Mon, 02/11/2013 - 15:30

Hi,

I'm trying to reproduce an analysis of a congeneric two- factor- model from a textbook and I can't figure out where I am going wrong. The model has two latent factors (selfevaluation and foreignevaluation scales) with three indicators each. The output I get looks like this:

> summary(congeneric)

free parameters:

[1] lbound ubound

<0 Zeilen> (oder row.names mit Länge 0)

observed statistics: 0

estimated parameters: 0

degrees of freedom: 0

-2 log likelihood: NA

saturated -2 log likelihood: NA

number of observations: 0

chi-square: NA

p: NA

Information Criteria:

df Penalty Parameters Penalty Sample-Size Adjusted

AIC: NA NA NA

BIC: NA NA NA

CFI: NA

TLI: NA

RMSEA: NA

timestamp: NULL

frontend time: NULL

backend time: NULL

independent submodels time: NULL

wall clock time: NULL

cpu time: NULL

openmx version number: NULL

I have tried all sorts of modifications to my code, but nothing helped. It seems like I must be overlooking something terribly important. I would really appreciate some help! I attached my code and the data.

Jens

Attachment | Size |
---|---|

TwoFactorModel.r | 1.42 KB |

TwoFactor_Data.dat | 3.66 KB |

You'll laugh when you see this. Try

`summary(congeneric.fit)`

You had been trying to get the summary of the unfitted model. :)

`summary(congeneric)`

For this reason I always do

i.e., don't change the model name just because it's fitted. This makes for far fewer errors, and captures a strength of openMx, which is that a "model is a fit is a model"

I also don't tend to use the

`greatLongInformativeNameForAModel`

paradigm.When you're in a workflow, I just use a short generic model name like "m1":

otherwise you end up with long statements, and very easily confused words like

Then the informative name is the internal one, which shows up in

`mxCompare`

.Your milage may of course vary, I am told.

Uuuh... that's embarassing. Well, thank you for your help!

I've done it dozens of times, as has everyone else on the development team. Welcome, you're one of us!

That makes it a bit better, that it happened to you, too:-) Thanks for the warm welcome and for this forum!