NlsyLinks (version 1.003)

Links79PairExpanded: Kinship linking file for pairs of relatives. It builds upon the Links79Pair dataset.

Description

Please first read the documentation for Links79Pair. That dataset contains the same pairs/rows, but only a subset of the variables/columns. Specifies the relatedness coefficient (ie, 'R') between subjects in the same extended family. Each row represents a unique relationship pair. An extended family with $k$ subjects will have $k$($k$-1)/2 rows. Typically, Subject1 is older while Subject2 is younger. Currently this dataset contains only Gen2 siblings. However, it soon will be generalized to the five categories specified by the RelationshipPath variable. Code written using this dataset should NOT assume it contains only Gen2 sibiling pairs. See an example of filtering the relationship category in the in Links79Pair documentation.

Usage

data(Links79PairExpanded)

source

See Links79Pair.

Details

Please first read the documentation for Links79Pair. That dataset contains the same pairs/rows, but only a subset of the variables/columns. The specific steps to determine the R coefficient will be described in an upcoming publication. The following information may influence the decisions of an applied researcher. A distinction is made between 'Explicit' and 'Implicit' information. Explicit information comes from survey items that directly address the subject's relationships. For instance in 2006, surveys asked if the sibling pair share the same biological father (eg, Y19940.00 and T00020.00). Implicit information comes from items where the subject typically isn't aware that their responses may be used to determine genetic relatedness. For instance, if two siblings have biological fathers with the same month of death (eg, R37722.00 and R37723.00), it may be reasonable to assume they share the same biological father. 'Interpolation' is our lingo when other siblings are used to leverage insight into the current pair. For example, assume Subject 101, 102, and 103 have the same mother. Further assume 101 and 102 report they share a biological father, and that 101 and 103 share one too. Finally, assume that we don't have information about the relationship between 102 and 103. If we are comfortable with our level of uncertainty of these determinations, then we can interpolate/infer that 102 and 103 are full-siblings as well. **Alternate wording that I like less: For example, assume Subject 101, 102, and 103 have the same mother. And that we are reasonable sure than 101 and 102 are full-siblings, and that 101 and 103 are full-siblings. If we are comfortable with our level of uncertainty of these two determinations, then we can interpolate/infer that 102 and 103 are full-siblings as well.** The math and weight scores are duplicated from ExtraOutcomes79, but are included here to make some examples more concise and accessible.

Examples

library(NlsyLinks) #Load the package into the current R session.
data(Links79PairExpanded)  #Load the dataset from the NlsyLinks package.
olderR <- Links79PairExpanded$RExplicitOlderSibVersion  #Declare a concise variable name.
youngerR <- Links79PairExpanded$RExplicitYoungerSibVersion  #Declare a concise variable name.

plot(jitter(olderR), jitter(youngerR))  #Scatterplot the siblings' responses.
table(youngerR, olderR)  #Table of the relationship between the siblings' responses.
ftable(youngerR, olderR, dnn=c("Younger's Version", "Older's Version")) #A formatted table.

#write.csv(Links79PairExpanded, file='F:/Projects/RDev/NlsyLinksStaging/Links79PairExpanded.csv', 
#  row.names=FALSE)