Joseph Cote posted on Saturday, June 09, 2012 - 12:11 am
How can you constrain basic SEM parameters is MPlus? I am trying to constrain the variance of TD(31,31)> 0 (the estimated error variance is negative). But I can't find information about the notation used in MPLUS for various parameters. In EQS I would use the command /CON (E31,E31)>0; (for V31=*F1+E31;).
I assume the command would be something like the following with "TD31" replaced with the appropriate name for the parameter. MODEL CONSTRAINT: NEW(TD31); TD31>0;
I tried f11 by CInspir2* CInspir3 CInspir1 (ineq); f11@1; Model Constraint: ineq<1;
This fixed the negative error variance problem, but caused all the loadings for f11 to be equal.
It would also be helpful to know how to extend this to other parameters (lambda, beta, psi, phi, etc).
I have read the Model Constraints section of the manual and search the discussion postings and web - sorry, I am still at a loss of how to do this. Thank you for your help.
Hello, how can I constrain factor loadings and error variances to specific values? I tried this syntax, but it gives me error messages "A parameter label or the constant 0 must appear on the left-hand side of a MODEL CONSTRAINT statement.' Here is my code: MODEL: CREAT BY creat1-creat9; INCIV BY inciv1-inciv7; COMMUN BY commun1-commun5; commun1-commun5 (resvar1- resvar5); Model Constraint: 0.958 = commun1; 0.999 = commun2; 0.581 = commun3; 0.950 = commun4; 0.974 = commun5; 0.410 = resvar1; 0.325 = resvar2; 1.120 = resvar3; 0.297 = resvar4; 0.289 = resvar5;
I am trying to estimate an interaction/mediation effect using the model constraint option. Is it possible to constrain the regression coefficients obtained from the on statement? Trying the following, I get the fatal error THIS PARAMETER RESTRICTION MODEL IS NOT AVAILABLE. I would be very happy if you could have a look on my code and perhaps tell me where the error lies. Thank you very much in advance.
VARIABLE: NAMES ARE x1-x21; USEVARIABLES ARE x1-x14 x19-x21; CATEGORICAL ARE x1-x3; MISSING =.;
CONSTRAINT ARE fto1 fto2;
DEFINE: ! Definition of dummy variables x15_1=0; IF (x15==2) THEN x15_1 = 1; x15_2=0; IF (x15==3) THEN x15_2 = 1; STANDARDIZE x4-x14 x19-x21;
MODEL: ! Specification of the measurement model F1 BY x1* x2@1 x3; F2 BY x5-x10; F3 BY x11* x12@1 x13 x14; F4 BY x19-x21;
! Specification of the structural model F4 on F1 (b1) F2 (b2) F3 (b3);
Thank you very much for your answer. I tried that. Actually, I want to estimate an interaction between a categorical variable with three categories and a latent construct. Thus, I think I have to specify two interaction terms using dummy variables: int_1 | F1 XWITH x_1; !x_1 representing x==2; int_2 | F1 XWITH x_2; !x_2 representing x==3; F4 on F1 F2 F3 x_1 x_2 int_1 int_2; The output of this model (parameter specification) indicates that the observed variables x_1 and x_2 are treated as latent variables because both variables have corresponding entries in the beta matrix and for both dimensions in the lambda matrix. Is this correct or just a notational problem? Thank you again very much for your support.
J.D. Smith posted on Monday, March 04, 2013 - 3:34 pm
I am trying to conduct a Wald test and I am interested in the significance of the change in R^2. I know you can specify a "new" parameter in the Model Constraint command and define it for comparison but I have been unable to find the correct syntax for the R^2 parameter? Any guidance would be appreciated. Thank you!
Dear Drs. Muthen, I have an APIM (dyadic data) with two indipendent variables and two mediators and I want to test, whether mediation differs between genders. I try to restrict the indirect effects to be equal, but I am not sure how to do this.
tmsm IND tconfm tanxim (p1); tmsm IND tconff tanxim; tmsm IND tconff tanxieF; tmsm IND tconfm tanxieF;
tmsF IND tconff tanxieF (p2); tmsF IND tconfm tanxieF; tmsF IND tconfm tanxim; tmsF IND tconff tanxim;
! Now, the indirect effect p1 should be set equal with p2, but I am not sure how to do this with the model contraint command.
MODEL CONSTRAINT: NEW (p1 p2); p1 = p2;
But if I let this model run, I get the following output: THE DEGREES OF FREEDOM FOR THIS MODEL ARE NEGATIVE. THE MODEL IS NOT IDENTIFIED. NO CHI-SQUARE TEST IS AVAILABLE. CHECK YOUR MODEL.
... and the degrees of freedom are -1 instead of 1. What would be the correct way to put the two indirect effects to be equal?
1) Is it possible to use MODEL CONSTRAINT: NEW to specify quantities that are functions of some model parameters and variables in the data? For example, the model has parameters a and b, the data include variables x1 and x2, and I want to compute: c = mean(a*x1 + b*x2).
2) If the answer to (1) is yes, is it possible to use vector/matrix multiplication notation in specifying new quantities in MODEL CONSTRAINT? Some of the functions I am dealing with are a bit messy in regular algebraic notation.
Thank you. Sorry, it was just a toy example. I need to average over the sample a complex function that cannot be reduced to simple sufficient statistics but requires the actual data.
I am curious, in what cases can a variable listed in VARIABLE: CONSTRAINT be used in the right hand side of a statement in MODEL CONSTRAINT to define a new parameter? I am thinking to reduce information in a function of a data vector to a scalar we would need some function in the group of mean, sum, median, quantile, etc.
The Mplus Users Guide mentions that variables listed in VARIABLE: CONSTRAINT can be used on the right hand side of statements in MODEL CONSTRAINT but none of the examples given are about this.
Hi, I have another question. If I want a lot more precision than 3 decimal places, would it be OK to use MODEL CONSTRAINT to specify a new parameter that is 1000 times a model parameter? I know I could save results to get more decimal places, but would this trick work too?