Estimates the heritability of additive traits using a single variable.

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).

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.

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.

Details

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

References

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

Author

Will Beasley

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.
#> [1] "Results of ACE estimation: [show]"
#>     ASquared     CSquared     ESquared    CaseCount 
#> 6.075526e-01 1.043565e-01 2.880909e-01 1.176800e+04 

estimatedMath <- DeFriesFulkerMethod3(
  dataSet    = dsDF,
  oName_S1   = "MathStandardized_S1",
  oName_S2   = "MathStandardized_S2"
)
estimatedMath #ASquared and CSquared should be 0.85 and 0.045.
#> [1] "Results of ACE estimation: [show]"
#>     ASquared     CSquared     ESquared    CaseCount 
#> 7.734447e-01 1.469204e-01 7.963486e-02 1.668000e+04 

class(GetDetails(estimatedMath))
#> [1] "lm"
summary(GetDetails(estimatedMath))
#> 
#> Call:
#> stats::lm(formula = dv_S1Centered ~ 0 + dv_S2Centered + interaction)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -49.195  -6.921   0.213   7.096  48.021 
#> 
#> Coefficients:
#>               Estimate Std. Error t value Pr(>|t|)    
#> dv_S2Centered  0.14692    0.02541   5.783 7.49e-09 ***
#> interaction    0.77344    0.05723  13.515  < 2e-16 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 11.22 on 16678 degrees of freedom
#>   (5548 observations deleted due to missingness)
#> Multiple R-squared:  0.2368,	Adjusted R-squared:  0.2367 
#> F-statistic:  2587 on 2 and 16678 DF,  p-value: < 2.2e-16
#>