OpenMx Structural Equation Modeling
Mixed effects model - matrix specification
Hello,
I am looking for advice with regard to the implementation of mixed effects model in OpenMx.
I am starting with a simple model where one dependent variable Y is predicted by four correlated variables X1-X4. The participants in the sample are clustered in pairs (they are twins).
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Problems with Constrain expected Means and Variances to be equal across Twin Order
Hi all,
I am trying to fit several bivariate [between my exposure at age 16 and my outcomes at age 21 (accounting for age and sex)] and trivariate [my exposure at age 16 and my outcomes at ages 21 and 26 (also accounting for age and sex)] ACE models. However, I’ve started by fitting univariate saturated and submodels, followed by ACE and submodels, to eventually fit my bivariate models. The saturated models run fine across all, but when I try to fit the constrained models (by twin order and zygosity), I get the following error message:
outdated-solved
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NA CIs for Bivariate Model
Here is the script:
### SELECT VARIABLES HERE
data_file <- "Cerebellum_volumes.csv"
variable_input1 <- "lsas_total"
variable_input2 <- "Vermis_VII"
cov1 <- "Total_Cerebel_Vol"
cov2 <- "age"
### Import data
dat <- read.csv(file=data_file, header=TRUE, sep = ";")
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checking total h2 at time 2 in a longitudinal choelsky model with paths script
I am conducting a longitudinal Cholesky model with one variable in two time points.
I use the script written below with paths.
How can I modify this script to find for the total h2 at time 2? that is, I want to find the heritability explained by both the genetic effect from time 1 and the genetic effect from time 2, and the confidence interval of this estimate.
Thank you very much for your help
goodness-of-fit indices for ML SEM
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Best Practices for 3lvl measurement SEM
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Problems with non-convex Hessian with multilevel SEM
While I'm looking for general guidelines to deal with non-cenvex Hessians and status code 5-issues in that thread, it's probably good to look at my models to find issues here.
General strategies to deal with non-convex Hessian matrix
I am working on a org psych paper that is currently under review. **The model** is a quite complex *multilevel mediation SEM*, featuring *four latent variables* (only one is exogenous/independent, the others are endogeneous mediators/outcomes). I also include the *measurement models* for each variable (i.e., the respective items as manifest variables). Until now I have done the estimations in a 2-level model using lavaan. For the review I am trying to add a third level to control for further nestedness.
Understanding runHelper() error: MxExpectationRAM: latent exogenous variables are not supported
Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
MxExpectationRAM: latent exogenous variables are not supported (x -> sw)
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