umx (version 1.9.1)

umxACEcov: Run a Cholesky with covariates, either fixed (def var in the means) or random (in the expected covariance matrix)

Description

Often, researchers include covariates in 2-group Cholesky umxACE twin models. A simple method is to regressing covariates from the data (see umx_residualize). A second method (supported in umxACEcov) is to include the covariates in the means model. This is the 'fixed' option for covariates models them in the mean as definition variables. On the plus side, there is no distributional assumption for this method. A downside of this approach is that all covariates must be non-NA, thus dropping any rows where one or more covariates are missing. This is wasteful of data.

Usage

umxACEcov(name = "ACEcov", selDVs, selCovs, dzData, mzData, sep = NULL,
  dzAr = 0.5, dzCr = 1, addStd = TRUE, addCI = TRUE, boundDiag = 0,
  equateMeans = TRUE, bVector = FALSE, thresholds = c("deviationBased",
  "left_censored"), autoRun = getOption("umx_auto_run"), suffix = NULL,
  optimizer = NULL)

Arguments

name

The name of the model (defaults to"ACE").

selDVs

The variables to include from the data (do not include suffixes).

selCovs

The covariates to include from the data (do not include suffixes).

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").

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).

thresholds

How to implement ordinal thresholds: c("deviationBased", "left_censored").

autoRun

Whether to run the model and return it, or just return it.

suffix

synonym for 'sep' (see above).

optimizer

optionally set the optimizer. Default (NULL) does nothing.

Value

- mxModel of subclass mxModel.ACEcov

Details

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.

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

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. doi:https://doi.org/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. doi:https://doi.org/10.1007/s10519-015-9771-1.

See Also

Other Twin Modeling Functions: plot.MxModel, umxACESexLim, umxACE_cov_fixed, umxACEv, umxACE, umxCF_SexLim, umxCP, umxGxE_window, umxGxE, umxIP, umxPlotACEcov, umxPlotCP, umxPlotGxE, umxPlotIP, umxReduceACE, umxSummaryACEcov, umxSummaryACEv, umxSummaryACE, umxSummaryCP, umxSummaryGxE, umxSummaryIP, umx_long2wide, umx_wide2long, umx, xmu_twin_check

Examples

# NOT RUN {
require(umx)
data(twinData)
# Replicate age to age1 & age2
twinData$age1 = twinData$age2 = twinData$age
# 80 rows so example runs fast for CRAN
mzData = subset(twinData, zygosity == "MZFF")[1:80, ]
dzData = subset(twinData, zygosity == "DZFF")[1:80, ]
# }
# NOT RUN {
mzData = subset(twinData, zygosity == "MZFF")
dzData = subset(twinData, zygosity == "DZFF")
# }
# NOT RUN {
# =====================================================================
# = Trying to use identical var (like age) as a random cov is ILLEGAL =
# =====================================================================
# }
# NOT RUN {
m1 = umxACEcov(selDVs = "bmi", selCovs = "age", dzData = dzData, mzData = mzData, sep = "")
# }
# NOT RUN {
# ========================================================
# = 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, suffix = "")

# 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, suffix = "")

# }
# NOT RUN {
# ===========================================================================
# = A bivariate example (need a dataset with a VIABLE COVARIATE to do this) =
# ===========================================================================
selDVs  = c("ht", "wt") # Set the DV
selCovs = c("income") # Set the COV
selVars = umx_paste_names(selDVs, covNames = selCovs, sep = "", suffixes = 1:2)
# 80 rows so example runs fast on CRAN
mzData = subset(twinData, zygosity == "MZFF", selVars)[1:80, ]
dzData = subset(twinData, zygosity == "DZFF", selVars)[1:80, ]
m1 = umxACEcov(selDVs = selDVs, selCovs = selCovs,
   dzData = dzData, mzData = mzData, suffix = "", autoRun = TRUE
)
# }
# NOT RUN {
# }