CleanSemAceDataset: Produces a cleaned dataset that works well with when using...
In NlsyLinks: Utilities and kinship information for research with the NLSY

Description Usage Arguments Details Value Author(s) Examples

This function takes a ‘GroupSummary’ data.frame (which is created by the RGroupSummary function) and returns a data.frame that is used by the Ace function.

1	CleanSemAceDataset(dsDirty, dsGroupSummary, oName_S1, oName_S2, rName = "R")

`dsDirty`	This is the `data.frame` to be cleaned.
`dsGroupSummary`	The `data.frame` containing information about which groups should be included in the analyses. It should be created by the `RGroupSummary` function.
`oName_S1`	The name of the manifest variable (in `dsDirty`) for the first subject in each pair.
`oName_S2`	The name of the manifest variable (in `dsDirty`) for the second subject in each pair.
`rName`	The name of the variable (in `dsDirty`) indicating the pair's relatedness coefficient.

The function takes dsDirty and produces a new data.frame with the following features:

[A] Only three existing columns are retained: O1, O2, and R. They are assigned these names.

[B] A new column called GroupID is created to reflect their group membership (which is based on the R value). These valuesa re sequential integers, starting at 1. The group with the weakest R is 1. The group with the strongest R has the largest GroupID (this is typically the MZ tiwns).

[C] Any row is excluded if it has a missing data point for O1, O2, or R.

[D] The data.frame is sorted by the R value. This helps program against the multiple-group SEM API sometimes.

A data.frame with one row per subject pair. The data.frame contains the following variables (which can NOT be changed by the user through optional parameters):

`R`	The pair's `R` value.
`O1`	The outcome variable for the first subject in each pair.
`O2`	The outcome variable for the second subject in each pair.
`GroupID`	Indicates the pair's group membership.

Will Beasley

library(NlsyLinks) #Load the package into the current R session.
dsLinks <- Links79PairExpanded #Start with the built-in data.frame in NlsyLinks
dsLinks <- dsLinks[dsLinks$RelationshipPath=='Gen2Siblings', ] #Use only NLSY79-C siblings

oName_S1 <- "MathStandardized_S1" #Stands for Outcome1
oName_S2 <- "MathStandardized_S2" #Stands for Outcome2
dsGroupSummary <- RGroupSummary(dsLinks, oName_S1, oName_S2)

dsClean <- CleanSemAceDataset( dsDirty=dsLinks, dsGroupSummary, oName_S1, oName_S2, rName="R" )
summary(dsClean)

dsClean$AbsDifference <- abs(dsClean$O1 - dsClean$O2)
plot(jitter(dsClean$R), dsClean$AbsDifference, col="gray70")