Hi all,
I'm writing a simple latent change score demo using mxPath, and found something interesting. I believe we've talked about default means models before, but I don't recall.
In this model, two occasions of data (wisc1 and wisc6 below) are restated as the first occasion (wisc1) and a change or difference score (diff). As such, the second observation (wisc6) is fully explained, and has no intercept and no residual. By default, specifying 'type="RAM"' assigns freely estimated manifest means, so the code below actually has three means for two manifest variables. The easy thing is just to add an extra path function, specifying a fixed intercept of zero for wisc6. I'm asking a design question, however: should I have to override a default and tell OpenMx not to give wisc6 a mean?
x <- matrix(c(40.658, 50.686, 50.686, 108.014), nrow=2) y <- c(18.781, 47.341) <code> example <- mxModel("My Title", mxData(x, type="cov", means=y, numObs=204), type="RAM", manifestVars=c("wisc1", "wisc6"), latentVars="diff", mxPath(c("wisc1", "diff"), "wisc6", TRUE, 1, FALSE, 1, NA), mxPath(c("wisc1", "diff"), c("wisc1", "diff"), FALSE, 2, TRUE, 45, c("v_1", "v_d")), mxPath("wisc1", "diff", FALSE, 2, TRUE, 10, "cov_1d"), mxPath("one", c("wisc1", "diff"), FALSE, 1, TRUE, 1, c("mean1", "mean_diff")) )