Dear Vera, If the smoker/non-smoker variable is exogenous (as seems to be implied by your calling it a "predictor") then you can simply create a 0/1 dummy regressor for it and calculate covariances in the usual manner. The coefficient for the variable would have the usual interpretation for a dummy regressor. I wouldn't calculate correlations, as opposed to covariances, since this would muddy the interpretation. A also wouldn't use Spearman or Kendall correlations.
On the other hand, if smoker/non-smoker is endogenous, then you could use biserial/tetrachoric correlations: see the hector() function in the polycor package. There's an example (though with polytomous rather than dichotomous variables) in ?boot.sem and in a paper on the sem package that appeared in Structural Equation Modeling, which is available at <http://socserv.mcmaster.ca/jfox/Misc/sem/SEM-paper.pdf>. I hope this helps, John -------------------------------- John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of Lila86 > Sent: April-01-08 5:32 PM > To: [email protected] > Subject: [R] SEM with a categorical predictor variable > > > Hi, > > we are trying to do structural equation modelling on R. However, one of > our > predictor variables is categorical (smoker/nonsmoker). Now, if we want > to > run the sem() command (from the sem library), we need to specify a > covariance matrix (cov). However, Pearson's correlation does not work > on the > dichotomous variable, so instead we produced a covariance matrix using > the > Spearman's (or Kendalls) correlation method, which works. > > Running the sem() command on our model using that covariance matrix > works > fine, but I am not sure if it was okay to make the covariance matrix > using > Spearman or Kendall. Can we interpret the regression coefficients that > we > find in summary(sem) just as if we had used Pearsons correlation in the > covariance matrix? Or is there any other way to define a SEM including > categorical variables without using a covariance matrix? > > I appreciate every help. Thank you very much, > Vera > > > -- > View this message in context: http://www.nabble.com/SEM-with-a- > categorical-predictor-variable-tp16425959p16425959.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > [email protected] 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. ______________________________________________ [email protected] 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.
