I'm running OpenMx 1.0.2 on Ubuntu 10.04. OpenMx shows the degrees of freedom differently for using raw data than for using the covariance matrix (and means vector). The model has one factor with 6 indicators and 9 parameters are being estimated (some parameters are constrained to equal other parameters).

Here is the output for the raw data:

observed statistics: 120

estimated parameters: 9

degrees of freedom: 111

-2 log likelihood: 894.0595

saturated -2 log likelihood: NA

number of observations: 20

chi-square: NA

p: NA

AIC (Mx): 672.0595

BIC (Mx): 280.7666

adjusted BIC:

RMSEA: NA

timestamp: 2010-11-09 16:20:59

frontend time: 0.3233688 secs

backend time: 0.03640819 secs

independent submodels time: 8.201599e-05 secs

wall clock time: 0.359859 secs

cpu time: 0.359859 secs

openmx version number: 1.0.2-1497

Note that the chi-square and RMSEA are not estimated.

If instead I input the covariance matrix and means vector:

observed statistics: 27

estimated parameters: 9

degrees of freedom: 18

-2 log likelihood: 645.686

saturated -2 log likelihood: 574.1632

number of observations: 20

chi-square: 71.52271

p: 2.492757e-08

AIC (Mx): 35.52271

BIC (Mx): 8.799766

adjusted BIC:

RMSEA: 0.3855829

timestamp: 2010-11-09 16:23:32

frontend time: 0.1366770 secs

backend time: 0.01501584 secs

independent submodels time: 8.106232e-05 secs

wall clock time: 0.1517739 secs

cpu time: 0.1517739 secs

openmx version number: 1.0.2-1497

The parameter estimates are not exactly the same between the two runs but similar (not sure why, only difference is the input format) but the degrees of freedom are different (what I would expect when I input the covariance matrix) and the chi-square and RMSEA are computed.

What am I missing?