Dear Gus,
If the nominal variables are exogenous then you need not use polychoric and
polyserial correlations. (Indeed, if they are polytomous and unordered, then
it would be inappropriate to do so.) Simply use dummy exogenous variables,
as you would in a dummy regression.
You could use a one-sided formula, along with model.matrix() and cov() to
compute the input covariance matrix, as in the following example, using the
Prestige data frame in the car package:
> S <- cov(model.matrix(~ type + income + education, data=Prestige))[-1,-1]
> S
typeprof typewc income education
typeprof 0.21849358 -0.07500526 1157.0972 1.051153e+00
typewc -0.07500526 0.18146434 -447.3270 5.373869e-02
income 1157.09720177 -447.32695140 17877104.7629 6.672664e+03
education 1.05115296 0.05373869 6672.6640 7.556652e+00
(Note the [-1,-1] to get rid of the row/column for the invariant constant
regressor; alternatively, you could put -1 in the model formula, but then
you'd get 3 dummy regressors for the three categories of type.)
An alternative would be to fit a model with a constant, in which case you
could use raw.moments() to compute the moment matrix, as in:
> S <- raw.moments(model.matrix(~ type + income + education, data=Prestige))
> S
Raw Moments
(Intercept) typeprof typewc income education
(Intercept) 1.0000000 0.3163265 0.2346939 6938.857 10.795102
typeprof 0.3163265 0.3163265 0.0000000 3340.235 4.455204
typewc 0.2346939 0.0000000 0.2346939 1185.745 2.586735
income 6938.8571429 3340.2346939 1185.7448980 65842423.776 81510.246531
education 10.7951020 4.4552041 2.5867347 81510.247 124.013771
N = 98
If by "path analysis" you mean a recursive model, then, since there are no
latent variables, you might as well use lm() to fit the model equation by
equation. If the model is nonrecursive, you could use tsls() in the sem
package to fit the model equation by equation, or one of estimators in the
systemfit package.
Finally, "numeric" means quantitative, which may or may not literally be
continuous. A nominal variable is represented by a factor in R, but remember
that the functions in polycor require that the levels be ordered.
I hope this helps,
John
------------------------------
John Fox, Professor
Department of Sociology
McMaster University
Hamilton, Ontario, Canada
web: socserv.mcmaster.ca/jfox
> -----Original Message-----
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On
> Behalf Of Gus Jespersen
> Sent: May-29-08 1:02 PM
> To: r-help@r-project.org
> Subject: [R] appropriate covariance matrix for multiple nominal exogenous
and
> multiple continuous endogenous variables in SEM
>
> Hi,
> I would like to use the sem package to perform a path analysis (no
> latent variables) with a mixture of 2 nominal exogenous, 1 continuous
> exogenous, and 4 continuous endogenous variables. I seek advice as to
> how to calculate the appropriate covariance matrix for use with the sem
> package.
>
> I have read through the polycor package, and am confused as to the use
> of "numeric" for the hetcor function. Is this used synonymous with a
> continuous variable, or perhaps a nominal variable?
>
> Thanks,
> Gus
>
> ______________________________________________
> 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@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.
