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 donât 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 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
Antra EL MOUSSELLY Date: Mon, 28 Mar 2011 05:40:32 -0700 From: ml-node+3411579-510061861-225...@n4.nabble.com To: antr...@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 ______________________________________________ [hidden email] 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. If you reply to this email, your message will be added to the discussion below:http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3411579.html To unsubscribe from Structural equation modeling in R(lavaan,sem), click here. -- View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3412181.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]
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