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I'm trying to run an LTA with 4 time points and 3 classes for each time point and a single covariate (3 level variable modeled with dummy variables)influencing the transitions, which I modeled as an interaction. I can't seem to figure out if the transition probabilities average over the covariate or are calculated without the covariate. My goal is to determine if the transition probabilities differ by level of the covariate. Can I get this matrix by each level of the covariate? Also, how do I interpret the estimate given for regression of the classes (e.g., C1#1, C1#2) on the covariate? Thanks for your help. 


They average over the covariates. There is not automatic way to get this matrix for each level of the covariate. See the Nylund dissertation on the website for detailed information about LTA. 


Thanks for your help. I'm now having trouble with setting my reference class. I tried changing the starting values, but that didn't work. I want no alcohol use to be the reference class (coded as 0 for all the alcohol variables), but with the output below Class 3 is the problem alcohol class (1s on all the alcohol variables). It is a 3 class model with 4 time points. CLASSES = c1(3) c2(3) c3 (3) c4 (3); ANALYSIS: TYPE = MIXTURE MISSING; MODEL: %OVERALL% c2 on c1; c3 on c2; c4 on c3; MODEL c1: %c1#1% [alcopp6r$1  alcsoc6$1*15] (17); %c1#2% [alcopp6r$1  alcsoc6$1*1] (814); %c1#3% [alcopp6r$1  alcsoc6$1*15] (1521); MODEL c2: %c2#1% [alcopp7r$1  alcsoc7$1*15] (17); %c2#2% [alcopp7r$1  alcsoc7$1*1] (814); %c2#3% [alcopp7r$1  alcsoc7$1*15] (1521); MODEL c3: %c3#1% [alcopp8r$1  alcsoc8$1*15] (17); %c3#2% [alcopp8r$1  alcsoc8$1*1] (814); %c3#3% [alcopp8r$1  alcsoc8$1*15] (1521); MODEL c4: %c4#1% [alcopp9r$1  alcsoc9$1*15] (17); %c4#2% [alcopp9r$1  alcsoc9$1*1] (814); %c4#3% [alcopp9r$1  alcsoc9$1*15] (1521); 


For u = 0, 1 a threshold of 15 ensures u = 1 and a threshold of +15 u = 0. Looks like class 1 and 3 should be switched. 


Hi, I'm running a LTA with 2 time points and would like to know whether transition probabilities vary by gender while controlling for age; and vice versa. Age is categorical (6574; 7584;85+ years). I tried regressing on both age and gender but then I can only estimate gender specific transition probabilities one age group at a time and not across age groups. Yes I read chapter 13...a thousand times. Did I miss something? Instead, would it make sense to use a "known class" for gender and regress on knownclass and age to get my gender transition probabilities while controlling for age? Here's what I have in mind: CLASSES = cg (2) c1(4) c2(5); KNOWNCLASS = cg (sex=0 sex=1); ANALYSIS: TYPE = MIXTURE; MODEL: %OVERALL% c2#1c2#4 on c1#1c1#3 cg age; Thanks! I'm running out of ideas here...and the reviewers will be asking for this! 


It sounds like you want the marginal transition effect for a certain covariate from a model with several covariates. If you create a gender knownclass, a transition probability table for c1 x c2 would give you what you want. I don't recall if this is what the output provides, but do try. Otherwise, perhaps you have to estimate say the gender effect at each age and then weight the transitions probabilities with the frequencies of the ages. 


Exactly, I want the marginal transition effect. I did run the model and the output gives me the mean Cg#1 and the regression coefficients for each (C2#. on Cg#1) and (C1#. on Cg#1). It also gives me the overall transition probabilities (c1 x c2) and the (Cg classes x C1 classes). Now, I’m not certain how to calculate the transition probabilities with a known class (e.g. gender). For example, to estimate transition probabilities from C1#1 to C2#1 when female=1. Do I just add the following terms to calculate the log odds (referring to your 2x2 table in chapter 13): a1 + b11 + (Cg#1) + (C2#1 on Cg#1)? For male, I would just use the table as it is since male=0. Thank you for your precious help. LL 


The LTA dissertation by Karen Nylund on our web site under Papers, Latent Transition Analysis gives details on how to compute transition probabilities as a function of covariates. 

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