umxACEcov: Run a Cholesky with covariates that are random (in the...

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

Run a Cholesky with covariates that are random (in the expected covariance matrix)

Description

Often, researchers include covariates in 2-group Cholesky umxACE() twin models. The umxACEcov 'random' option models the covariates in the expected covariance matrix, thus allowing all data to be preserved. The downside is that this method has a strong assumption of multivariate normality. Covariates like age, which are perfectly correlated in twins cannot be used. Covariates like sex, which are ordinal, violate the normality assumption. Binary and ordinal covariates like sex also violate the normality assumption. Which is most of the use cases :-(.

Usage

umxACEcov(
  name = "ACEcov",
  selDVs,
  selCovs,
  dzData,
  mzData,
  sep = NULL,
  type = c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS"),
  allContinuousMethod = c("cumulants", "marginals"),
  dzAr = 0.5,
  dzCr = 1,
  addStd = TRUE,
  addCI = TRUE,
  boundDiag = 0,
  equateMeans = TRUE,
  bVector = FALSE,
  autoRun = getOption("umx_auto_run"),
  tryHard = c("no", "yes", "ordinal", "search"),
  optimizer = NULL
)

Arguments

name	The name of the model (defaults to"ACE").
selDVs	The variables to include from the data (do not include sep).
selCovs	The covariates to include from the data (do not include sep).
dzData	The DZ dataframe.
mzData	The MZ dataframe.
sep	Separator text between basename for twin variable names. Often "_T". Used to expand selDVs into full column names, i.e., "dep" –> c("dep_T1", "dep_T2").
type	Analysis method one of c("Auto", "FIML", "cov", "cor", "WLS", "DWLS", "ULS")
allContinuousMethod	"cumulants" or "marginals". Used in all-continuous WLS data to determine if a means model needed.
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).
addStd	Whether to add the algebras to compute a std model (defaults to TRUE).
addCI	Whether to add intervals to compute CIs (defaults to TRUE).
boundDiag	= Whether to bound the diagonal of the a, c, and e matrices.
equateMeans	Whether to equate the means across twins (defaults to TRUE).
bVector	Whether to compute row-wise likelihoods (defaults to FALSE).
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"
optimizer	optionally set the optimizer. Default (NULL) does nothing.

Details

The following figure shows how the ACE model with random covariates appears as a path diagram:

Value

• OpenMx::mxModel() of subclass mxModel.ACEcov

References

• Neale, M. C., & Martin, N. G. (1989). The effects of age, sex, and genotype on self-report drunkenness following a challenge dose of alcohol. Behavior Genetics, 19, 63-78. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/BF01065884")}.

• Schwabe, I., Boomsma, D. I., Zeeuw, E. L., & Berg, S. M. (2015). A New Approach to Handle Missing Covariate Data in Twin Research : With an Application to Educational Achievement Data. Behavior Genetics, 46, 583-95. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10519-015-9771-1")}.

See Also

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

Examples

## Not run: 
# ============================================
# = BMI, can't use Age as a random covariate =
# ============================================
require(umx)
data(twinData)
# Replicate age to age1 & age2
twinData$age1 = twinData$age2 = twinData$age
mzData = subset(twinData, zygosity == "MZFF")
dzData = subset(twinData, zygosity == "DZFF")

# =====================================================================
# = Trying to use identical var (like age) as a random cov is ILLEGAL =
# =====================================================================
m1 = umxACEcov(selDVs = "bmi", selCovs = "age", dzData = dzData, mzData = mzData, sep = "")

# ========================================================
# = Use an lm-based age-residualisation approach instead =
# ========================================================

resid_data = umx_residualize("bmi", "age", suffixes = 1:2, twinData)
mzData = subset(resid_data, zygosity == "MZFF")
dzData = subset(resid_data, zygosity == "DZFF")
m2     = umxACE("resid", selDVs = "bmi", dzData = dzData, mzData = mzData, sep = "")

# Univariate BMI without covariate of age for comparison
mzData = subset(twinData, zygosity == "MZFF")
dzData = subset(twinData, zygosity == "DZFF")
m3 = umxACE("raw_bmi", selDVs = "bmi", dzData = dzData, mzData = mzData, sep = "")

# ===========================================================================
# = A bivariate example (need a dataset with a VIABLE COVARIATE to do this) =
# ===========================================================================
selDVs  = "wt" # Set the DVs
selCovs = "ht" # Set the COV
selVars = umx_paste_names(selDVs, covNames = selCovs, sep = "", sep = 1:2)
mzData = subset(twinData, zygosity == "MZFF")
dzData = subset(twinData, zygosity == "DZFF")
m1 = umxACEcov(selDVs = selDVs, selCovs = selCovs,
   dzData = dzData, mzData = mzData, sep = "", autoRun = TRUE
)

## End(Not run)

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