Dear Chad,
It's possible that I don't understand properly what you've done, but
it appears as if you're passing to bootSem() the covariance matrices for the
observed data rather than the case-by-variable data sets themselves. That's
also what you say you're doing, and it's what the error message says.
Moreover, if you look at the documentation in ?bootSem, you'll is that the
Cov argument isn't a covariance matrix, but "a function to compute the
input covariance or moment matrix; the default is cov. Use cor if the model is
fit to the correlation matrix. The function hetcor in the polycor package will
compute product-moment, polychoric, and polyserial correlations among mixed
continuous and ordinal variables (see the first example below for an
illustration)."
So what is there to bootstrap if bootSem() doesn't have access to the
original data sets? I suppose that one could do a parametric bootstrap of some
sort, but that's not what bootSem() does -- in implements a nonoparametric
bootstrap, which requires the original data.
I hope this helps,
John
-----------------------------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario, Canada
http://socserv.socsci.mcmaster.ca/jfox/
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Chad Danyluck
> Sent: Wednesday, August 27, 2014 12:22 PM
> To: r-help at r-project.org
> Subject: [R] Problems bootstrapping multigroup SEM
>
> Hello,
>
> I am having difficulty resolving an error I receive trying to bootstrap
> a
> multigroup SEM. The error (below) indicates that the model called to
> bootSem doesn't contain matrices. This is true, sort of, because I
> created
> a list of two covariance matrices for the model to call. All of this
> syntax
> works fine (a summary of "MAP.mg.sem" will produce parameter
estimates,
> goodness of fit indices, etc.), however, the bootSem function does not
> run.
> Any ideas on a workaround?
>
> MLM.MAP.Data$IAT.factor <- as.factor(IAT)
> IAT.factor <- MLM.MAP.Data$IAT.factor
> evaluative.MAP.data <- subset(data.frame(IAT.factor, exp.race,
> meditation.experience, years.meditate, repeated.iat, repeated.ERN, age,
> acceptance, awareness, FCz.GNG.150.incor, FCz.GNG.150.cor,
> FCz.stereo.150.incor, FCz.stereo.150.cor, FCz.eval.150.incor,
> FCz.eval.150.cor), IAT==2)
> stereotype.MAP.data <- subset(data.frame(IAT.factor, exp.race,
> meditation.experience, years.meditate, repeated.iat, repeated.ERN, age,
> acceptance, awareness, FCz.GNG.150.incor, FCz.GNG.150.cor,
> FCz.stereo.150.incor, FCz.stereo.150.cor, FCz.eval.150.incor,
> FCz.eval.150.cor), IAT==1)
>
> MAP.stereotype.cov <- cov(na.omit(stereotype.MAP.data[,-1]))
> MAP.evaluative.cov <- cov(na.omit(evaluative.MAP.data[,-1]))
> MAP.cov.list <- list(stereotype=MAP.stereotype.cov,
> evaluative=MAP.evaluative.cov)
>
> #### Specify your MSEM path model: Years Meditating, ERN, IAT####
> MAP.msem.model <- specifyModel()
> years.meditate -> repeated.ERN, path1
> years.meditate -> repeated.iat, path2
> repeated.ERN -> repeated.iat, path3
> age -> repeated.iat, path4
> years.meditate <-> years.meditate, var1
> repeated.ERN <-> repeated.ERN, var2
> age <-> age, var3
> age <-> years.meditate, cov1
> repeated.iat <-> repeated.iat, d1
>
> MAP.mg.mod <- multigroupModel(MAP.msem.model,
groups=c("stereotype",
> "evaluative"))
>
> MAP.mg.sem <- sem(MAP.mg.mod, MAP.cov.list,
> c(nrow(stereotype.MAP.data),
> nrow(evaluative.MAP.data)), group="IAT.factor")
>
> system.time(bootSem(MAP.mg.sem, R=100, MAP.cov.list))
>
> Error in bootSem.msem(MAP.mg.sem.2, MAP.cov.list, R = 100) :
> the model object doesn't contain data matrices
>
> --
> Chad M. Danyluck
> PhD Candidate, Psychology
> University of Toronto
> Lab: http://embodiedsocialcognition.com
>
>
> ?There is nothing either good or bad but thinking makes it so.? -
> William
> Shakespeare
>
> [[alternative HTML version deleted]]
>
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