Ace: Estimates the heritability of additive traits using a single...

In NlsyLinks: Utilities and Kinship Information for Research with the NLSY

Estimates the heritability of additive traits using a single variable.

Description

An ACE model is the foundation of most behavior genetic research. It estimates the additive heritability (with a), common environment (with c) and unshared heritability/environment (with e).

Usage

AceUnivariate(
  method         = c("DeFriesFulkerMethod1","DeFriesFulkerMethod3"),
  dataSet,
  oName_S1,
  oName_S2,
  rName          = "R",
  manifestScale  = "Continuous"
)

DeFriesFulkerMethod1(dataSet, oName_S1, oName_S2, rName="R")

DeFriesFulkerMethod3(dataSet, oName_S1, oName_S2, rName="R")

Arguments

method	The specific estimation technique.
dataSet	The base::data.frame that contains the two outcome variables and the relatedness coefficient (corresponding to oName_S1, oName_S2, and rName)
oName_S1	The name of the outcome variable corresponding to the first subject in the pair. This should be a character value.
oName_S2	The name of the outcome variable corresponding to the second subject in the pair. This should be a character value.
rName	The name of the relatedness coefficient for the pair (this is typically abbreviated as R). This should be a character value.
manifestScale	Currently, only continuous manifest/outcome variables are supported.

Details

The AceUnivariate() function is a wrapper that calls DeFriesFulkerMethod1() or DeFriesFulkerMethod3(). Future versions will incorporate methods that use latent variable models.

Value

Currently, a list is returned with the arguments ASquared, CSquared, ESquared, and RowCount. In the future, this may be changed to an S4 class.

Author(s)

Will Beasley

References

Rodgers, Joseph Lee, & Kohler, Hans-Peter (2005). Reformulating and simplifying the DF analysis model. Behavior Genetics, 35 (2), 211-217.

Examples

library(NlsyLinks) # Load the package into the current R session.
dsOutcomes <- ExtraOutcomes79
dsOutcomes$SubjectTag <- CreateSubjectTag(
  subjectID    = dsOutcomes$SubjectID,
  generation   = dsOutcomes$Generation
)
dsLinks <- Links79Pair
dsLinks <- dsLinks[dsLinks$RelationshipPath == "Gen2Siblings", ] # Only Gen2 Sibs (ie, NLSY79C)
dsDF <- CreatePairLinksDoubleEntered(
  outcomeDataset     = dsOutcomes,
  linksPairDataset   = dsLinks,
  outcomeNames       = c("MathStandardized", "HeightZGenderAge", "WeightZGenderAge")
)

estimatedAdultHeight <- DeFriesFulkerMethod3(
  dataSet    = dsDF,
  oName_S1   = "HeightZGenderAge_S1",
  oName_S2   = "HeightZGenderAge_S2"
)
estimatedAdultHeight # ASquared and CSquared should be 0.60 and 0.10 for this rough analysis.

estimatedMath <- DeFriesFulkerMethod3(
  dataSet    = dsDF,
  oName_S1   = "MathStandardized_S1",
  oName_S2   = "MathStandardized_S2"
)
estimatedMath # ASquared and CSquared should be 0.85 and 0.045.

class(GetDetails(estimatedMath))
summary(GetDetails(estimatedMath))

NlsyLinks documentation built on Sept. 22, 2023, 9:06 a.m.