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library(OpenMx)
data(demoOneFactor)
manifests <- names(demoOneFactor)
latents <- c("G")
template <- mxModel(
"template", type="RAM",
manifestVars = manifests,
latentVars = latents,
mxPath(from=latents, to=manifests, values=rnorm(length(manifests))),
mxPath(from=manifests, arrows=2, values=rlnorm(length(manifests))),
mxPath(from=latents, arrows=2, free=FALSE, values=1.0),
mxPath(from = 'one', to = manifests, values=rnorm(length(manifests))))
factorRaw <- mxModel(template, name="OneFactorRaw",
mxData(demoOneFactor, type="raw"))
factorCov <- mxModel(template, name="OneFactorCov",
mxData(observed=cov(demoOneFactor), means=colMeans(demoOneFactor),
type="cov", numObs=nrow(demoOneFactor)))
plan <- mxComputeSequence(list(
mxComputeOnce('fitfunction', 'fit'),
mxComputeNumericDeriv(checkGradient=FALSE, hessian=FALSE, iterations=2),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
factorRaw <- mxRun(mxModel(factorRaw, plan))
factorCov <- mxRun(mxModel(factorCov, plan))
omxCheckCloseEnough(factorRaw$output$fit, factorCov$output$fit + prod(dim(demoOneFactor))*log(2*pi), 1e-10)
omxCheckCloseEnough(factorRaw$output$gradient, factorCov$output$gradient, 1e-5)
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