Dear Lisa,
There doesn't seem to be anything logically wrong with your model.
I don't have much time today to look into it, but trying different
optimizers in version 2.0-0 of sem, using the correlation matrix in place of the
covariance matrix, and setting the par.size parameter, I was unable to obtain an
admissible solution. I also was unable using factanal() to fit an exploratory
factor analysis for five factors to your data. I expect that the problem is
ill-conditioned.
Best,
John
------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
On Tue, 8 Nov 2011 08:18:28 -0800 (PST)
lisamp85 <lisamlpham at gmail.com> wrote:> Hello.
>
> I started using the sem package in R and after a lot of searching and
trying
> things I am still having difficulty. I get the following error message
when
> I use the sem() function:
>
> Warning message:
> In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names >
vars, :
> Could not compute QR decomposition of Hessian.
> Optimization probably did not converge.
>
> I started with a simple example using the specify.model() function, but it
> is really straight forward. I uploaded my specify.model script and my data
> covariance matrix here too so I wouldn't clutter this email with the
entire
> model (20 observed variables, 5 factors). Could this error message be from
> the data itself and not from my path model?
>
> I have my observed variables X and my unobserved variables F. I have ONLY
> exogenous latent variables (i.e. they never appear on the right side of the
> single head arrow ->). I include all possible factor covariances FjFk,
and
> the only constraints I've made was to restrict the Factor variances to
1.
> My model follows in this basic format (as you can see from my uploaded
> file):
>
> # Factors (where I specify which observed variables load on to which
> factors)
> # I have only exogenous latent variables
> F.i -> X.j, lamj.i, NA
> .
> .
> .
> # Observed variable variances
> X.j <-> X.j, ej, NA
> .
> .
> .
> # Factor variances (I fixed all factor variances to 1)
> F.i <-> F.i, NA, 1
> .
> .
> .
> # Factor covariances (I represent all factor covariances, i.e. the upper or
> lower triangle of a covariance matrix)
> F.i <-> F.k, FiFk, NA
> .
> .
> .
>
> Did I do something wrong here?
> Here are my uploaded files:
> CFA script: http://r.789695.n4.nabble.com/file/n4016569/CFA_script.txt
> CFA_script.txt
> Covariance matrix:
> http://r.789695.n4.nabble.com/file/n4016569/covariance_matrix.RData
> covariance_matrix.RData
>
>
> Thank you so much for any and all of your help.
> Lisa
>
> --
> View this message in context:
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>
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