Alice Frye posted on Thursday, September 25, 2008 - 6:16 am
I've been running some LPA with continuous and rare event count variables. I have used NB(i) for the rare event count variables. I find that within a 3 class model, for example, the estimate for the inflation term is identical (really identical, not just close) across classes within a model. The term representing scores of one or more varies across classes like other estimates. This also occurs if I use zero inflated poisson regression for the count variables.
I'd be grateful for any thoughts on why that is and/or what it means.
It is the Mplus default that these parameters are held equal across classes. To relax the equalities, mention the parameter in the class-specific parts of the MODEL command.
Alice Frye posted on Wednesday, October 15, 2008 - 1:15 pm
This is another question about using NB (i) with LPA.
I have run a model in which the estimates of the inflation terms are allowed to vary across classes (as are all the other point estimates of variables in the model). For example in one class an inflation term representing having not committed spouse abuse or having committed spouse abuse is estimated at -1.25.
Can anyone tell me how I can express an inflation term as probability--the probability of having not committed the act or having committed the act? Or what syntax I would use to produce this information along with the other results?
In Mplus, u# is a binary latent inflation variable for a certain count outcome u and u#=1 indicates that the individual is unable to assume any value except 0. So an estimate of [u#] is a logit intercept/mean, say m, so that
P(u#=1) = 1/(1+exp(-m)).
For example, m = -15 implies that this probability is zero so there is no inflation - nobody is unable to assume any value except 0 (prob=0 for the zero class), i.e. everyone follows the regular NB with counts 0, 1, 2,....