From: John Fox <jfox_at_mcmaster.ca>

Date: Mon, 28 Mar 2011 15:41:22 -0400

John Fox

Sen. William McMaster Prof. of Social Statistics Department of Sociology

McMaster University

Hamilton, Ontario, Canada

http://socserv.mcmaster.ca/jfox/

R-help_at_r-project.org 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. Received on Mon 28 Mar 2011 - 19:43:40 GMT

Date: Mon, 28 Mar 2011 15:41:22 -0400

Dear jouba,

I think you're using the sem() function in the sem package.

I'm not sure that I understand your question, but I think it is why you need to specify the variance of the exogenous variable x1 as a parameter. The answer is that it is a parameter to be estimated from the data, but you can avoid specifying it explicitly by using the fixed.x argument to sem().

I hope this helps,

John

On Mon, 28 Mar 2011 09:00:05 -0700 (PDT)
jouba <antrael_at_hotmail.com> wrote:

*>
**>
*

> Dear all ,

*> I am trying to run sem by an example with my data but i have problme with an exogen variable x1 so my examlpe is below
**> when i add i the equation we have no pboblem but i donâ€™t know why ??
**>
**> x1 <->x1, sigmma7, NA
**> for me this an exogen variable and i am not obliged to specify this equation
**>
**> model.se<-specify.model()
**> x1->x2,gamm1,NA
**> x2->x3,gamm2,NA
**> x3>x4,gamm3,NA
**> x4->x5,gamm4,NA
**> x7->x6,gamm5,NA
**> x6->x5,gamm6,NA
**> x2 <->x2 ,sigmma1,NA
**> x3 <->x3 ,simma2,NA
**> x4 <->x4 ,sigmma3,NA
**> x5 <->x5 ,sigmma4,NA
**> x7 <->x7 ,sigmma5,NA
**> x6 <->x6 ,sigmma6,NA
**>
**> sem.se <- sem(model.se, cov(se), 245)
**> Erreur dans solve.default(C) :
**> sous-programme Lapack dgesv : le systĂ¨me est exactement singulier
**> De plus : Message d'avis :
**> In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, :
**> The following variables have no variance or error-variance parameter (double-headed arrow):
**> x1
**> The model is almost surely misspecified; check also for missing covariances.
**>
**> Thanks a lot
**>
**>
**> Antra EL MOUSSELLY
**>
**>
**>
**>
**>
**> Date: Mon, 28 Mar 2011 05:40:32 -0700
**> From: ml-node+3411579-510061861-225466_at_n4.nabble.com
**> To: antrael_at_hotmail.com
**> Subject: Re: Structural equation modeling in R(lavaan,sem)
**>
**> On 03/28/2011 04:18 AM, jouba wrote:
**> >
**> > Jeremy thanks a lot for your response I have read sem package help
**> > and I currently reading the help of lavaan I see that there is also
**> > an other function called lavaan can do the SEM analysis So I wonder
**> > what is the difference between this function and the sem function
**>
**> The 'sem()' function (in the lavaan package) is more user-friendly, in
**> the sence that it sets a number of reasonable options by default, before
**> calling the lower-level 'lavaan()' function (which has the 'feature' of
**> doing nothing automatically, but expects that you really know what your
**> are doing).
**>
**> Most users should only use the 'sem()' function (or the 'cfa()'
**> function). For non-standard models, the 'lavaan()' function gives more
**> control.
**>
**> > Also I am wondering in the case where we have categorical variables
**> > and discreet variables??
**>
**> Currently, the lavaan package (0.4-7) has no support for categorical
**> variables.
**>
**> > calculate the correlation matrix , mainly when we have to calculate
**> > these between a quantitative and qualitative variables, I wonder if
**> > polycor package is the best solution for this
**>
**> It depends. The 'hetcor()' function in the polycor package may provide a
**> suitable correlation matrix that can be used with the 'sem' package or
**> the 'lavaan' package. However, AFAIK, the polycor does not compute the
**> corresponding asymptotic weight matrix which you need for getting proper
**> standard errors and test statistics (in a WLS context).
**>
**> The OpenMx package (http://openmx.psyc.virginia.edu/) has some support
**> for categorical (ie binary/ordinal) observed variables (although I'm not
**> sure if they can handle the joint analysis of ordinal and continuous
**> variables yet).
**>
**> But none of this is needed _if_ the categorical variables are all
**> exogenous (ie predictor variables only) in which case you can still use
**> the methods for continuous data.
**>
**> Yves.
**>
**> --
**> Yves Rosseel -- http://www.da.ugent.be
**> Department of Data Analysis, Ghent University
**> Henri Dunantlaan 1, B-9000 Gent, Belgium
**>
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*

John Fox

Sen. William McMaster Prof. of Social Statistics Department of Sociology

McMaster University

Hamilton, Ontario, Canada

http://socserv.mcmaster.ca/jfox/

R-help_at_r-project.org 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. Received on Mon 28 Mar 2011 - 19:43:40 GMT

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