Hi John Thanks a lot for the reply. Could you suggest how I can correct this problem? I tried using a correlation matrix instead of raw moments, but still got the same error. I also fixed parameters v1,v2,v3,a1 at 1; then it gave me the error that the system is exatly singular. To answer the points that you raised: 1. x1-x6 are not causes; they are just indicatiors. Does that change my model? 2. Is the size of the dataset going to be a real limitation? How can I take care of that? Would appreciate your reply, Thanks Sunil ----------------------------------- Dear Sunil, There are 7*8/2 = 28 raw moments among the 7 observed variables. Of these, 6*7/2 = 21 are used for the moments among the 6 fixed-exogenous variables, leaving 28 - 21 = 7 df. You model has 11 free parameters. So df for the model = 11 - 7 = -4. Some additional comments: If you're using raw moments, why isn't there a constant variable in the model? Do you really intend x1 -- x6 to be causes, rather than indicators, of m1 and m2? Why are there no normalizing constraints on the latent variables? Do you really want to fit a model like this to so small a data set? I hope this helps, John John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Sunil W
Sent: Wednesday, November 30, 2005 10:08 PM
To: r-help at stat.math.ethz.ch
Subject: [R] Error in structural equation model - "The model
has negativedegrees of freedom"
Hi
I am running a structural equation model with R using the sem
command; am getting the following error:
"Error in sem.default : The model has negative degrees of
freedom = -4"
My model is as follows:
s_model = specify.model()
x1->m1, b1,NA
x2->m1, b2,NA
x3->m2, b3,NA
x4->m2, b4,NA
x5->m2, b5,NA
x6->m2, b6,NA
m1->y, a1,NA
m2->y, a2,NA
m1<->m1, v1,NA
m2<->m2, v2,NA
y<->y, v3,NA
x1-x6 are observed independent variables, m1 and m2 are the
latent variables and y is the observed dependent variable. I
use the raw.moments command for calculating the covariance
matrix, based on a data with 147 observations.
The command that I use is as follows:
s = sem(s_model,S=R,obs.variables=colnames(R),
fixed.x=c('x1','x2','x3','x4','x5','x6'), raw=TRUE)
I would appreciate any help on this; I am new to structural
equation models
and realize that I may be making a silly error.
Thanks
Sunil
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