Longitudinal IRT - non-positive definite matrix

[Claire Chen]

Dear Professors,

I am a novice mirt user and am trying to obtain trait scores for a repeated measure (5 items, 5-point likert scale) across 3 time points. The ultimate goal is to use the trait scores as indicators of further latent growth modeling.

Using the R code adapted from  https://philchalmers.github.io/mirt/html/Longitudinal-IRT.html ),  I got the warning message as below:

Iteration: 1, Log-Lik: -23702.913, Max-Change: 1.01782

Iteration: 2, Log-Lik: -23833.759, Max-Change: 0.29002

Iteration: 3, Log-Lik: -23683.620, Max-Change: 0.19118

Iteration: 4, Log-Lik: -23611.674, Max-Change: 0.13221

Iteration: 5, Log-Lik: -23574.670, Max-Change: 0.09855

Iteration: 6, Log-Lik: -23564.176, Max-Change: 0.08492

Iteration: 7, Log-Lik: -23584.722, Max-Change: 0.79980Warning messages:

1: Latent trait variance-covariance matrix became non-positive definite. 

2: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

3: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

4: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

5: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

6: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

7: In log(eigen(sigma, symmetric = TRUE, only.values = TRUE)$values) :

  NaNs produced

My concern was that there is an item (s2) not having the same number of response categories as the other time points. I still got the same warning after I removed this item or collapsed the response category. Could you please take a look at the code and raw data (not collapsed) uploaded in the link below and kindly let me know how to move forward?

https://drive.google.com/drive/folders/1xK_M0ePC1dl3Qbmo_rV8ay55z9TFPaKT?usp=sharing

Another question is about another set of repeated measure (4 items, 3 time points) - I was able to the run the same code without getting any warning; however, some of the covariances were >1. Could I trust the derived scores in this case?

Any suggestions would be much appreciated. Thank you!

Claire

[Phil Chalmers]

Hi Claire,

It looks like your model was slightly too unstable to use such a low quadrature point number for numerical integration, which was causing the vcov matrix between the latent traits to take unrealistic values during the successive EM iterations. However, I bumped it up to quadpts = 9, which took notably longer, but the model did end up converging after 81 EM cycles. HTH. 

Phil

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