Dear David and Ista, I haven't looked at this model carefully, but the fact that the df are 0 suggests that the model is just-identified and therefore necessarily perfectly reproduces the covariances among the observed variables. Removing a parameter would over-identify the model, making possible the computation of the missing fit statistics.
Regards, John On Wed, 7 Mar 2007 18:31:09 +0000 "David Barron" <[EMAIL PROTECTED]> wrote: > It's not the correlation as such that is the problem; it's because > you > only have 10 degrees of freedom "available" with four observed > variables, and you are estimating 10 parameters, which is why you get > a chi square of zero. When you remove any one free parameter (such > as > the correlation), the model becomes identified. > > On 07/03/07, Ista Zahn <[EMAIL PROTECTED]> wrote: > > Hi, > > > > New to both R and SEM, so this may be a very simple question. I am > > trying to run a very simple path analysis using the sem package. > > There are 2 exogenous (FARSCH, LOCUS10) and 2 endogenous (T_ATTENT, > > RMTEST) observed variables in the model. The idea is that T_ATTENT > > mediates the effect of FARSCH and LOCUS10 on RMTEST. The RAM > > specification I used is > > > > FARSCH -> T_ATTENT, y1x1, NA > > LOCUS10 -> T_ATTENT, y1x2, NA > > FARSCH -> RMTEST10, y2x1, NA > > LOCUS10 -> RMTEST10, y2x2, NA > > T_ATTENT -> RMTEST10, y2y1, NA > > FARSCH <-> FARSCH, x1x1, NA > > LOCUS10 <-> LOCUS10, x2x2, NA > > T_ATTENT <-> T_ATTENT, y1y1, NA > > RMTEST10 <-> RMTEST10, y2y2, NA > > LOCUS10 <-> FARSCH, x2x1, NA > > > > This model runs, but using the summary function does not return the > > usual model fit statistics, only the following: > > > > Model Chisquare = 0 Df = 0 Pr(>Chisq) = NA > > Chisquare (null model) = 8526.8 Df = 6 > > Goodness-of-fit index = 1 > > BIC = 0 > > > > If I omit the last line from the RAM specification(i.e., delete > > "LOCUS10 <-> FARSCH, x2x1, NA"), I DO get all the usual statistics: > > > > Model Chisquare = 1303.7 Df = 1 Pr(>Chisq) = 0 > > Chisquare (null model) = 8526.8 Df = 6 > > Goodness-of-fit index = 0.95864 > > Adjusted goodness-of-fit index = 0.58639 > > RMSEA index = 0.30029 90% CI: (NA, NA) > > Bentler-Bonnett NFI = 0.84711 > > Tucker-Lewis NNFI = 0.082726 > > Bentler CFI = 0.84712 > > BIC = 1294.1 > > > > My understanding is the you should always put in the correlation > > between exogenous predictors, but when I do this I don't get fit > > statistics. Can anyone help me understand what is happening here? > > > > Thank you, > > > > Ista > > > > ______________________________________________ > > [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. > > > > > -- > ================================= > David Barron > Said Business School > University of Oxford > Park End Street > Oxford OX1 1HP > > ______________________________________________ > [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. -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario, Canada http://socserv.mcmaster.ca/jfox/ ______________________________________________ [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.
