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?