I want to do LCGA with a cohortsequential design. I ran LCGA's with slope and intercept constrained and unconstrained, but I don't get a chiČ value. classes=C(6); knownclass = C(cohort=1 cohort=2 cohort=3 cohort=4 cohort=5 cohort=6); model: %overall% I S | LAC1@0LAC2@1LAC3@2LAC4@3; I; S; I with S; %c#1% [I] (1); [S] (2); %c#2% I S | LAC2@1LAC3@2LAC4@3; [I] (1); [S] (2); %c#3% I S | LAC1@1LAC2@2LAC3@3LAC4@4; [I] (1); [S] (2); %c#4% I S | LAC1@2LAC2@3LAC3@4; [I] (1); [S] (2); %c#5% I S | LAC1@3LAC2@4; [I] (1); [S] (2); %c#6% I S | LAC1@4; [I] (1); [S] (2);
How do I get a chiČ to compare the models through chiČ difference test?
So from this part of the output of each model? Loglikelihood
H0 Value -15474.940 H0 Scaling Correction Factor 1.8329 for MLR
I also found this: https://www.statmodel.com/chidiff.shtml But I don't get a loglikelihood for H1 with LCGA, only when I do LCGM. In that formula, would L0 be the logelikelihood of the constrained model and L1 of the unconstrained model? Or do I need to use another formula?