Hi, I’m 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
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.
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!
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.