Report modifications which would improve fit.
This function uses the mechanical modification-indices approach to detect single paths which, if added or dropped, would improve fit.
umxMI( model = NA, matrices = NA, full = TRUE, numInd = NA, typeToShow = "both", decreasing = TRUE )
model |
An |
matrices |
which matrices to test. The default (NA) will test A & S for RAM models |
full |
Change in fit allowing all parameters to move. If FALSE only the parameter under test can move. |
numInd |
How many modifications to report. Use -1 for all. Default (NA) will report all over 6.63 (p = .01) |
typeToShow |
Whether to shown additions or deletions (default = "both") |
decreasing |
How to sort (default = TRUE, decreasing) |
Notes:
Runs much faster with full = FALSE (but this does not allow the model to re-fit around the newly- freed parameter).
Compared to mxMI, this function returns top changes, and also suppresses the run message.
Finally, of course: see the requirements for (legitimate) post-hoc modeling in mxMI()
You are almost certainly doing better science when testing competing models rather than modifying a model to fit.
Other Model Summary and Comparison:
umxCompare()
,
umxEquate()
,
umxReduce()
,
umxSetParameters()
,
umxSummary()
,
umx
require(umx) data(demoOneFactor) manifests = names(demoOneFactor) m1 = umxRAM("One Factor", data = demoOneFactor, type = "cov", umxPath("G", to = manifests), umxPath(var = manifests), umxPath(var = "G", fixedAt = 1) ) # umxMI(m1, full=FALSE)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.