Diagnose problems in a model - this is a work in progress.
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.
umxDiagnose(model, tryHard = FALSE, diagonalizeExpCov = FALSE)
model |
an |
tryHard |
whether I should try and fix it? (defaults to FALSE) |
diagonalizeExpCov |
Whether to diagonalize the ExpCov |
Best diagnostics are:
Observed data variances and means
Expected variances and means
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
helpful messages and perhaps a modified model
Other Teaching and Testing functions:
tmx_show.MxModel(),
umxPower()
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.