umx (version 4.9.0)

umxSuperModel: Make a multi-group model

Description

umxSuperModel takes 1 or more models and wraps them in a supermodel with a mxFitFunctionMultigroup() fit function that minimizes the sum of the fits of the sub-models.

note: Any duplicate model-names are renamed to be unique by suffixing _1 etc.

Usage

umxSuperModel(
  name = "super",
  ...,
  autoRun = getOption("umx_auto_run"),
  tryHard = c("no", "yes", "ordinal", "search"),
  std = FALSE
)

Arguments

name

The name for the container model (default = 'super')

...

Models forming the multiple groups contained in the supermodel.

autoRun

Whether to run the model (default), or just to create it and return without running.

tryHard

Default ('no') uses normal mxRun. "yes" uses mxTryHard. Other options: "ordinal", "search"

std

Show standardized parameters, raw (default), or just the fit indices (null)

Value

• mxModel()

References

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

See Also

• mxFitFunctionMultigroup(), umxRAM()

Other Core Model Building Functions: umxMatrix(), umxModify(), umxPath(), umxRAM(), umx

Examples

# NOT RUN {
# }
# NOT RUN {
library(umx)
# Create two sets of data in which X & Y correlate ~ .4 in both datasets.
manifests = c("x", "y")
tmp = umx_make_TwinData(nMZpairs = 100, nDZpairs = 150, 
		AA = 0, CC = .4, EE = .6, varNames = manifests)

# Group 1
grp1   = tmp[tmp$zygosity == "MZ", manifests]
g1Data = mxData(cov(grp1), type = "cov", numObs = nrow(grp1), means=umx_means(grp1))

# Group 2
grp2   = tmp[tmp$zygosity == "DZ", manifests]
g2Data = mxData(cov(grp2), type = "cov", numObs = nrow(grp2), means=umx_means(grp2))


# Model 1 (could add autoRun = FALSE if you don't want to run this as it is being built)
m1 = umxRAM("m1", data = g1Data,
	umxPath("x", to = "y", labels = "beta"),
	umxPath(var = manifests, labels = c("Var_x", "Resid_y_grp1")),
	umxPath(means = manifests, labels = c("Mean_x", "Mean_y"))
)

# Model 2
m2 = umxRAM("m2", data = g2Data,
	umxPath("x", to = "y", labels = "beta"),
	umxPath(var = manifests, labels=c("Var_x", "Resid_y_grp2")),
	umxPath(means = manifests, labels=c("Mean_x", "Mean_y"))
)

# Place m1 and m2 into a supermodel, and autoRun it
# NOTE: umxSummary is only semi-smart/certain enough to compute saturated models etc
# and report multiple groups correctly.

m3 = umxSuperModel('top', m1, m2)

umxSummary(m3, std= TRUE)

# |name         | Std.Estimate| Std.SE|CI                |
# |:------------|------------:|------:|:-----------------|
# |beta         |         0.51|   0.05|0.51 [0.41, 0.61] |
# |Var_x        |         1.00|   0.00|1 [1, 1]          |
# |Resid_y_grp1 |         0.74|   0.05|0.74 [0.64, 0.84] |
# |beta         |         0.50|   0.05|0.5 [0.41, 0.6]   |
# |Var_x        |         1.00|   0.00|1 [1, 1]          |
# |Resid_y_grp2 |         0.75|   0.05|0.75 [0.65, 0.84] |

summary(m3)

# ====================================
# = Test models with duplicate names =
# ====================================
data(GFF)
mzData = subset(GFF, zyg_2grp == "MZ")
dzData = subset(GFF, zyg_2grp == "DZ")
selDVs = c("gff", "fc", "qol")
m1 = umxCP(selDVs= selDVs, nFac= 1, dzData= dzData, mzData= mzData, sep= "_T", autoRun= TRUE)
m2 = mxRename(m1, "CP2")
umxModelNames(m1) # "top" "MZ" "DZ"
umxModelNames(m2) # "top" "MZ" "DZ"
super = umxSuperModel("myModel", m1, m2, autoRun = TRUE)
umxModelNames(super)
# }