Dear Costantino, > -----Original Message----- > From: [email protected] [mailto:[email protected]] On > Behalf Of Analisi Dati > Sent: March-30-09 11:13 AM > To: [email protected] > 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]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
