umxDoC: Build and run a 2-group Direction of Causation twin models.

In umx: Structural Equation Modeling and Twin Modeling in R

Build and run a 2-group Direction of Causation twin models.

Description

Testing causal claims is often difficult due to an inability to conduct experimental randomization of traits and situations to people. When twins are available, even when measured on a single occasion, the pattern of cross-twin cross-trait correlations can (given distinguishable modes of inheritance for the two traits) falsify causal hypotheses.

umxDoC implements a 2-group model to form latent variables for each of two traits, and allows testing whether trait 1 causes trait 2, vice-versa, or even reciprocal causation.

Using latent variables instead of a manifest measure for testing causation, avoids the bias created by differences in measurement error in which the more reliable measure appears to "cause" the less reliable one (Gillespie and Martin, 2005).

The following figure shows how the DoC model appears as a path diagram (for two latent variables X and Y, each with three indicators). Note: For pedagogical reasons, only the model for 1 twin is shown, and only one DoC pathway drawn.

Usage

umxDoC(
  name = "DoC",
  var1Indicators,
  var2Indicators,
  mzData = NULL,
  dzData = NULL,
  sep = "_T",
  causal = TRUE,
  autoRun = getOption("umx_auto_run"),
  intervals = FALSE,
  tryHard = c("no", "yes", "ordinal", "search"),
  optimizer = NULL,
  data = NULL,
  zyg = "zygosity"
)

Arguments

name	The name of the model (defaults to "DOC").
var1Indicators	variables defining latent trait 1
var2Indicators	variables defining latent trait 2
mzData	The MZ dataframe
dzData	The DZ dataframe
sep	The separator in twin variable names, default = "_T", e.g. "dep_T1".
causal	whether to add the causal paths (default TRUE)
autoRun	Whether to run the model (default), or just to create it and return without running.
intervals	Whether to run mxCI confidence intervals (default = FALSE)
tryHard	Default ('no') uses normal mxRun. "yes" uses mxTryHard. Other options: "ordinal", "search"
optimizer	Optionally set the optimizer (default NULL does nothing).
data	= NULL If building the MZ and DZ datasets internally from a complete data set.
zyg	= "zygosity" (for the data= method of using this function)

Value

• mxModel() of subclass MxModelDoC

References

• N.A. Gillespie and N.G. Martin (2005). Direction of Causation Models. In Encyclopedia of Statistics in Behavioral Science, 1. 496–499. Eds. Brian S. Everitt & David C. Howell.

• McGue, M., Osler, M., & Christensen, K. (2010). Causal Inference and Observational Research: The Utility of Twins. Perspectives on Psychological Science, 5, 546-556. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1745691610383511")}

• Rasmussen, S. H. R., Ludeke, S., & Hjelmborg, J. V. B. (2019). A major limitation of the direction of causation model: non-shared environmental confounding. Twin Res Hum Genet, 22, 1-13. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/thg.2018.67")}

See Also

• umxDiscTwin()

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

Examples

## Not run: 

# ========================
# = Does Rain cause Mud? =
# ========================

# ================
# = 1. Load Data =
# ================
data(docData)
docData = umx_scale_wide_twin_data(c(var1, var2), docData, sep= "_T")
mzData  = subset(docData, zygosity %in% c("MZFF", "MZMM"))
dzData  = subset(docData, zygosity %in% c("DZFF", "DZMM"))

# =======================================
# = 2. Define manifests for var 1 and 2 =
# =======================================
var1 = paste0("varA", 1:3)
var2 = paste0("varB", 1:3)

# =======================================================
# = 3. Make the non-causal (Cholesky) and causal models =
# =======================================================
Chol = umxDoC(var1= var1, var2= var2, mzData= mzData, dzData= dzData, causal= FALSE)
# nb: DoC initially has causal paths fixed @0
DoC  = umxDoC(var1= var1, var2= var2, mzData= mzData, dzData= dzData, causal= TRUE)
a2b   = umxModify(DoC, "a2b", free = TRUE, name = "a2b"); summary(a2b)
b2a   = umxModify(DoC, "b2a", free = TRUE, name = "b2a"); summary(b2a)
Recip = umxModify(DoC, c("a2b", "b2a"), free = TRUE, name = "Recip"); summary(Recip)

# Compare fits
umxCompare(Chol, c(a2b, b2a, Recip))

# ==========================================
# = Alternative call with data in one file =
# ==========================================
data(docData)
docData = umx_scale_wide_twin_data(c(var1, var2), docData, sep= "_T")
DoC = umxDoC(var1= paste0("varA", 1:3), var2= paste0("varB", 1:3),
	  mzData= c("MZFF", "MZMM"), dzData= c("DZFF", "DZMM"), data = docData
)

## End(Not run)

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