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 Jean Frisou posted on Sunday, August 06, 2006 - 6:34 am
I have some problem with a model constraint. The syntax is the same as in the example 5.20. The ouput file incates an error

*** ERROR
Unknown parameter label in MODEL CONSTRAINT: LA11

The part of program file is this one:

DATA: FILE IS C: Etude RL/Mplus/modèle/
artram/datamod.dat;

VARIABLE: NAMES ARE x11-x16 x21-x26 y21-y24;
USEVARIABLES ARE x11 x12 x15
x21 x22 x25;

ANALYSIS: ESTIMATOR = MLMV;
DIFFTEST = etatrait.dat;

MODEL:
ENGA1 BY x11@1(la11);
...........

MODEL CONSTRAINT:

NEW(rel11 rel12 rel15 rel21 rel22 rel25 );


rel11 = la11**2*vf1/(la11**2*vf1 + ve11);
rel12 = la12**2*vf1/(la12**2*vf1 + ve12);

rel11 = rel21;rel12 = rel22;
OUTPUT:
TECH4;
SAMPSTAT;
RESIDUAL;
STANDARDIZED;

Can somebody indicates me where is the error ???

Thank you for the help
 Linda K. Muthen posted on Sunday, August 06, 2006 - 9:10 am
I can't see from what you have posted unless it is that you are labelling a fixed parameter. Please send your input, data, output, and license number to support@statmodel.com.
 Jill McClain posted on Thursday, September 10, 2009 - 5:20 pm
Hi Drs. Muthen. I am trying to use the model constraint command to generate predicted values for a dependent variable for set values of my independent variables (from a linear regression). I can do this by hand, of course, but I'm hoping that using labeled parameters in Mplus will properly propagate the errors so that my predicted values will have appropriate confidence intervals (please let me know if this is not the case). However, I cannot figure out how to label or otherwise include the intercept (other than simply entering the intercept value from the output, in which case the error won't be accounted for). Is it possible to label the intercept in the model command or otherwise indicate that I want to use the estimated intercept and its standard error in the model constraint calculation? Thanks.

Jill
 Bengt O. Muthen posted on Thursday, September 10, 2009 - 5:44 pm
If you have

y ON x (b);

the intercept is simply referred to as

[y] (a);

so that you can write

Model Constraint:
New(yhatxi);

yhatxi = a + b*xi;

where yhatxi gets a point estimate and a SE that takes into account the sampling error in a and b.
 Jill McClain posted on Friday, September 11, 2009 - 8:58 am
Thanks very much. That worked perfectly. Oddly, though, the SE for yhatxi is substantially smaller when I use the labeled parameter "a" than when I simply insert the numerical value of a (which has no error as far as Mplus knows) into my constraint equation. Is that correct?
 Bengt O. Muthen posted on Friday, September 11, 2009 - 9:42 am
Check Tech3 - maybe the a and b estimates are negatively correlated.
 Jill McClain posted on Friday, September 11, 2009 - 3:20 pm
Hm, yes. There are actually 18 betas in the equation (this is a very large cohort study), and 17 are negatively correlated with the intercept. Is this a problem? Thanks for any insight you can offer!
 Bengt O. Muthen posted on Friday, September 11, 2009 - 3:41 pm
That's not a problem, but it explains the reduction in SE that you reported.
 Jill McClain posted on Friday, September 11, 2009 - 5:21 pm
Excellent. Thanks very much, as always!
 Rik Pieters posted on Tuesday, May 22, 2012 - 2:55 pm
Dear Drs. Muthen

Is it possible to label the threshold in a probit analysis, in the same way as you indicate in the response to the question by Jill McClain?

y ON x (b);
the intercept is simply referred to as
[y] (a);
so that you can write
Model Constraint:
New(yhatxi);
yhatxi = a + b*xi;

My attempt produced an error message.
 Bengt O. Muthen posted on Tuesday, May 22, 2012 - 5:21 pm
Yes, but probit gives a threshold tau,

[y$1] (tau);

instead of an intercept, and the probit replaces y,

probit = -tau + b*x;

You can also translate the probit into a probability using the Phi function.
 anonymous Z posted on Thursday, July 28, 2016 - 10:13 am
Dear Drs. Muthen,

I am using model constraint to create indirect effects, and meanwhile generate bias corrected bootstrap confidence intervals for indirect effects.

The output gives only 95%CI for unstandardized results

Lower 2.5% Estimate Upper 2.5%
New/Additional Parameters
O1 -0.333 -0.175 -0.044

O2 -0.395 -0.175 -0.054

Given the intervals excluding zero, can I just report that the indirect effects were significant or should I create standardized results?

Thanks so much!
Jing
 Bengt O. Muthen posted on Thursday, July 28, 2016 - 12:08 pm
Bootstrapped CIs are unlikely to disagree for raw and standardized indirect effects. I typically decide on significance based on the raw and if I want to describe the effect size I compare it to the SDs of the X and Y (that is, I consider the standardized effect) - but without further discussing significance for the standardized.
 anonymous Z posted on Friday, July 29, 2016 - 6:38 am
Dr. Muthen, thanks so much!
 Timothy Ihongbe posted on Monday, November 21, 2016 - 7:39 am
Dear Drs. Muthen,

I'm running an SEM model to simultaneously test for the effect of 2 mediators.

I'm having problems with the model constraint.

*** ERROR in MODEL CONSTRAINT command
The following parameter label is ambiguous. Check that the corresponding
parameter has not been changed. Parameter label: A1

Here's my syntax:

DATA:
FILE IS "Downloads\tdvv.csv";
FORMAT IS free;

VARIABLE:
NAMES ARE WT STR CLU RCE AGE SEX EDU SC DV SUB RSK;
.............
CATEGORICAL ARE DV;
CLUSTER is CLU;
STRATIFICATION IS STR;
WEIGHT IS WT;

ANALYSIS:
TYPE IS COMPLEX;
PARAMETERIZATION=THETA;
ESTIMATOR IS WLSMV;
iteration = 1000;

MODEL:
DV ON SC SUB RSK SEX (a1-a4);
SUB ON SC RSK (b1-b2);
RSK ON SC SUB RCE AGE EDU (c1-c5);
DV ON SUB SC RSK SEX (d1-d4);
DV ON RSK SC SUB SEX (e1-e4);

MODEL CONSTRAINT:
new (dir indir1 indir2 indir_tot tot );

dir=a1;
indir1 = b1*d1;
indir2=c1*e1;
indir_tot=( b1*d1)+( c1*e1);
tot = a1 + ( b1*d1)+( c1*e1);

OUTPUT:
standardized;

Thank you.

Timothy
 Linda K. Muthen posted on Monday, November 21, 2016 - 3:47 pm
You have labelled some parameters twice, for exmaple, DV on SSC SUB RSK SEX. Label them only once. Actually you have DV three times with the same covariates.
 Timothy Ihongbe posted on Tuesday, November 22, 2016 - 8:06 am
Thank you, Dr. Muthen. I made the corrections and it ran smoothly.

Timothy
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