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mplus code and output.doc [6] | 117.5 KB |

model9.csv [7] | 69.15 KB |

thirtyfixationswodummy.csv [8] | 15.52 KB |

thirtyfixationswithdummy.csv [9] | 19.33 KB |

thirtyfixationswithdummy.R [10] | 10.26 KB |

thirtyfixationswodummy.R [11] | 9.96 KB |

Hi,

The data set i use has 214 individuals for which I have different number of observations - varies between 21 and 30.

I try to estimate a model of nonlinear growth - I specify this using constraints on the factor loadings. When i estimate this model in Mplus I use dummy variables that load on the observed for the missing data. There are 9 Dummy variables that are =1 if I observe data for that individual at that time point, =0 otherwise. For example, if D22=1 for ID1, then individual 1 has observed data at time point 22. The Mplus model is estimated normally, it looks ok. However, when i do the same in OpenMx, it doesn't work.

In OpenMx I have tried two approaches:

1. use the same model as in Mplus with dummies that load on the observed - Path20

2. use the same model as in Mplus but exclude the dummies. All other Paths and constraints are kept. I would expect this approach to work, since i use raw data and therefore FIML.

Neither of these two is successful.

I have the following errors/warnings for the two approaches:

1. Error: The job for model 'Mplus copy' exited abnormally with the error message: MxComputeGradientDescent: fitfunction Mplus copy.fitfunction is not finite (Expected covariance matrix for continuous variables is not positive-definite in data row 164)

In addition: Warning message:

In model 'Mplus copy' Optimizer returned a non-zero status code 10. Starting values are not feasible. Consider mxTryHard()

i have used mxTryHard, but then I get the following error:

Error : The job for model 'Mplus copy' exited abnormally with the error message: MxComputeGradientDescent: fitfunction Mplus copy.fitfunction is not finite (Expected covariance matrix for continuous variables is not positive-definite in data row 164)

Error in omxSetParameters(model, labels = names(params), values = params * :

'labels' argument must not contain duplicate values

- Warning message:

In model 'Mplus copy' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)

The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)

Here are some questions I hope you can help me with:

1. In the raw data set I have NA for missing values. Is there anything else i need to do such that the model is estimated with FIML?

2. How come the model that exactly replicated the Mplus code is not giving the same output?

3. What is the error on "'labels' argument must not contain duplicate values" about? I don't see what is wrong with the labels I use.

Note: I have also estimated the same model on the first nine time points, so I have no missing data. All is ok and I get the same results in Mplus and OpenMx. Adding missing data messes it up.

Thank you,

Ana