umxReduceACE | R Documentation |
This function can perform model reduction on umxACE()
models,
testing dropping A and C, as well as an ADE or ACE model, displaying the results
in a table, and returning the best model.
umxReduceACE(
model,
report = c("markdown", "inline", "html", "report"),
intervals = TRUE,
testD = TRUE,
baseFileName = "tmp",
tryHard = c("yes", "no", "ordinal", "search"),
silent = FALSE,
digits = 2,
...
)
model |
an ACE or ADE |
report |
How to report the results. "html" = open in browser |
intervals |
Recompute CIs (if any included) on the best model (default = TRUE) |
testD |
Whether to test ADE and DE models (TRUE) |
baseFileName |
(optional) custom filename for html output (defaults to "tmp") |
tryHard |
(default = "yes") |
silent |
Don't print the ACE models (default = FALSE) |
digits |
rounding in printout (default = 2) |
... |
Other parameters to control model summary |
It is designed for testing univariate models. You can offer up either the ACE or ADE base model.
Suggestions for more sophisticated automation welcomed!
Best fitting model
Wagenmakers, E.J., & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin and Review, 11, 192-196. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/BF03206482")}
umxReduceGxE()
, umxReduce()
Other Twin Modeling Functions:
power.ACE.test()
,
umx
,
umxACE()
,
umxACEcov()
,
umxACEv()
,
umxCP()
,
umxDiffMZ()
,
umxDiscTwin()
,
umxDoC()
,
umxDoCp()
,
umxGxE()
,
umxGxE_window()
,
umxGxEbiv()
,
umxIP()
,
umxMRDoC()
,
umxReduce()
,
umxReduceGxE()
,
umxRotate.MxModelCP()
,
umxSexLim()
,
umxSimplex()
,
umxSummarizeTwinData()
,
umxSummaryACE()
,
umxSummaryACEv()
,
umxSummaryDoC()
,
umxSummaryGxEbiv()
,
umxSummarySexLim()
,
umxSummarySimplex()
,
umxTwinMaker()
## Not run:
data(twinData)
mzData = subset(twinData, zygosity == "MZFF")
dzData = subset(twinData, zygosity == "DZFF")
m1 = umxACE(selDVs = "bmi", dzData = dzData, mzData = mzData, sep = "")
# ===========================================================================
# = Table of parameters + fit comparisons, ready too copy to word processor =
# ===========================================================================
umxReduce(m1, silent=TRUE, digits=2, repo="h")
# ==========================================
# = Function captures the preferred model =
# ==========================================
m2 = umxReduce(m1)
umxSummary(m2)
# works for ADE input also
m1 = umxACE(selDVs = "bmi", dzData = dzData, mzData = mzData, sep = "", dzCr = .25)
## End(Not run)
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