Structural equation modeling in R(lavaan,sem)

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Structural equation modeling in R(lavaan,sem)

jouba
I am a new user of the function sem in package sem and lavaan for structural equation modeling
1. I don’t know what is the difference between this function and CFA function, I know that cfa for confirmatory analysis but I don’t  know what is the difference between confirmatory analysis and  structural equation modeling in the package lavaan.
2. I have data that I want to analyse but I have some missing data I must to impute these missing data and I use this package or there is a method that can handle missing data (I want to avoid to delete observations where I have some missing data)
3. I have to use variables that arn’t normally distributed , even if I tried to do some transformation to theses variables t I cant success to have normally distributed data , so I decide to  work with these data non normally distributed, my question  my result will be ok even if I have non normally distributd data.
4. If I work with the package ggm for separation d , without latent variables we will have the same result as SEM function I guess
5. How about when we have the number of observation is small n, and what  is the method to know that we have the minimum of observation required??



Thanks a lot


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

Jeremy Miles-2
On 27 March 2011 12:12, jouba <[hidden email]> wrote:

> I am a new user of the function sem in package sem and lavaan for
> structural
> equation modeling
> 1. I don’t know what is the difference between this function and CFA
> function, I know that cfa for confirmatory analysis but I don’t  know what
> is the difference between confirmatory analysis and  structural equation
> modeling in the package lavaan.
>

Confirmatory factor analyses are a class of SEMs.  All CFAs are SEMs, some
SEMs are CFA.  Usually (but definitions vary), if you have a measurement
model only, that's a CFA.  If you have a structural model too, that's SEM.

If you don't understand this distinction, might I suggest a little more
reading before you launch into the world of lavaan?  Things can get quite
tricky quite quickly.


> 2. I have data that I want to analyse but I have some missing data I must
> to
> impute these missing data and I use this package or there is a method that
> can handle missing data (I want to avoid to delete observations where I
> have
> some missing data)
>

No, you can use full information maximum likelihood estimation (= direct ML)
to model data in the presence of missing data.


> 3. I have to use variables that arn’t normally distributed , even if I
> tried
> to do some transformation to theses variables t I cant success to have
> normally distributed data , so I decide to  work with these data non
> normally distributed, my question  my result will be ok even if I have non
> normally distributd data.
>

Depends.  Lavaan can do things like Satorra-Bentler scaled chi-square, which
are robust to non-normality, and corrects your chi-square for (multivariate)
kurtosis.


> 4. If I work with the package ggm for separation d , without latent
> variables we will have the same result as SEM function I guess
>

Not familiar with ggm.  I'll leave that for someone else.


> 5. How about when we have the number of observation is small n, and what
>  is
> the method to know that we have the minimum of observation required??
>
>
>
>
Another very difficult question.  Short answer:  it depends.  Sometimes you
see recommendations based on the number of participants per parameter, which
is usually around 5-10.  These are somewhat flawed, but it's better than
nothing.

Again, I should reiterate that you have a hard road in front of you, and it
will be made much easier if you read a couple of introductory SEM texts,
which will  answer this sort of question.


Jeremy



--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

        [[alternative HTML version deleted]]


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RE: Structural equation modeling in R(lavaan,sem)

jouba
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
Also I am wondering in the case where we have categorical variables and discreet variables??
For me one of the problems is how we will 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 or there is other ideas for functions witch can do the work
Cordially


Antra EL MOUSSELLY


 

Date: Sun, 27 Mar 2011 18:08:02 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

On 27 March 2011 12:12, jouba <[hidden email]> wrote:

> I am a new user of the function sem in package sem and lavaan for
> structural
> equation modeling
> 1. I don’t know what is the difference between this function and CFA
> function, I know that cfa for confirmatory analysis but I don’t  know what
> is the difference between confirmatory analysis and  structural equation
> modeling in the package lavaan.
>

Confirmatory factor analyses are a class of SEMs.  All CFAs are SEMs, some
SEMs are CFA.  Usually (but definitions vary), if you have a measurement
model only, that's a CFA.  If you have a structural model too, that's SEM.

If you don't understand this distinction, might I suggest a little more
reading before you launch into the world of lavaan?  Things can get quite
tricky quite quickly.


> 2. I have data that I want to analyse but I have some missing data I must
> to
> impute these missing data and I use this package or there is a method that
> can handle missing data (I want to avoid to delete observations where I
> have
> some missing data)
>

No, you can use full information maximum likelihood estimation (= direct ML)
to model data in the presence of missing data.


> 3. I have to use variables that arn’t normally distributed , even if I
> tried
> to do some transformation to theses variables t I cant success to have
> normally distributed data , so I decide to  work with these data non
> normally distributed, my question  my result will be ok even if I have non
> normally distributd data.
>

Depends.  Lavaan can do things like Satorra-Bentler scaled chi-square, which
are robust to non-normality, and corrects your chi-square for (multivariate)
kurtosis.


> 4. If I work with the package ggm for separation d , without latent
> variables we will have the same result as SEM function I guess
>

Not familiar with ggm.  I'll leave that for someone else.


> 5. How about when we have the number of observation is small n, and what
>  is
> the method to know that we have the minimum of observation required??
>
>
>
>
Another very difficult question.  Short answer:  it depends.  Sometimes you
see recommendations based on the number of participants per parameter, which
is usually around 5-10.  These are somewhat flawed, but it's better than
nothing.

Again, I should reiterate that you have a hard road in front of you, and it
will be made much easier if you read a couple of introductory SEM texts,
which will  answer this sort of question.


Jeremy



--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

        [[alternative HTML version deleted]]


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

yrosseel
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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RE: Structural equation modeling in R(lavaan,sem)

jouba
 

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

 

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

Jeremy Miles-2
On 28 March 2011 09:00, jouba <[hidden email]> wrote:

Your syntax is not very tidy. That makes it hard to check.


> 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
>

That's probably wrong.


> x4->x5,gamm4,NA
> x7->x6,gamm5,NA
> x6->x5,gamm6,NA
>

Are the above two correct?


> x2 <->x2 ,sigmma1,NA
> x3 <->x3 ,simma2,NA
> x4 <->x4 ,sigmma3,NA
> x5 <->x5 ,sigmma4,NA
> x7 <->x7 ,sigmma5,NA
>
x6 <->x6 ,sigmma6,NA
>
>

It's a somewhat unusual looking model. What are you trying to do?

Jeremy


--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

        [[alternative HTML version deleted]]

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

Fox, John
In reply to this post by jouba
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 <[hidden email]> 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
>  
> 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: [hidden email]
> To: [hidden email]
> 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]]
>

------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

______________________________________________
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RE: Structural equation modeling in R(lavaan,sem)

jouba

 

Effectively in this situation I am working with the function sem in the package sem

I will try this parameter fix.c

 

Thanks a lot



Antra EL MOUSSELLY


 

Date: Mon, 28 Mar 2011 12:42:31 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

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 <[hidden email]> 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
>  
> 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: [hidden email]
> To: [hidden email]
> 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]]
>
------------------------------------------------
John Fox
Sen. William McMaster Prof. of Social Statistics
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

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RE: Structural equation modeling in R(lavaan,sem)

jouba
In reply to this post by Jeremy Miles-2
i am working with the same data with the two sem functions in packages lavaan and sem
the manner how  description of the model are differnt for  the the funcion in sem
and the function sem in  lavaan
 
Thanks a lot

Antra EL MOUSSELLY


 

Date: Mon, 28 Mar 2011 12:33:46 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

On 28 March 2011 09:00, jouba <[hidden email]> wrote:

Your syntax is not very tidy. That makes it hard to check.


> 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
>

That's probably wrong.


> x4->x5,gamm4,NA
> x7->x6,gamm5,NA
> x6->x5,gamm6,NA
>

Are the above two correct?


> x2 <->x2 ,sigmma1,NA
> x3 <->x3 ,simma2,NA
> x4 <->x4 ,sigmma3,NA
> x5 <->x5 ,sigmma4,NA
> x7 <->x7 ,sigmma5,NA
>
x6 <->x6 ,sigmma6,NA
>
>

It's a somewhat unusual looking model. What are you trying to do?

Jeremy


--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

        [[alternative HTML version deleted]]

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RE: Structural equation modeling in R(lavaan,sem)

jouba
In reply to this post by jouba
Dear all,
There is some where documentation to understand all  indices in the output of the function sem(package lavaan ) ??
for example  Chi-square test baseline model, Full model versus baseline model, Loglikelihood and Information Criteria, Root Mean Square Error of Approximation, Standardized Root Mean Square Residual…
Th same question for the sem funtion (sem package)
Thanks in advance for your help
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Re: Structural equation modeling in R(lavaan,sem)

Jeremy Miles-2
sem (the package) documentation is not intended to teach you how to do
SEM (the technique) (there's very little R documentation that is
intended to teach you how to do a particular statistical technique).

There are several good books out there, but here's a free access
journal article, which will help.

http://www.biomedcentral.com/1756-0500/3/267

Might I also suggest you take a look at the semnet list, which is
populated by practitioners of SEM.

Jeremy



On 29 March 2011 12:25, jouba <[hidden email]> wrote:

> Dear all,
> There is some where documentation to understand all  indices in the output
> of the function sem(package lavaan ) ??
> for example  Chi-square test baseline model, Full model versus baseline
> model, Loglikelihood and Information Criteria, Root Mean Square Error of
> Approximation, Standardized Root Mean Square Residual…
> Th same question for the sem funtion (sem package)
> Thanks in advance for your help
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3415954.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [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.
>



--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

______________________________________________
[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
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RE: Structural equation modeling in R(lavaan,sem)

jouba
Ok thanka lot  you for your response

Antra EL MOUSSELLY


 

Date: Tue, 29 Mar 2011 13:31:32 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

sem (the package) documentation is not intended to teach you how to do
SEM (the technique) (there's very little R documentation that is
intended to teach you how to do a particular statistical technique).

There are several good books out there, but here's a free access
journal article, which will help.

http://www.biomedcentral.com/1756-0500/3/267

Might I also suggest you take a look at the semnet list, which is
populated by practitioners of SEM.

Jeremy



On 29 March 2011 12:25, jouba <[hidden email]> wrote:

> Dear all,
> There is some where documentation to understand all  indices in the output
> of the function sem(package lavaan ) ??
> for example  Chi-square test baseline model, Full model versus baseline
> model, Loglikelihood and Information Criteria, Root Mean Square Error of
> Approximation, Standardized Root Mean Square Residual…
> Th same question for the sem funtion (sem package)
> Thanks in advance for your help
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3415954.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [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.
>


--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

______________________________________________
[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.



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RE: Structural equation modeling in R(lavaan,sem)

jouba
In reply to this post by Jeremy Miles-2

Dear all

 

I have an  error mesage

« error message : the MLM estimator can not be used when data are incomplete »

When i use the function sem(package lavaan) and  when i  fill  the  paramters  estimator  and missing 

estimator="MLM", missing="ml"

 

i understand by this that i am not allowed to use estimator= ``mlm ``  in parralle to the parameter missing

 

Best wishes



Antra EL MOUSSELLY


 

Date: Tue, 29 Mar 2011 13:31:32 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

sem (the package) documentation is not intended to teach you how to do
SEM (the technique) (there's very little R documentation that is
intended to teach you how to do a particular statistical technique).

There are several good books out there, but here's a free access
journal article, which will help.

http://www.biomedcentral.com/1756-0500/3/267

Might I also suggest you take a look at the semnet list, which is
populated by practitioners of SEM.

Jeremy



On 29 March 2011 12:25, jouba <[hidden email]> wrote:

> Dear all,
> There is some where documentation to understand all  indices in the output
> of the function sem(package lavaan ) ??
> for example  Chi-square test baseline model, Full model versus baseline
> model, Loglikelihood and Information Criteria, Root Mean Square Error of
> Approximation, Standardized Root Mean Square Residual…
> Th same question for the sem funtion (sem package)
> Thanks in advance for your help
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3415954.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [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.
>


--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

______________________________________________
[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.



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

yrosseel
On 03/29/2011 10:49 PM, jouba wrote:
>
> Dear all
>
> I have an  error mesage
> « error message : the MLM estimator can not be used when data are incomplete »
> When i use the function sem(package lavaan) and  when i  fill  the  paramters  estimator  and missing
> estimator="MLM", missing="ml"
>
> i understand by this that i am not allowed to use estimator= ``mlm ``  in parralle to the parameter missing

You understood correctly. Estimator 'MLM' (=ML estimation +
Satorra-Bentler scaled test statistic) is only defined for complete
data. The equivalent for incomplete data is the estimator 'MLR' (=ML
estimation + Yuan-Bentler scaled test statistic).

Yves.

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RE: Structural equation modeling in R(lavaan,sem)

jouba
In reply to this post by Jeremy Miles-2

Daer all,

I have a question concerning longitudinal data:

When we have a longitudinal data and we have to do sem analysis there is in the package lavaan some functions,options in this package that help to do this or we can treat these data like non longitudinal data

 

Thanks you a lot

 



Antra EL MOUSSELLY


 

Date: Tue, 29 Mar 2011 13:31:32 -0700
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

sem (the package) documentation is not intended to teach you how to do
SEM (the technique) (there's very little R documentation that is
intended to teach you how to do a particular statistical technique).

There are several good books out there, but here's a free access
journal article, which will help.

http://www.biomedcentral.com/1756-0500/3/267

Might I also suggest you take a look at the semnet list, which is
populated by practitioners of SEM.

Jeremy



On 29 March 2011 12:25, jouba <[hidden email]> wrote:

> Dear all,
> There is some where documentation to understand all  indices in the output
> of the function sem(package lavaan ) ??
> for example  Chi-square test baseline model, Full model versus baseline
> model, Loglikelihood and Information Criteria, Root Mean Square Error of
> Approximation, Standardized Root Mean Square Residual…
> Th same question for the sem funtion (sem package)
> Thanks in advance for your help
>
>
> --
> View this message in context: http://r.789695.n4.nabble.com/Structural-equation-modeling-in-R-lavaan-sem-tp3409642p3415954.html
> Sent from the R help mailing list archive at Nabble.com.
>
> ______________________________________________
> [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.
>


--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

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

Jeremy Miles-2
On 3 April 2011 12:38, jouba <[hidden email]> wrote:
>
> Daer all,
> I have a question concerning longitudinal data:
> When we have a longitudinal data and we have to do sem analysis there is in the package lavaan some functions,options in this package that help to do this or we can treat these data like non longitudinal data
>


No, and (qualified) no.

1. There are (AFAIK) no options, functions that are specific to
longitudinal data.

2. You don't treat these data as non-longitudinal data, you add
parameters that are appropriate though.  For example, look at the
model shown on http://lavaan.ugent.be.  dem60 and dem65 are two
measures of the same construct at different timepoints, so there are
correlations over time for each pair of measured variables that are
measures of that construct - i.e. y1 ~~ y5

3. You would get much better answers on the SEM mailing list - semnet.
You can join it here: http://www2.gsu.edu/~mkteer/semnet.html#Joining.

Jeremy



--
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com

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

yrosseel
In reply to this post by jouba
On 04/03/2011 09:38 PM, jouba wrote:
>
> Daer all, I have a question concerning longitudinal data: When we
> have a longitudinal data and we have to do sem analysis there is in
> the package lavaan some functions,options in this package that help
> to do this or we can treat these data like non longitudinal data

The function 'growth' (in the lavaan package) can be used for (standard)
growth modeling. Good material about growth modeling (using Mplus) can
be found here:

http://statistics.ats.ucla.edu/stat/mplus/seminars/gm/default.htm

Next, you can read how to do growth modeling with lavaan by reading
section 7 in the lavaan intro, which you can download from the
documentation section on the lavaan website (http://lavaan.org).

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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RE: Structural equation modeling in R(lavaan,sem)

jouba

Thanks you for your response
For lavaan package can i have more information about this example you have applied in the section 7
the meanings  of The variables (c1,c2,c3,c4, i ,s ,x1,x2)
I think i have need more information to learn  more about how  able to apply growth model  in my data (longitudianl data)
Thanks a lot

Antra EL MOUSSELLY


 

Date: Mon, 4 Apr 2011 02:16:42 -0500
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

On 04/03/2011 09:38 PM, jouba wrote:
>
> Daer all, I have a question concerning longitudinal data: When we
> have a longitudinal data and we have to do sem analysis there is in
> the package lavaan some functions,options in this package that help
> to do this or we can treat these data like non longitudinal data

The function 'growth' (in the lavaan package) can be used for (standard)
growth modeling. Good material about growth modeling (using Mplus) can
be found here:

http://statistics.ats.ucla.edu/stat/mplus/seminars/gm/default.htm

Next, you can read how to do growth modeling with lavaan by reading
section 7 in the lavaan intro, which you can download from the
documentation section on the lavaan website (http://lavaan.org).

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

yrosseel
On 04/04/2011 07:14 PM, jouba wrote:
>
>
> Thanks you for your response
> For lavaan package can i have more information about this example you have applied in the section 7
> the meanings  of The variables (c1,c2,c3,c4, i ,s ,x1,x2)
> I think i have need more information to learn  more about how  able to apply growth model  in my data (longitudianl data)

In the example, c1-c4 are time-varying covariates, i and s are the
random intercept and slope respectively, and x1 and x2 are two exogenous
covariates influencing the intercept and slope.

Please note: the lavaanIntroduction document is hardly useful to _learn_
about growth models (or any SEM model for that matter). It only explains
how to fit them using the lavaan package. To learn about growth models,
you may want to read any one of the books below:

Latent Curve Models: A Structural Equation Perspective (Wiley Series in
Probability and Statistics) by Kenneth A. Bollen and Patrick J. Curran
(Hardcover - Dec 23, 2005)

Latent Growth Curve Modeling (Quantitative Applications in the Social
Sciences) by Dr. Kristopher J. Preacher, Aaron Lee Wichman, Robert
Charles MacCallum and Dr. Nancy E. Briggs (Paperback - Jun 27, 2008)

An Introduction to Latent Variable Growth Curve Modeling: Concepts,
Issues, and Applications (Quantitative Methodology) (Quantitative
Methodology Series) by Terry E. Duncan, Susan C. Duncan and Lisa A.
Strycker (Paperback - May 23, 2006)

Yves.

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RE: Structural equation modeling in R(lavaan,sem)

jouba
Thanks a lot

Antra EL MOUSSELLY


 

Date: Tue, 5 Apr 2011 09:49:08 -0500
From: [hidden email]
To: [hidden email]
Subject: Re: Structural equation modeling in R(lavaan,sem)

On 04/04/2011 07:14 PM, jouba wrote:
>
>
> Thanks you for your response
> For lavaan package can i have more information about this example you have applied in the section 7
> the meanings  of The variables (c1,c2,c3,c4, i ,s ,x1,x2)
> I think i have need more information to learn  more about how  able to apply growth model  in my data (longitudianl data)

In the example, c1-c4 are time-varying covariates, i and s are the
random intercept and slope respectively, and x1 and x2 are two exogenous
covariates influencing the intercept and slope.

Please note: the lavaanIntroduction document is hardly useful to _learn_
about growth models (or any SEM model for that matter). It only explains
how to fit them using the lavaan package. To learn about growth models,
you may want to read any one of the books below:

Latent Curve Models: A Structural Equation Perspective (Wiley Series in
Probability and Statistics) by Kenneth A. Bollen and Patrick J. Curran
(Hardcover - Dec 23, 2005)

Latent Growth Curve Modeling (Quantitative Applications in the Social
Sciences) by Dr. Kristopher J. Preacher, Aaron Lee Wichman, Robert
Charles MacCallum and Dr. Nancy E. Briggs (Paperback - Jun 27, 2008)

An Introduction to Latent Variable Growth Curve Modeling: Concepts,
Issues, and Applications (Quantitative Methodology) (Quantitative
Methodology Series) by Terry E. Duncan, Susan C. Duncan and Lisa A.
Strycker (Paperback - May 23, 2006)

Yves.

______________________________________________
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and provide commented, minimal, self-contained, reproducible code.



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