Model: %within% y on yg11-yg14 yic11-yic14 x12-x31; %between% y#1 with y#2 y#3 y#4; y#2 with y#3 y#4; y#3 with y#4;
*** WARNING in MODEL command One or more between-level covariances involving the following variable is free while its between-level variance is fixed at 0. Fix all between-level covariances with this variable at 0 or free its variance. Problem with: Y#1
it's same for Y#2, Y#3
Between Level Y#1 WITH Y#2 0.000 Y#3 0.000 Y#4 0.000
Y#2 WITH Y#3 0.000 Y#4 0.000
Y#3 WITH Y#4 0.000
Means Y#1 -4.226 Y#2 -8.726 Y#3 -8.248 Y#4 3.603
And I need constant's coefficients, but did not calculate that.
Sorry for seemingly basic questions. I really need your help, professor.
But note that this results in 4-dimensional integration which is heavy, slow, and imprecise (you can try Integration = Montecarlo(5000). You may want to put a factor behind these 4 as shown in UG examples for 2-level mixture modeling (the latent class variable is also nominal).
Yohan Choi posted on Friday, September 14, 2018 - 6:57 pm
Thanks professor. Very thanks for your kind reply.
I have one more question. Can I have predicted probability with 95% CI with some fixed covariates in this model?
Send your example to firstname.lastname@example.org. I can't replicate what you are describing. It shouldn't happen. You can add U#3@0 U#4@0 U#5@0; and that should eliminate the random effects for the additional categories. If you see covariances for these random intercepts that means that they are numerically integrated and should be reflected in the dimensions of numerical integration. If only two intercepts are free you would have just 2 dimensions of numerical integration. If you have covariances there will be more dimensions of numerical integrations and the run would be very slow. If there are covariance there must be also variances since the variance covariance for the random effects is always positive definite , i.e., the variance must also be free.
Hi There, I am trying to run a random effects 2 level multinomial logistic regression where the outcome is coded as 0 (reference), 1 and 2. My model is running using the below code but my effect sizes are in the opposite directions as expected. Please let me know if you see any issues with the code! Thanks so much,
usevariables are s_female s_ageyrs j_pared2 j_int j_ext median enrol js_Sel js_Sel_mean t_Sel3_mean ov2 t_selprog_m5 ;
I am now trying to estimate several univariable models using FIML where I only have a variable measured at level one included in the analysis, but I still want to make sure I am appropriately adjusting for the multilevel nature of the data. Therefore, I am wondering if when you have a variable only measured at the individual level do you have to estimate the variances of your outcome at the upper level?
For example, which of the following is correct:
usevariables are js_Sel ov3;
nominal is ov3; idvariable x_student_ID; cluster= x_idschool;! teach_id; within= js_Sel ; missing are all (999); define: if (ov2==0) then ov3=2; if (ov2==1) then ov3=1; if (ov2==2) then ov3=0;
With continuous DVs as in UGex9.1, Mplus estimates the Between-level variance without it having to be mentioned in the input. You can mention it if you want to. Either way, it will show up in the output. With other outcomes like your nominal DV, Mplus does not do that automatically because it may lead to too heavy computations due to many dimensions of integration. So you have to specifically mention the (residual) variances you want estimated.
The rationale for adding the residual variance in your case is that you want to be able to estimate R-square and not unrealistically set it at 1 (by analogy of regular linear regression).
No, saying Type=Twolevel is not enough to account for clustering - you also have to follow through and specify the model with some between-level variation which is how the clustering gets captured. See e.g. my 1994 paper on our website:
Muthén, B. (1994). Multilevel covariance structure analysis. In J. Hox & I. Kreft (eds.), Multilevel Modeling, a special issue of Sociological Methods & Research, 22, 376-398. download paper contact author