Hi chaps,
I was incorporating mxConstraint() into a model today and getting the following error

mxRun(model)
Running common_path_constraint_ACE 
Error in mxRun(model) : Non-conformable matrices in Matrix Subtract.

The actual problem is that I was equating a 2*2 Identity to a 1*1 Full, so the error is misleading...

Would be nice if the error contained the matrix names (i.e., L and nFac_IMatrix) and mxConstraint rather than subtraction

Here's a fragment to show the usage...
share = mxModel("all",

Currently this doesn't work:

fit= mxRun(model); mxRun(fit)

  Running twinACE
  Running twinACE
  Error: The expected means matrix associated with the FIML objective function in model    
     'twinACE' does not contain dimnames.

I guess the dimnames are being stripped off in the result making the resulting fit not a runnable model.

Hi Tim,

The transition from 'fixed' to 'closed' isn't happening after 2 weeks on the ticket system.

starting in 821 (or perhaps 820), running a model causes a series of empty arrays to be printed early on in the execution...

> fit = mxRun(model)
Running ACE
[[1]]
[1] NA

[[2]]
[1] NA

[[3]]
[1] NA

[[4]]
[1] NA

[[5]]
[1] NA

[[6]]
[1] NA

[[1]]
[1] NA

[[2]]
[1] NA

[[3]]
[1] NA

[[4]]
[1] NA

[[5]]
[1] NA

[[6]]
[1] NA

One of our beta testers has Ubuntu linux with gcc 4.3 on an x86_64 machine. Can we get a compile of npsol for that architecture?

When installing on a linux platform, change the installation script to point to OpenMx_0.1.3-776.tgz instead of OpenMx_0.1.3-776.tar.gz, or rename the files in the repository to OpenMx_0.1.3-776.tar.gz.

Per Mx manual, 6th edition:
http://ibgwww.colorado.edu/workshop2008/cdrom/Software/Mx/doc/mxmanual…

Table 4.1 - matrix types and their free elements
Table 4.3 - Examples of use of the Matrices command to specify the dimensions of different matrix types
Table 4.5 - matrix functions

Documentation helper gives an error
Running AlgebraExample
Error in mxRun(modelD) :
MxAlgebraObjective's objective algebra does not evaluate to a 1x1 matrix.

I'd suggest this:

A <- mxMatrix("Full", nrow = 3, ncol = 3, values=2, name = "A")

# Simple example: algebra B simply evaluates to the matrix A
B <- mxAlgebra(A, name = "B")

# Compute A + B
C <- mxAlgebra(A + B, name = "C")

# Compute sin(C)
D <- mxAlgebra(sin(C), name = "D")

# Make a model and evaluate the mxAlgebra object 'D'
A <- mxMatrix("Full", nrow = 3, ncol = 3, values=2, name = "A")

ML on raw data should convert to FIML in the back-end.