

LCA with a distal outcome and covariates 

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Paul Spin posted on Friday, December 01, 2017  3:19 pm



A relative newbie to Mplus... I am attempting to model Y= B0 + B1*LAS + B2*Z + error where Y is a count variable, LAS is a latent class variable w/ 3 categories, and Z is a vector of covariates. I am following Web Notes 21 [Section 3.2], which returns classspecific intercepts and slope for each Z. Instead, I would like to get classindependent main effects for Z while still allowing for classspecific intercepts. Here is my code: TITLE: STAGE 1: Estimate latent class model; DATA: File = data.csv ; VARIABLE: NAMES = y a1a8 z; USEVARIABLES = a1a8 ; CATEGORICAL = a1a8; CLASSES = c(3); AUXILIARY = y z; ANALYSIS: TYPE = MIXTURE; Savedata: File = lcaoutput.csv ; Save = bchweights; TITLE: Y on C and Z ; DATA: File = lcaoutput.csv: VARIABLE: NAMES = y a1a8 z W1W3 MLC; USEVARIABLES = y z W1W3 ; CLASSES = c(3); Training = W1W3(bch); ANALYSIS: TYPE = MIXTURE; MODEL: %Overall% C y on z; %c#1% y on z; %c#2% y on z; %c#3% y on z; Q1: How to I modify the second input file to obtain what I described above? Q2: How do I test for statistically significant differences in the classspecific intercepts? Thank you! 


Q1: Try dropping the classspecific y on z statements. The intercept varies by class as the default. Q2: Give parameter labels to the classspecific intercepts in the Model command, like: %c#1% [y] (p1); %c#2% [y] (p2); %c#3% [y] (p3); and then use Model Test to do Wald testing like testing if all 3 are the same: 0 = p1p2; 0 = p3p1; 

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