umxDiagnose {umx}

Diagnose problems in a model - this is a work in progress.

Description

The goal of this function WILL BE (not currently functional) to diagnose problems in a model and return suggestions to the user. It is a work in progress, and of no use as yet.

Usage

umxDiagnose(model, tryHard = FALSE, diagonalizeExpCov = FALSE)

Arguments

model

an mxModel() to diagnose

tryHard

whether I should try and fix it? (defaults to FALSE)

diagonalizeExpCov

Whether to diagonalize the ExpCov

Details

Best diagnostics are:

1. Observed data variances and means

2. Expected variances and means

3. Difference of these?

Try * diagonalizeExpCov diagonal * umx_is_ordered()

more tricky - we should really report the variances and the standardized thresholds.

The guidance would be to try starting with unit variances and thresholds that are within +/- 2 SD of the mean. bivariate outliers %p option

Value

• helpful messages and perhaps a modified model

References

• https://tbates.github.io, https://github.com/tbates/umx

See Also

Other Teaching and Testing functions: tmx_show.MxModel(), umxPower()

Examples

## Not run: 
require(umx)
data(demoOneFactor)
manifests = names(demoOneFactor)

m1 = umxRAM("OneFactor", data = demoOneFactor, type= "cov",
	umxPath("G", to = manifests),
	umxPath(var = manifests),
	umxPath(var = "G", fixedAt = 1)
)
m1 = mxRun(m1)
umxSummary(m1, std = TRUE)
umxDiagnose(m1)

## End(Not run)