Longitudinal SEM and Latent Growth Curves

Hi everyone. I'm trying to fit a bi-linear spline model with an estimated knot point (y_nt = g_0n + g_1n*(min(0,time-lambda)) + g_2n*(max(0,time-lambda). I've pasted my script below, which runs (with warnings), but provides estimates (below as well) that are not reasonable given the min and max functions and the estimate of the knot point (e.g., min of (0 and 1-9.14) is -3.44). Can the min and max functions be used in this manner?

################# Spline Growth Model with Estimated Knot Point #################
spl.hght.omx <- mxModel("Spline Growth Model, Path Specification",

Dear All,

I'm undertaking a 4th year project in final year in university. I run my longitudinal data and it seems final. My supervisor has now asked me run randomly generated data but i keep getting the same error: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). Can anyone help please?

Hi,

I'm trying to run a random intercept, random slope model from the book Latent Growth Curve Modeling by Preacher, Wichman, MacCallum, & Briggs (2008, p.31). All the coefficients, degrees of freedom, chi-square, look the same as the book, but not for RMSEA. The reported RMSEA from the book is 0.07, which I also replicated in LISREL. However, the value in OpenMx is 0.1453412. Does anyone know how that is calculated?

I attached the R script. Thanks in advance.

Mark

degrees of freedom: 14
-2 log likelihood: 11832.83
saturated -2 log likelihood: 11756.92

I cannot find a clear example of a path diagram for a latent class regression where the manifest variables are longitudinal (at varying time points for each subject). The slope and intercept for time for each response are different for each latent class. The closest thing seems to be a longitudinal latent growth curve models but they seem to require the same time points for all subjects. I would greatly appreciate information on this if only where to look.

Hello everyone!
I have prepared a linear growth model to estimate genetic and environmental influences (ACE) in the intake and the slope with ordinal twin data: 3 variables, 7 categories in each of them. The sample is is divided in 6 groups (and so is the script), according to zygosity and sex: MZ men, DZ men, MZ women, DZ women, DZ man-woman and DZ woman-man.
I have been reading from: Mehta PD, Neale MC, Flay BR. Squeezing interval change from ordinal panel

Hi,

I ran a piecewise latent growth model with 9 timepoints and the knot at the 6th timepoint. The model converged normally but the chi-square, p and rmsea were all NA. Is there any reason this should be the case or am i missing something?

Regards,
Yonghao

In your latent growth curve model example (http://openmx.psyc.virginia.edu/docs/OpenMx/latest/TimeSeries_Path.html), you suggest:
-freely estimating the means and variances of the latent intercept and slope factors
-constraining the means of the manifest variables to be zero (to make their means dependent on the intercept and slope means)
-constraining the residual variances of the manifest variables to be equal over time

Hi,

I have been trying to specify the parameters and constraints for a cross-lagged model, though I'm not sure how to identify it correctly (sorry if I use incorrect terminology). Some of the values are similar to what I would expect, though I'm thinking about how the data should behave given free or fixed parameters. I won't say anymore, rather I'll past the model below.

Any suggestions about how to set the parameters to get the most accurate estimate of what is going on in the data would be greatly appreciated. On another note when I post a message I receive this error message

I thought this might be useful for those new to OpenMx (like myself) who are trying out the matrix specifications way of specifying latent growth curves. I ran into a bug when declaring a lgc model where the input method was 'cov' rather than 'raw', and since I couldn't figure out how to fix it directly I composed a step around way to do it. I believe that this issue is well known since the links from the current OpenMx Users Manual for

http://openmx.psyc.virginia.edu/repoview/1/trunk/demo/LatentGrowthCurveModel_MatrixCov.R

I am working with the script I received at the workshop in Boulder earlier this year. I have altered the original script slightly to fit my data, but I am getting the following error messages in this section of my script:

#MZ AND DZ EXPECTED MATRICES
>
> # Algebra for expected Int & Slope means + sex effects (betas) for MZs
> mxModel("MZ",
+ mxData(data.frame(mzData,mzDefs), type="raw",) # Requests MZ definition variables: GENDER_1 & GENDER_2
+ mxMatrix( type="Full", nrow=1, ncol=1, free=FALSE, labels=c("data.GENDER_1"), name="GENDER_1"), # Selects GENDER_1 from mzDefs