Re: [R] Structural equation modeling in R(lavaan,sem)

From: John Fox <>
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,

On Mon, 28 Mar 2011 09:00:05 -0700 (PDT)  jouba <> 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 don’t know why ??
> x1 <->x1, sigmma7, NA
> for me this an exogen variable and i am not obliged to specify this equation
> 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(, cov(se), 245)
> Erreur dans solve.default(C) :
> sous-programme Lapack dgesv : le système 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
> Date: Mon, 28 Mar 2011 05:40:32 -0700
> From:
> To:
> 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 ( 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 --
> 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 mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Mon 28 Mar 2011 - 19:43:40 GMT

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