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

Description Usage Arguments Details Value Author(s) References Examples

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

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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 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 theoutcome 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

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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 May 29, 2017, 3:55 p.m.

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