umxTwinMaker: Make a twin model from the model describing just one person

In umx: Structural Equation Modeling and Twin Modeling in R

Make a twin model from the model describing just one person

Description

xmu_path2twin takes a collection of paths describing the model for 1 person and returns a completed twin model. This consists of a umxSuperModel() containing MZ and DZ umxRAM() models.

Pass into umxTwinMaker:

1. A list of paths making up the twin 1 model

2. In t1_t2links, a vector describing the component relationships connecting twin 1 to twin 2 (The default here is 1 and .5 for the a, and, for c and e are 1 and 0 in both groups, respectively.

Details

Some rules. All labels are expanded with a twin suffix: so "var1" -> "var1_T1" etc. so you provide the person-model using just the base name (and tell umxTwinMaker() how to expand it by providing a separator string).

Rule 2: The latent a, c, and e latent variables must be labelled to match the base name given in t1_t2links. To avoid clashes, variables must not match the numbered variables in t1_t2links - by default names like "a1" are reserved for ace.

Usage

umxTwinMaker(
  name = "m1",
  paths,
  t1_t2links = list(a = c(1, 0.5), c = c(1, 1), e = c(0, 0)),
  mzData = NULL,
  dzData = NULL,
  sep = "_T",
  autoRun = getOption("umx_auto_run")
)

Arguments

name	The name for the resulting umxSuperModel() (Default "m1").
paths	A vector of umxPath()s describing one person.
t1_t2links	base name (and values) of paths that covary between T1 and T2. Default: c('a'=c(1,.5), 'c'=c(1,1), 'e'=c(0,0))
mzData	Data for MZ twins.
dzData	Data for DZ twins.
sep	The separator used to create twin 1 and 2 names (Default "_T")
autoRun	Whether to run the supermodel before returning it.

Value

• umxSuperModel()

References

• tutorials, github

See Also

• umxRAM(), umxSuperModel(), umxPath()

Other Twin Modeling Functions: power.ACE.test(), umxACEcov(), umxACEv(), umxACE(), umxCP(), umxDiffMZ(), umxDiscTwin(), umxDoCp(), umxDoC(), umxGxE_window(), umxGxEbiv(), umxGxE(), umxIP(), umxMRDoC(), umxReduceACE(), umxReduceGxE(), umxReduce(), umxRotate.MxModelCP(), umxSexLim(), umxSimplex(), umxSummarizeTwinData(), umxSummaryACEv(), umxSummaryACE(), umxSummaryDoC(), umxSummaryGxEbiv(), umxSummarySexLim(), umxSummarySimplex(), umx

Examples

## Not run: 
# We'll make some ACE models, but first, let's clean up the twinData 
# set for analysis
# 1. Add a separator to the twin variable names (with sep = "_T")
# 2. Scale the data so it's easier for the optimizer.
data(twinData)
tmp = umx_make_twin_data_nice(data=twinData, sep="", zygosity="zygosity", numbering=1:2)
tmp = umx_scale_wide_twin_data(varsToScale= c("wt", "ht"), sep= "_T", data= tmp)
mzData = subset(tmp, zygosity %in%  c("MZFF", "MZMM"))
dzData = subset(tmp, zygosity %in%  c("DZFF", "DZMM"))

# ==========================
# = Make an ACE twin model =
# ==========================
# 1. Define paths for *one* person:
paths = c(
   umxPath(v1m0 = c("a1", 'c1', "e1")),
   umxPath(means = c("wt")),
   umxPath(c("a1", 'c1', "e1"), to = "wt", values=.2)
)
# 2. Make a twin model from the paths for one person
m1 = umxTwinMaker("test", paths, mzData = mzData, dzData= dzData)
plot(m1, std= TRUE, means= FALSE)

# 3. comparison with umxACE...
m2 = umxACE(selDVs="wt", mzData = mzData, dzData=dzData, sep="_T")

# =====================
# = Bivariate example =
# =====================
latents = paste0(rep(c("a", "c", "e"), each = 2), 1:2)
biv = c(
umxPath(v1m0 = latents),
umxPath(mean = c("wt", "ht")),
umxPath(fromEach = c("a1", 'c1', "e1"), to = c("ht", "wt")),
umxPath(c("a2", 'c2', "e2"), to = "wt")
)
tmp= umxTwinMaker(paths= biv, mzData = mzData, dzData= dzData)
plot(tmp, means=FALSE)

# How to use latents other than a, c, and e: define in t1_t2links
paths = c(
umxPath(v1m0 = c("as1", 'c1', "e1")),
umxPath(means = c("wt")),
umxPath(c("as1", 'c1', "e1"), to = "wt", values=.2)
)
m1 = umxTwinMaker("test", paths, mzData = mzData, dzData= dzData, 
	t1_t2links = list('as'=c(1, .5), 'c'=c(1, 1), 'e'=c(0, 0))
)


## End(Not run)

umx documentation built on May 29, 2024, 5:40 a.m.