ~ 1 | ID, components = list(list(formula = Score ~
poly(wave,3),
class = "cnormlong", min = 1, max = 4)), data = datlong, n.groups = 3)
In this model I can find these parameters as follows:
mmlcrl$df
mmlcr1$loglikelihood
mmlcr1$BIC
And I can identify individual parameter estimates such as posterior
probabilities of
belonging to specific classes, specific data points, corre...