umxSuperModel: Make a multi-group model

In tbates/umx: Structural Equation Modeling and Twin Modeling in R

Make a multi-group model

Description

umxSuperModel takes 1 or more models and wraps them in a supermodel with a OpenMx::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

• OpenMx::mxModel()

References

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

See Also

• OpenMx::mxFitFunctionMultigroup(), umxRAM()

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

Examples

## 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)

## End(Not run)

tbates/umx documentation built on Dec. 14, 2024, 11:28 a.m.