Standard errors of zero using R3STEP PreviousNext
Mplus Discussion > Latent Variable Mixture Modeling >
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 Michelle Colder Carras posted on Thursday, August 14, 2014 - 5:53 am
Hi,
Im doing LCA with R3STEP using SUBPOPULATION with some missing data and finding that many standard errors are 0 in the latent class regression. The regression drops a total of 58 observations and uses 651. This problem occurs when using 8 covariates (2 binary, 2 categorical, 4 standardized continuous) and 6 categorical latent class indicators.

All of the categorical covariates have standard errors of 0 in each of the 3 multinomial regressions no matter which class is used as the reference class. For the continuous covariates, this happens only sometimes. For example, when using class 4 as the reference class I get:
C#1 ON

DEPMST1 0.287 0.000 999.000 0.000
LONMST1 0.339 0.181 1.872 0.061

C#2 ON

DEPMST1 -0.460 0.137 -3.361 0.001
LONMST1 0.416 0.000 999.000 0.000

When I attempt to add an additional 3 continuous covariates, an additional 6 observations are dropped and even more SEs become 0. E.g., when using class 4 as the reference class, the SEs for the coefficients for the regressions of both DEPMST1 and LONMST1 are now 0 in the C#1 regression, but the SE is still calculated for DEPMST1 in the regression of C#2.

Thank you for any insight/help you might have.

-Michelle Carras
 Linda K. Muthen posted on Thursday, August 14, 2014 - 9:04 am
It looks like that parameters are being fixed not that the standard errors are zero. For further information, send the output and your license number to support@statmodel.com.
 Sabrina Twilhaar posted on Monday, March 09, 2020 - 4:18 am
Hello,
Based on the literature indicating a non-linear relation between X and Y, I would like to include a quadratic term in my LPA model that I'm analyzing using R3STEP (automatic). X is measured in full weeks (range: 24-31). When I start with a univariate model only including X everything goes fine. When I include X + X^2, the SE of the intercepts become zero (and Est./SE ***** and 999). So something goes wrong here. Similarly, when I include X and X^2 in my full model (9 predictors in total, n=1977), SEs and p-values of all predictors become very small. This is not true when X^2 is not included in the model.

I use R3STEP with type=imputation in the data command. I excluded X and X^2 from the 'use variables' list and included them as auxiliary variables during imputation. There are no missing values in X and X^2 and all correlations between predictors are <0.4.

I hope someone has an idea about what I'm doing wrong. Thanks a lot in advance!
 Sabrina Twilhaar posted on Monday, March 09, 2020 - 7:04 am
I might found the solution, but I am not sure.

I think it might have to do with the fact that I used raw instead of orthogonal polynomials. Using orthogonal polynomials at least solved the problems I described above.

Do you think this makes sense?
 Bengt O. Muthen posted on Monday, March 09, 2020 - 3:51 pm
Try using subtracting the mean Xbar in creating X and X^2:

X - Xbar

(X-Xbar)*(X -Xbar)

Also, even if X and Y have a nonlinear relationship, this doesn't mean that X and C (the latent class variable) has such a relationship.
 Sabrina Twilhaar posted on Tuesday, March 10, 2020 - 4:13 am
Thank you very much. This seems a more straightforward option and it gives me basically the same results (in terms of SE and p-values) as with the orthogonal polynomials. As you correctly noted, different from the relation between X and Y, the relation between X and C is best described using the linear term.
 Bengt O. Muthen posted on Tuesday, March 10, 2020 - 10:23 am
Good.
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