subject:"\[R\] Help with SEM package \- model significance"

Re: [R] Help with SEM package - model significance

2014-07-21 Thread Bernardo Santos

Hi John,

Thanks for your reply (1 month later lol).
In fact maybe the point is that I do not understand exactly the role of latent 
variables (what they are, and how to define them in R) in SEM.
Do you have any suggestion of easy basic literature on SEM that can help me 
with that?
Most things I have read are old (and use some different statistics programs) 
and offer examples too far from ecology, than I had some difficulties to 
understand the method in general.

But, as far as I understood, SEM is like a simple multiple regression (linear 
model), but that takes into account the relation of different variables 
simultaneously, isn't that?

Thank you very much.
Best regards,

Bernardo



Em Segunda-feira, 16 de Junho de 2014 8:40, John Fox  
escreveu:
 


Dear Bernardo,

The df for the LR chisquare over-identification test come not from the number 
of observations, but from the difference between the number of observable 
variances and covariances, on the one hand, and free parameters to estimate, on 
the other. In your case, these numbers are equal, and so df = 0. The LR 
chisquare for a just-identified model is also necessarily 0: the model 
perfectly reproduces the covariational structure of the observed variables. 

R (and most statistical software) by default writes very small and very large 
numbers in scientific format. In your case, -2.873188e-13 = -2.87*10^-13, that 
is, 0 within rounding error. You can change the way numbers are printed with 
the R scipen option.

Some other observations:

(1) Your model is recursive and has no latent variables; you would get the same 
estimates from OLS regression using lm().

(2) For quite some time now, the sem package has included specifyEquations() as 
a more convenient way of specifying a model, in preference to specifyModel(). 
See ?specifyEquations.

(3) You don't have to specify the error variances directly; specifyEquations(), 
or specifyModel(), will supply them.

I hope this helps,
John



John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/
    
    
On Sun, 15 Jun 2014 20:15:31 -0700 (PDT)

> Dear all, 
> 
> I used "sem" function from the package SEM to fit a model. However, I cannot 
> say if the model is correspondent to the data or not (chisquare test).
> I used the commands:
> 
> model1 <- specifyModel()
> estadio -> compflora, a1, NA
> estadio -> compfauna, a2, NA
> estadio -> interacoesobs, a3, NA
> compflora -> compfauna, b1, NA
> compflora -> interacoesobs, b2, NA
> compfauna -> interacoesobs, c1, NA
> estadio <-> estadio, e1, NA
> compflora <-> compflora, e2, NA
> compfauna <-> compfauna, e3, NA
> interacoesobs <-> interacoesobs, e4, NA
> 
> sem1 <- sem(model1, cov.matrix, length(samples))
> summary(sem1)
> 
> and I got the result:
> 
> Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA AIC =  20 BIC =  
> -2.873188e-13 Normalized Residuals Min.   1st Qu.    Median      Mean   3rd 
> Qu.      Max. 
> 0.000e+00 0.000e+00 2.957e-16 3.193e-16 5.044e-16 8.141e-16  R-square for 
> Endogenous Variables compflora     compfauna interacoesobs  0.0657        
> 0.1056        0.2319  Parameter Estimates Estimate     Std Error    z value   
>  Pr(>|z|)                                    
> a1 3.027344e-01 1.665395e-01 1.81779316 6.909575e-02 compflora <--- estadio   
>        
> a2 2.189427e-01 1.767404e-01 1.23878105 2.154266e-01 compfauna <--- estadio   
>        
> a3 7.314192e-03 1.063613e-01 0.06876742 9.451748e-01 interacoesobs <--- 
> estadio      
> b1 2.422906e-01 1.496290e-01 1.61927587 1.053879e-01 compfauna <--- compflora 
>        
> b2 3.029933e-01 9.104901e-02 3.32780446 8.753328e-04 interacoesobs <--- 
> compflora    
> c1 4.863368e-02 8.638177e-02 0.56300857 5.734290e-01 interacoesobs <--- 
> compfauna    
> e1 6.918133e+04 1.427102e+04 4.84767986 1.249138e-06 estadio <--> estadio     
>        
> e2 9.018230e+04 1.860319e+04 4.84767986 1.249138e-06 compflora <--> compflora 
>        
> e3 9.489661e+04 1.957568e+04 4.84767986 1.249138e-06 compfauna <--> compfauna 
>        
> e4 3.328072e+04 6.865289e+03 4.84767986 1.249138e-06 interacoesobs <--> 
> interacoesobs Iterations =  0 
> 
> I understand the results, but I do not know how to interpret the first line 
> that tells me about the model:
> Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA
> 
> How can DF be zero, if the number of observations I used in sem funcition was 
> 48 and I have only 4 variables? What is the p value?
> 
> Thanks in advance.
> Bernardo Niebuhr
>     [[alternative HTML version deleted]]
> 
[[alternative HTML version deleted]]

__
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Re: [R] Help with SEM package - model significance

2014-07-21 Thread John Fox

Dear Bernado,

 This isn't really a suitable topic to pursue on the r-help list, so I'll just 
comment briefly:

On Mon, 21 Jul 2014 17:34:52 -0700
 Bernardo Santos  wrote:
> Hi John,
> 
> Thanks for your reply (1 month later lol).
> In fact maybe the point is that I do not understand exactly the role of 
> latent variables (what they are, and how to define them in R) in SEM.
> Do you have any suggestion of easy basic literature on SEM that can help me 
> with that?
> Most things I have read are old (and use some different statistics programs) 
> and offer examples too far from ecology, than I had some difficulties to 
> understand the method in general.
> 

I have some materials at 
 from a 
recent workshop on SEMs, including some reading and suggestions for reading, 
but not I'm afraid from ecology.
 
> But, as far as I understood, SEM is like a simple multiple regression (linear 
> model), but that takes into account the relation of different variables 
> simultaneously, isn't that?
> 

In SEMs the response variable from one regression equation can be an 
explanatory variable in another, and the models can incorporate latent 
variables, which aren't measured directly, but rather indirectly through their 
observable effects ("indicators") or even in some cases through their 
observable causes.

I hope this helps,
 John

> Thank you very much.
> Best regards,
> 
> Bernardo
> 
> 
> 
> Em Segunda-feira, 16 de Junho de 2014 8:40, John Fox  
> escreveu:
>  
> 
> 
> Dear Bernardo,
> 
> The df for the LR chisquare over-identification test come not from the number 
> of observations, but from the difference between the number of observable 
> variances and covariances, on the one hand, and free parameters to estimate, 
> on the other. In your case, these numbers are equal, and so df = 0. The LR 
> chisquare for a just-identified model is also necessarily 0: the model 
> perfectly reproduces the covariational structure of the observed variables. 
> 
> R (and most statistical software) by default writes very small and very large 
> numbers in scientific format. In your case, -2.873188e-13 = -2.87*10^-13, 
> that is, 0 within rounding error. You can change the way numbers are printed 
> with the R scipen option.
> 
> Some other observations:
> 
> (1) Your model is recursive and has no latent variables; you would get the 
> same estimates from OLS regression using lm().
> 
> (2) For quite some time now, the sem package has included specifyEquations() 
> as a more convenient way of specifying a model, in preference to 
> specifyModel(). See ?specifyEquations.
> 
> (3) You don't have to specify the error variances directly; 
> specifyEquations(), or specifyModel(), will supply them.
> 
> I hope this helps,
> John
> 
> 
> 
> John Fox, Professor
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox/
>     
>     
> On Sun, 15 Jun 2014 20:15:31 -0700 (PDT)
> Bernardo Santos  wrote:
> > Dear all, 
> > 
> > I used "sem" function from the package SEM to fit a model. However, I 
> > cannot say if the model is correspondent to the data or not (chisquare 
> > test).
> > I used the commands:
> > 
> > model1 <- specifyModel()
> > estadio -> compflora, a1, NA
> > estadio -> compfauna, a2, NA
> > estadio -> interacoesobs, a3, NA
> > compflora -> compfauna, b1, NA
> > compflora -> interacoesobs, b2, NA
> > compfauna -> interacoesobs, c1, NA
> > estadio <-> estadio, e1, NA
> > compflora <-> compflora, e2, NA
> > compfauna <-> compfauna, e3, NA
> > interacoesobs <-> interacoesobs, e4, NA
> > 
> > sem1 <- sem(model1, cov.matrix, length(samples))
> > summary(sem1)
> > 
> > and I got the result:
> > 
> > Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA AIC =  20 BIC =  
> > -2.873188e-13 Normalized Residuals Min.   1st Qu.    Median      Mean   3rd 
> > Qu.      Max. 
> > 0.000e+00 0.000e+00 2.957e-16 3.193e-16 5.044e-16 8.141e-16  R-square for 
> > Endogenous Variables compflora     compfauna interacoesobs  0.0657        
> > 0.1056        0.2319  Parameter Estimates Estimate     Std Error    z value 
> >    Pr(>|z|)                                    
> > a1 3.027344e-01 1.665395e-01 1.81779316 6.909575e-02 compflora <--- estadio 
> >          
> > a2 2.189427e-01 1.767404e-01 1.23878105 2.154266e-01 compfauna <--- estadio 
> >          
> > a3 7.314192e-03 1.063613e-01 0.06876742 9.451748e-01 interacoesobs <--- 
> > estadio      
> > b1 2.422906e-01 1.496290e-01 1.61927587 1.053879e-01 compfauna <--- 
> > compflora        
> > b2 3.029933e-01 9.104901e-02 3.32780446 8.753328e-04 interacoesobs <--- 
> > compflora    
> > c1 4.863368e-02 8.638177e-02 0.56300857 5.734290e-01 interacoesobs <--- 
> > compfauna    
> > e1 6.918133e+04 1.427102e+04 4.84767986 1.249138e-06 estadio <--> estadio   
> >          
> > e2 9.018230e+04 1.860319e+04 4.84767986 1.249138e-06 compflora <--> 
> > com

Re: [R] Help with SEM package - model significance

2014-06-16 Thread John Fox

Dear Bernardo,

The df for the LR chisquare over-identification test come not from the number 
of observations, but from the difference between the number of observable 
variances and covariances, on the one hand, and free parameters to estimate, on 
the other. In your case, these numbers are equal, and so df = 0. The LR 
chisquare for a just-identified model is also necessarily 0: the model 
perfectly reproduces the covariational structure of the observed variables. 

R (and most statistical software) by default writes very small and very large 
numbers in scientific format. In your case, -2.873188e-13 = -2.87*10^-13, that 
is, 0 within rounding error. You can change the way numbers are printed with 
the R scipen option.

Some other observations:

(1) Your model is recursive and has no latent variables; you would get the same 
estimates from OLS regression using lm().

(2) For quite some time now, the sem package has included specifyEquations() as 
a more convenient way of specifying a model, in preference to specifyModel(). 
See ?specifyEquations.

(3) You don't have to specify the error variances directly; specifyEquations(), 
or specifyModel(), will supply them.

I hope this helps,
 John

John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.mcmaster.ca/jfox/

On Sun, 15 Jun 2014 20:15:31 -0700 (PDT)
 Bernardo Santos  wrote:
> Dear all, 
> 
> I used "sem" function from the package SEM to fit a model. However, I cannot 
> say if the model is correspondent to the data or not (chisquare test).
> I used the commands:
> 
> model1 <- specifyModel()
> estadio -> compflora, a1, NA
> estadio -> compfauna, a2, NA
> estadio -> interacoesobs, a3, NA
> compflora -> compfauna, b1, NA
> compflora -> interacoesobs, b2, NA
> compfauna -> interacoesobs, c1, NA
> estadio <-> estadio, e1, NA
> compflora <-> compflora, e2, NA
> compfauna <-> compfauna, e3, NA
> interacoesobs <-> interacoesobs, e4, NA
> 
> sem1 <- sem(model1, cov.matrix, length(samples))
> summary(sem1)
> 
> and I got the result:
> 
> Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA AIC =  20 BIC =  
> -2.873188e-13 Normalized Residuals Min.   1st Qu.Median  Mean   3rd 
> Qu.  Max. 
> 0.000e+00 0.000e+00 2.957e-16 3.193e-16 5.044e-16 8.141e-16  R-square for 
> Endogenous Variables compflora compfauna interacoesobs  0.0657
> 0.10560.2319  Parameter Estimates Estimate Std Errorz value   
>  Pr(>|z|) 
> a1 3.027344e-01 1.665395e-01 1.81779316 6.909575e-02 compflora <--- estadio   
>
> a2 2.189427e-01 1.767404e-01 1.23878105 2.154266e-01 compfauna <--- estadio   
>
> a3 7.314192e-03 1.063613e-01 0.06876742 9.451748e-01 interacoesobs <--- 
> estadio  
> b1 2.422906e-01 1.496290e-01 1.61927587 1.053879e-01 compfauna <--- compflora 
>
> b2 3.029933e-01 9.104901e-02 3.32780446 8.753328e-04 interacoesobs <--- 
> compflora
> c1 4.863368e-02 8.638177e-02 0.56300857 5.734290e-01 interacoesobs <--- 
> compfauna
> e1 6.918133e+04 1.427102e+04 4.84767986 1.249138e-06 estadio <--> estadio 
>
> e2 9.018230e+04 1.860319e+04 4.84767986 1.249138e-06 compflora <--> compflora 
>
> e3 9.489661e+04 1.957568e+04 4.84767986 1.249138e-06 compfauna <--> compfauna 
>
> e4 3.328072e+04 6.865289e+03 4.84767986 1.249138e-06 interacoesobs <--> 
> interacoesobs Iterations =  0 
> 
> I understand the results, but I do not know how to interpret the first line 
> that tells me about the model:
> Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA
> 
> How can DF be zero, if the number of observations I used in sem funcition was 
> 48 and I have only 4 variables? What is the p value?
> 
> Thanks in advance.
> Bernardo Niebuhr
>   [[alternative HTML version deleted]]
>

__
R-help@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.

[R] Help with SEM package - model significance

2014-06-15 Thread Bernardo Santos

Dear all, 

I used "sem" function from the package SEM to fit a model. However, I cannot 
say if the model is correspondent to the data or not (chisquare test).
I used the commands:

model1 <- specifyModel()
estadio -> compflora, a1, NA
estadio -> compfauna, a2, NA
estadio -> interacoesobs, a3, NA
compflora -> compfauna, b1, NA
compflora -> interacoesobs, b2, NA
compfauna -> interacoesobs, c1, NA
estadio <-> estadio, e1, NA
compflora <-> compflora, e2, NA
compfauna <-> compfauna, e3, NA
interacoesobs <-> interacoesobs, e4, NA

sem1 <- sem(model1, cov.matrix, length(samples))
summary(sem1)

and I got the result:

Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA AIC =  20 BIC =  
-2.873188e-13 Normalized Residuals Min.   1st Qu.Median  Mean   3rd Qu. 
 Max. 
0.000e+00 0.000e+00 2.957e-16 3.193e-16 5.044e-16 8.141e-16  R-square for 
Endogenous Variables compflora compfauna interacoesobs  0.0657
0.10560.2319  Parameter Estimates Estimate Std Errorz value
Pr(>|z|) 
a1 3.027344e-01 1.665395e-01 1.81779316 6.909575e-02 compflora <--- estadio 
 
a2 2.189427e-01 1.767404e-01 1.23878105 2.154266e-01 compfauna <--- estadio 
 
a3 7.314192e-03 1.063613e-01 0.06876742 9.451748e-01 interacoesobs <--- estadio 
 
b1 2.422906e-01 1.496290e-01 1.61927587 1.053879e-01 compfauna <--- compflora   
 
b2 3.029933e-01 9.104901e-02 3.32780446 8.753328e-04 interacoesobs <--- 
compflora
c1 4.863368e-02 8.638177e-02 0.56300857 5.734290e-01 interacoesobs <--- 
compfauna
e1 6.918133e+04 1.427102e+04 4.84767986 1.249138e-06 estadio <--> estadio   
 
e2 9.018230e+04 1.860319e+04 4.84767986 1.249138e-06 compflora <--> compflora   
 
e3 9.489661e+04 1.957568e+04 4.84767986 1.249138e-06 compfauna <--> compfauna   
 
e4 3.328072e+04 6.865289e+03 4.84767986 1.249138e-06 interacoesobs <--> 
interacoesobs Iterations =  0 

I understand the results, but I do not know how to interpret the first line 
that tells me about the model:
Model Chisquare =  -2.873188e-13   Df =  0 Pr(>Chisq) = NA

How can DF be zero, if the number of observations I used in sem funcition was 
48 and I have only 4 variables? What is the p value?

Thanks in advance.
Bernardo Niebuhr
[[alternative HTML version deleted]]

__
R-help@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.

Re: [R] Help with SEM package - model significance

Re: [R] Help with SEM package - model significance

Re: [R] Help with SEM package - model significance

[R] Help with SEM package - model significance

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