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HELP WITH SEM LIBRARY AND WITH THE MODEL'S SPECIFICATION
2 messages · Analisi Dati, John Fox
Dear Costantino,
-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On
Behalf Of Analisi Dati Sent: March-30-09 11:13 AM To: r-help at r-project.org Subject: [R] HELP WITH SEM LIBRARY AND WITH THE MODEL'S SPECIFICATION Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the
values
of loadings are big or not. (In this code i used more than 3 factors)
. . . (many lines elided)
Now the problems, and my questions, are various: 1)In "mydata" i need to have only the questions or also my latent
variables?
In other words, i suppose that the mean of Q1,Q2,Q3 give me a variable called "OCB". In mydata i need also this mean???
No. sem() recognizes as latent variables (F1, F2, etc.) those variables that do not appear in the observed-variable covariance matrix. There are several examples in ?sem that illustrate this point. Moreover, the latent variables are not in general simply means of observed variables.
2)In the specification of my model, i didn't use nothing like "F1<-
F2......", is this a problem? this sentence what indicates??? that i have
a
mediation/moderation effect between variables???
By not specifying F1 <-> F2, you imply that the factors F1 and F2 are uncorrelated. This isn't illogical, but it produces a very restrictive model. Conversely, specifying F1 <-> F2 causes the covariance of F1 and F2 to be estimated; because you set the variances of the factors to 1, this covariance would be the factor correlation.
3)Now, if you look my code,you could see that i don't put in "mydata" the mean value called "OCB" (see point 1), and i don't write nothing about the relation between F1 and F2, and when i run the sem function i receive
these
warnings: 1: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : S is numerically singular: expect problems 2: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, :
That seems to me a reasonably informative error message: The observed-variable covariance matrix is singular. This could happen, e.g., if two observed variables are perfectly correlated, if an observed variable had 0 variance, or if there were more observed variables than observations.
S is not positive-definite: expect problems 3: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, :
That S is singular implies that it is not positive-definite, but because a non-singular matrix need not be positive-definite, sem() checks for both.
Could not compute QR decomposition of Hessian. Optimization probably did not converge. and after the summary i receive this error: coefficient covariances cannot be computed
These are the problems that sem() told you to expect.
What i can do for all this????
Without more information, it's not possible to know. You should figure out why the observed-variable covariance matrix is singular. I hope this helps, John
Hoping in your interest about this problem, i wish you the best. Costantino Milanese, a young researcher full of problems! [[alternative HTML version deleted]]
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