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There are a number of changes between Mx and OpenMx, mostly easy to learn.

You will still need to read in data, propose algebra, possibly create more than one group, estimate the model, and post-process and display the results. Much of the structure of an mx script translates easily into

R has much powerful access to data, including reading a range of formats, not barfing on names, accessing data over the web etc. You will need to replace. ....

Mx used groups to structure its analyses. The comparable structure in OpenMx is the model. Because R provides a lot of functionality, the mapping is not complete. This is clearest in mx groups used for standardising results rather than defining a SEM model. These will often be replaced entirely with manipulation of the data in regular R code. In multi-group models, mx groups will often be mapped directly into OpenMx (sub-)models. Thus a two-group twin design may well be implemented with two models in OpenMx, contained in a parent model. mx scripts often begin with a "Define" group specifying a set of matrices, and this can usually be translated directly to an OpenMx model.

OpenMx uses R-style operators, which entails some very straightforward find-and-replace type changes. For instance, tow very common functions: matrix multiplication and transposition, signified by "*" and "~" in mx, need to be changed to R functionss, namely %*% for matrix multiplication and t(x) for transposition of matrix x. A full list of function mappings is provided here operators-and-functions.

The other major change in OpenMx algebra is the addressing of symbols. For instance, in mx 1 to include a matrix from another group in an algebra, you would import the matrix from that group, then refer to it by its name. In OpenMx, you would refer to an object in another model.

So if in mx you said

**Begin Matrices** = **Group** 1

**Means** M ;

In OpenMx, you would say "**mxFIMLObjective**("expCov", "name.M"), where "name" is the name of what was group 1.

**save** and **get**

the result of an MxRun is itslef a model, populated with the final estimated values of the matrices and algebras you proposed.

Confidence intervals.

Categorical data

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## Comments

## Great insight

Thanks, great insight.

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