umxGxE: umxGxE: Implements ACE models with moderation of paths, e.g....

In umx: Structural Equation Modeling and Twin Modeling in R

umxGxE: Implements ACE models with moderation of paths, e.g. by SES.

Description

Make a 2-group GxE (moderated ACE) model (Purcell, 2002). GxE interaction studies test the hypothesis that the strength of genetic (or environmental) influence varies parametrically (usually linear effects on path estimates) across levels of environment. umxGxE allows detecting, testing, and visualizing G xE (or C or E x E) interaction forms.

Usage

umxGxE(
  name = "G_by_E",
  selDVs,
  selDefs,
  dzData,
  mzData,
  sep = NULL,
  data = NULL,
  zyg = "zygosity",
  digits = 3,
  lboundACE = NA,
  lboundM = NA,
  dropMissingDef = TRUE,
  dzAr = 0.5,
  dzCr = 1,
  autoRun = getOption("umx_auto_run"),
  tryHard = c("no", "yes", "ordinal", "search"),
  optimizer = NULL
)

Arguments

name	The name of the model (default= "G_by_E")
selDVs	The dependent variable (e.g. "IQ")
selDefs	The definition variable (e.g. "SES")
dzData	The DZ dataframe containing the Twin 1 and Twin 2 DV and moderator (4 columns)
mzData	The MZ dataframe containing the Twin 1 and Twin 2 DV and moderator (4 columns)
sep	How to expand selDVs into full names, i.e., "_T" makes "var" -> "var_T1" and "var_T2"
data	If provided, dzData and mzData are treated as valid levels of zyg to select() data sets (default = NULL)
zyg	If data provided, this column is used to select rows by zygosity (Default = "zygosity")
digits	Rounding precision for tables (default 3)
lboundACE	If not NA, then lbound the main effects at this value (default = NA, can help to set this to 0)
lboundM	If not NA, then lbound the moderator effects at this value (default = NA, can help to set this to 0)
dropMissingDef	Whether to automatically drop missing def var rows for the user (default = TRUE). You get a polite note.
dzAr	The DZ genetic correlation (defaults to .5, vary to examine assortative mating).
dzCr	The DZ "C" correlation (defaults to 1: set to .25 to make an ADE model).
autoRun	Optionally run the model (default), or just to create it and return without running.
tryHard	Optionally tryHard to get the model to converge (Default = 'no'). "yes" uses mxTryHard. Other options: "ordinal", "search".
optimizer	Optionally set the optimizer (default NULL does nothing)

Details

The following figure the GxE model as a path diagram:

Value

• GxE mxModel()

References

• Purcell, S. (2002). Variance components models for gene-environment interaction in twin analysis. Twin Research, 6, 554-571. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1375/twin.5.6.554")}

See Also

umxGxE_window(), umxReduce(), umxSummary()

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

Examples

## Not run: 
require(umx)
data(twinData) 
twinData$age1 = twinData$age2 = twinData$age
selDVs  = "bmi"
selDefs = "age"
mzData  = subset(twinData, zygosity == "MZFF")[1:100,]
dzData  = subset(twinData, zygosity == "DZFF")[1:100,]
m1 = umxGxE(selDVs= "bmi", selDefs= "age", sep= "", dzData= dzData, mzData= mzData, tryHard= "yes")

# Select the data on the fly with data= and zygosity levels
m1 = umxGxE(selDVs= "bmi", selDefs= "age", sep="", dzData= "DZFF", mzData= "MZFF", data= twinData)

# ===============================================================
# = example with Twins having different values of the moderator =
# ===============================================================

twinData$age1 = twinData$age2 = twinData$age
tmp = twinData
tmp$age2 = tmp$age2 +rnorm(n=length(tmp$age2))
selDVs  = "bmi"
selDefs = "age"
mzData = subset(tmp, zygosity == "MZFF")
dzData = subset(tmp, zygosity == "DZFF")
m1 = umxGxE(selDVs= "bmi", selDefs= "age", sep= "", dzData= dzData, mzData= mzData, tryHard= "yes")

# ====================================
# = Controlling output of umxSummary =
# ====================================
umxSummaryGxE(m1)
umxSummary(m1, location = "topright")
umxSummary(m1, separateGraphs = TRUE)

m2 = umxModify(m1, regex = "am_.*", comparison = TRUE, tryHard = "yes")

# umxReduce knows how to test all relevant hypotheses for GxE models,
# reporting these in a nice table.
umxReduce(m1)

## End(Not run)

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