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Hi Rob,I'm not sure what you're trying to do makes sense. Given that visual is a latent variable, correlating it to another item for another factor is odd. If you wanted to specify them as related, you probably would just add a cross-loading so that x4 loads on visual as well. In addition, in the syntax you posted, VISUAL wouldn't have a residual because there isn't anything predicting it. Typically one would be attempting to correlate the uniquenesses/residuals of the items in a measurement model.
On Tue, Jul 9, 2019 at 3:07 PM Rob R <redf...@gmail.com> wrote:
Hi all,--I am looking for any advice on how to include correlated residuals in a model, such that the residual variance of a latent variable is correlated with one of an observed measure.I am aware that the double tilda (i.e. ~~) can be used to include residual covariances in a model. However, when I attempt to correlate a latent factor from the model with an observed measure that loads on to a different latent factor, I encounter an error.Here is some sample syntax, and the exact error message that is received when running the code:HS.model <- ' visual =~ x1 + x2 + x3textual =~ x4 + x5 + x6speed =~ x7 + x8 + x9visual ~~ x4 'fit <- cfa(HS.model, data=HolzingerSwineford1939)Error in lav_model(lavpartable = lavpartable, lavoptions = lavoptions, :lavaan ERROR: parameter is not defined: visual ~~ x4If anybody is able to point out or provide advice on what syntax can be used to add the correlation between residual variances of a latent factor and an observed variable, your input would be very highly appreciated.Thanks,Rob
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