[R] Confirmatory factor analysis problems using sem package (works in Amos)
Solomon Messing
messing at stanford.edu
Tue Jul 28 20:38:28 CEST 2009
Thanks John. Can I ask under what function you call optim/nlm? I cannot
see how this is done by examining sem. Thanks,
-Solomon
> -----Original Message-----
> From: John Fox [mailto:jfox at mcmaster.ca]
> Sent: Monday, July 27, 2009 6:23 PM
> To: 'Solomon Messing'
> Cc: r-help at r-project.org
> Subject: RE: [R] Confirmatory factor analysis problems using sem package
> (works in Amos)
>
> Dear Solomon,
>
> When I originally programmed the sem() function, I used optim() and
> experimented with the different methods provided, settling on "BFGS" as
> the
> default. Shortly after that, I compared optim() to nlm() for a range of
> problems and found that the latter performed better. It would not be hard
> to
> replace nlm() with optim() in sem(), if you wish to do it.
>
> Regards,
> John
>
>
> > -----Original Message-----
> > From: Solomon Messing [mailto:messing at stanford.edu]
> > Sent: July-27-09 8:01 PM
> > To: 'John Fox'
> > Cc: r-help at r-project.org
> > Subject: RE: [R] Confirmatory factor analysis problems using sem package
> > (works in Amos)
> >
> > Dear John,
> >
> > Would it possible to use a different optimizer with the sem package?
> > Perhaps optim(..., method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B",
> > "SANN"),...) for example?
> >
> > Thank you very much,
> >
> > -Solomon
> >
> > > -----Original Message-----
> > > From: John Fox [mailto:jfox at mcmaster.ca]
> > > Sent: Friday, May 22, 2009 6:25 AM
> > > To: 'S. Messing'
> > > Cc: r-help at r-project.org
> > > Subject: RE: [R] Confirmatory factor analysis problems using sem
> package
> > > (works in Amos)
> > >
> > > Dear Solomon,
> > >
> > > > -----Original Message-----
> > > > From: r-help-bounces at r-project.org
> [mailto:r-help-bounces at r-project.org]
> > > On
> > > > Behalf Of S. Messing
> > > > Sent: May-22-09 1:27 AM
> > > > To: r-help at r-project.org
> > > > Subject: [R] Confirmatory factor analysis problems using sem package
> > > (works
> > > > in Amos)
> > > >
> > > >
> > > > Hello all,
> > > >
> > > > I'm trying to replicate a confirmatory factor analysis done in Amos.
> > >
> > > Occasionally in an ill-conditioned problem, one program will produce a
> > > solution and another won't. As a general matter, I'd expect Amos to be
> > > more
> > > robust than sem() since Amos is written specifically for SEMs, while
> sem()
> > > uses nlm(), a general-purpose optimizer.
> > >
> > > > The
> > > > idea is to compare a one-factor and a two-factor model. I get the
> > > following
> > > > warning message when I run either model:
> > > >
> > > > "Could not compute QR decomposition of Hessian.
> > > > Optimization probably did not converge."
> > > >
> > > > I have no idea what to do here.
> > >
> > > A general strategy is to set debug=TRUE in the call to sem() and see
> what
> > > happens in the optimization. Then there are several things that you
> can
> do
> > > to try to get the optimization to converge; see ?sem. In this case,
> > > however,
> > > I wasn't able to get a solution.
> > >
> > > The one-factor model is equivalent to a one-factor exploratory FA,
> which
> > > can
> > > be fit by ML using factanal():
> > >
> > > > factanal(factors=1, covmat=correl, n.obs=1100)
> > >
> > > Call:
> > > factanal(factors = 1, covmat = correl, n.obs = 1100)
> > >
> > > Uniquenesses:
> > > pvote jmposaff jmnegaff boposaff bonegaff
> > > obama.therm mccain.therm oddcand.D evencand.D
> > > 0.100 0.496 0.497 0.277 0.397
> > > 0.129 0.312 0.466 0.585
> > >
> > > Loadings:
> > > Factor1
> > > pvote -0.949
> > > jmposaff 0.710
> > > jmnegaff -0.709
> > > boposaff -0.850
> > > bonegaff 0.777
> > > obama.therm -0.934
> > > mccain.therm 0.830
> > > oddcand.D 0.731
> > > evencand.D 0.645
> > >
> > > Factor1
> > > SS loadings 5.744
> > > Proportion Var 0.638
> > >
> > > Test of the hypothesis that 1 factor is sufficient.
> > > The chi square statistic is 1710.03 on 27 degrees of freedom.
> > > The p-value is 0
> > >
> > > As you can see, the one-factor model fits the data very poorly (as
> does
> a
> > > two-factor EFA); I suspect, but am not sure, that this is the source
> of
> > > the
> > > problem in sem(). I couldn't get a solution from sem() even when I
> used
> > > the
> > > factanal() solution as start values.
> > >
> > >
> > > > I believe posters reported the same
> > > > problem.
> > >
> > > In almost all cases, the models haven't been properly specified, which
> is
> > > not the case here. Here, the model just doesn't fit the data.
> > >
> > > > It seems that the ability to invert the correlation matrix may
> > > > have something to do with this error, but solve(correl) yields a
> > > solution.
> > >
> > > No, the input correlation matrix is positive-definite. sem() would
> have
> > > complained if it were not:
> > >
> > > > eigen(correl, only.values=TRUE)
> > > $values
> > > [1] 6.12561630 0.82418329 0.71616585 0.51263750 0.24467315 0.18248909
> > > 0.17024374
> > > [8] 0.13905585 0.08493524
> > >
> > >
> > > I'll keep your problem as a test case to see whether I can produce a
> > > solution, possibly using a different optimizer -- as I mentioned,
> sem()
> > > uses
> > > nlm().
> > >
> > > Regards,
> > > John
> > >
> > >
> > > >
> > > > Here are my correlation matrix and model specifications:
> > > >
> > > > --------------------------- R CODE BEGIN
> > > > ------------------------------------------------
> > > >
> > > > library(sem)
> > > > correl <- matrix(
> > > > c(1.0000000,-0.6657822,0.6702089,0.7997673,-0.7225454,0.8992372,
> > > > -0.8026491,-0.6715168,-0.5781565,-
> > > > 0.6657822,1.0000000,-0.5107568,
> > > > -0.5030886,0.6971188,-
> > > > 0.6306937,0.7200848,0.5121227,0.4496810,
> > > >
0.6702089,-0.5107568,1.0000000,0.7276558,-
> > > > 0.3893792,0.6043672,
> > > >
-0.7176532,-0.5247434,-0.4670362,0.7997673,-
> > > > 0.5030886,0.7276558,
> > > >
1.0000000,-0.6251056,0.8164190,-0.6728515,-
> > > > 0.6371453,-0.5531964,
> > > > -0.7225454,0.6971188,-0.3893792,-
> > > > 0.6251056,1.0000000,-0.7760765,
> > > >
0.6175124,0.5567924,0.4914176,0.8992372,-
> > > > 0.6306937,0.6043672,
> > > >
0.8164190,-0.7760765,1.0000000,-0.7315507,-
> > > > 0.6625136,-0.5814590,
> > > > -0.8026491,0.7200848,-0.7176532,-
> > > > 0.6728515,0.6175124,-0.7315507,
> > > >
> > > 1.0000000,0.5910688,0.5096898,-0.6715168,0.5121227,-
> > > > 0.5247434,
> > > > -0.6371453,0.5567924,-
> > > > 0.6625136,0.5910688,1.0000000,0.8106496,
> > > > -0.5781565,0.4496810,-0.4670362,-
> > > > 0.5531964,0.4914176,-0.5814590,
> > > > 0.5096898,0.8106496,1.0000000),
> > > ,nrow=9,ncol=9)
> > > >
> > > > rownames(correl) = c("pvote", "jmposaff", "jmnegaff",
> > > > "boposaff","bonegaff",
> > > > "obama.therm", "mccain.therm",
> > > > "oddcand.D", "evencand.D" )
> > > >
> > > > colnames(correl) = c("pvote", "jmposaff", "jmnegaff",
> > > > "boposaff","bonegaff",
> > > > "obama.therm", "mccain.therm",
> > > > "oddcand.D", "evencand.D" )
> > > >
> > > > #One Factor Model:
> > > > model.all <- specify.model()
> > > > allmeasures -> pvote, b1, NA
> > > > allmeasures -> obama.therm, b2, NA
> > > > allmeasures -> mccain.therm, b3, NA
> > > > allmeasures -> jmposaff, b4, NA
> > > > allmeasures -> jmnegaff, b5, NA
> > > > allmeasures -> boposaff, b6, NA
> > > > allmeasures -> bonegaff, b7, NA
> > > > allmeasures -> oddcand.D, b8, NA
> > > > allmeasures -> evencand.D, b9, NA
> > > > allmeasures <-> allmeasures, NA,1
> > > > pvote <-> pvote, v1, NA
> > > > obama.therm <-> obama.therm, v2, NA
> > > > mccain.therm <-> mccain.therm, v3, NA
> > > > jmposaff <-> jmposaff, v4, NA
> > > > jmnegaff <-> jmnegaff, v5, NA
> > > > boposaff <-> boposaff, v6, NA
> > > > bonegaff <-> bonegaff, v7, NA
> > > > oddcand.D <-> oddcand.D, v8, NA
> > > > evencand.D <-> evencand.D, v9, NA
> > > >
> > > >
> > > > sem.all <- sem(model.all, correl, 1100)
> > > >
> > > > summary(sem.all)
> > > >
> > > > #Two Factor Model:
> > > > model.vi <- specify.model()
> > > > verbal -> pvote, b1, NA
> > > > verbal -> obama.therm, b2, NA
> > > > verbal -> mccain.therm, b3, NA
> > > > verbal -> jmposaff, b4, NA
> > > > verbal -> jmnegaff, b5, NA
> > > > verbal -> boposaff, b6, NA
> > > > verbal -> bonegaff, b7, NA
> > > > imp -> oddcand.D, b8, NA
> > > > imp -> evencand.D, b9, NA
> > > > imp <-> imp, NA, 1
> > > > verbal <-> verbal, NA, 1
> > > > pvote <-> pvote, v1, NA
> > > > obama.therm <-> obama.therm, v2, NA
> > > > mccain.therm <-> mccain.therm, v3, NA
> > > > jmposaff <-> jmposaff, v4, NA
> > > > jmnegaff <-> jmnegaff, v5, NA
> > > > boposaff <-> boposaff, v6, NA
> > > > bonegaff <-> bonegaff, v7, NA
> > > > oddcand.D <-> oddcand.D, v8, NA
> > > > evencand.D <-> evencand.D, v9, NA
> > > > imp <-> verbal, civ, NA
> > > >
> > > > sem.vi <- sem(model.vi, correl, 1100)
> > > > summary(sem.vi)
> > > >
> > > > --------------------------- R CODE END
> > > > ------------------------------------------------
> > > >
> > > > Thanks very much.
> > > >
> > > > -Solomon
> > > > --
> > > > View this message in context:
> http://www.nabble.com/Confirmatory-factor-
> > > > analysis-problems-using-sem-package-%28works-in-Amos%29-
> > > > tp23664618p23664618.html
> > > > Sent from the R help mailing list archive at Nabble.com.
> > > >
> > > > ______________________________________________
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> > > > PLEASE do read the posting guide
> > > http://www.R-project.org/posting-guide.html
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> > >
> >
>
>
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