Calculates summary statistics for each Relatedness Group in the sample.

Source: R/r-group-summary.R

Before and after running ACE Models, it is important to examine the characteristics of the different groups. When the ACE is estimated with an SEM using multiple groups, it is even even more important. Groups may contain too few subjects to have a well-behaved covariance matrix.

If a group's covariance matrix is not Positive Definite (or it's misbehaving in some other way), it's typically recommended to exclude that group from the SEM.

RGroupSummary(
  ds,
  oName_S1,
  oName_S2,
  rName = "R",
  determinantThreshold = 1e-05
)

Arguments

ds

The base::data.frame containing the following variables:
oName_S1

The name of the outcome variable corresponding to the first subject in the pair.
oName_S2

The name of the outcome variable corresponding to the first subject in the pair.
rName

The name of the variable specifying the pair's Relatedness coefficient.
determinantThreshold

The minimum value the covariance matrix's determinant (for the group) should exceed to be considered Positive Definite.

Value

A base::data.frame with one row per group. The base::data.frame contains the following variables:

  • R The group's R value. Note the name of this variable can be changed by the user, by specifying a non-default value to the rName argument.

  • Included Indicates if the group should be included in a multiple-group SEM.

  • PairCount The number of pairs in the group with complete data for R and the two outcome/manifest variables.

  • O1Mean The mean (of the outcome variable) among the group's first members, excluding the missing values.

  • O2Mean The mean (of the outcome variable) among the group's second members, excluding the missing values.

  • O1Variance The variance (of the outcome variable) among the group's first members.

  • O2Variance The variance (of the outcome variable) among the group's second members.

  • O1O2Covariance The covariance (of the outcome variable) across the group's first and second members.

  • Correlation The correlation (of the outcome variable) across the group's first and second members.

  • Determinant The determinant of the group's covariance matrix.

  • PosDefinite Indicates if the group's covariance matrix is positive definite.

Details

This function isn't specific to an ACE model and groups defined by R. It could be applied to any multiple-group SEM with two manifest/outcome variables. In the future, we may generalize it beyond two manifest variables.

To get summary stats for the entire sample, create a dummy indicator variable that assigns everyone to the same group. See the second example below.

The default determinantThreshold value is nonzero, in order to forgive slight numerical inaccuracies caused by fixed-precision arithmetic.

References

Please see Neale & Maes for more information about SEM with multiple groups.

Author

Will Beasley and David Bard

Examples

library(NlsyLinks) #Load the package into the current R session.
dsLinks        <- Links79PairExpanded   # Load the dataset from the NlsyLinks package.
dsLinks        <- dsLinks[dsLinks$RelationshipPath=='Gen2Siblings', ]
oName_S1       <- "MathStandardized_S1" # Stands for Outcome1
oName_S2       <- "MathStandardized_S2" # Stands for Outcome2
dsGroupSummary <- RGroupSummary(dsLinks, oName_S1, oName_S2)
dsGroupSummary

#>       R Included PairCount    O1Mean    O2Mean O1Variance O2Variance
#> 1 0.250     TRUE      2689  95.10450  95.97936   126.9489   150.1775
#> 2 0.375     TRUE       137  93.63139  93.36861   160.0120   136.6628
#> 3 0.500     TRUE      5491  99.89374 100.02868   168.7326   172.7293
#> 4 0.750    FALSE         2 108.50000 106.00000   220.5000    18.0000
#> 5 1.000     TRUE        21  98.21429  96.02381   289.4393   215.2369
#>   O1O2Covariance Correlation Determinant PosDefinite
#> 1       41.96914   0.3039577   17303.459        TRUE
#> 2       50.39790   0.3408090   19327.735        TRUE
#> 3       90.04116   0.5274225   21037.642        TRUE
#> 4       63.00000   1.0000000       0.000       FALSE
#> 5      229.10714   0.9179130    9807.933        TRUE

#Should return:
#      R Included PairCount   O1Mean   O2Mean O1Variance O2Variance O1O2Covariance Correlation
#1 0.250     TRUE      2718  94.6439  95.5990    169.650    207.842        41.0783    0.218761
#2 0.375     TRUE       139  92.6043  93.1655    172.531    187.081        40.4790    0.225311
#3 0.500     TRUE      5511  99.8940 100.1789    230.504    232.971       107.3707    0.463336
#4 0.750    FALSE         2 108.5000 106.0000    220.500     18.000        63.0000    1.000000
#5 1.000     TRUE        22  98.6364  95.5455    319.195    343.117       277.5887    0.838789
#  Determinant PosDefinite
#1     33573.0        TRUE
#2     30638.7        TRUE
#3     42172.2        TRUE
#4         0.0       FALSE
#5     32465.6        TRUE

#To get summary stats for the whole sample, create one large inclusive group.
dsLinks$Dummy <- 1
(dsSampleSummary <- RGroupSummary(dsLinks, oName_S1, oName_S2, rName="Dummy"))

#>   Dummy Included PairCount   O1Mean   O2Mean O1Variance O2Variance
#> 1     1     TRUE      8340 98.24454 98.60504   160.6817   168.9103
#>   O1O2Covariance Correlation Determinant PosDefinite
#> 1       78.80601   0.4783524    20930.41        TRUE

#Should return:
#  Dummy Included PairCount   O1Mean   O2Mean O1Variance O2Variance O1O2Covariance
#1     1     TRUE      8392 98.07162 98.56864    216.466   229.2988       90.90266
#  Correlation Determinant PosDefinite
#1   0.4080195     41372.1        TRUE
###
### ReadCsvNlsy79
###
if (FALSE) {
filePathGen2 <- "~/Nlsy/Datasets/gen2-birth.csv"
ds <- ReadCsvNlsy79Gen2(filePath=filePathGen2)
}