A small data.frame containing the inferred ancestry on the synthetic profiles.

Source: R/RAIDS.R

The object is a data.frame with 4 columns.

data(matKNNSynthetic)

Format

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

sample.id

a character string representing the unique synthetic profile identifier.

D

a numeric representing the number of dimensions used to infer the ancestry of the synthetic profile.

K

a numeric representing the number of neighbors used to infer the ancestry of the synthetic profile.

SuperPop

a character string representing the inferred ancestry of the synthetic profile for the specific D and K values.

Value

The data.frame containing the information about the synthetic profiles. The data.frame contains 4 columns:

sample.id

a character string representing the unique synthetic profile identifier.

D

a numeric representing the number of dimensions used to infer the ancestry of the synthetic profile.

K

a numeric representing the number of neighbors used to infer the ancestry of the synthetic profile.

SuperPop

a character string representing the inferred ancestry of the synthetic profile for the specific D and K values.

Details

This dataset can be used to test the computeSyntheticROC function.

See also

computeSyntheticROC

for calculating the AUROC of the inferences for specific values of D and K using the inferred ancestry results from the synthetic profiles

Examples


## Loading demo dataset containing pedigree information for synthetic
## profiles
data(pedSynthetic)

## Loading demo dataset containing the inferred ancestry results
## for the synthetic data
data(matKNNSynthetic)

## Retain one K and one D value
matKNN <- matKNNSynthetic[matKNNSynthetic$D == 5 & matKNNSynthetic$K == 4, ]

## Compile statistics from the
## synthetic profiles for fixed values of D and K
results <- RAIDS:::computeSyntheticROC(matKNN=matKNN,
    matKNNAncestryColumn="SuperPop",
    pedCall=pedSynthetic, pedCallAncestryColumn="superPop",
    listCall=c("EAS", "EUR", "AFR", "AMR", "SAS"))

results$matAUROC.All
#>   pcaD K   ROC.AUC ROC.CI  N NBNA
#> 1    5 4 0.6227679      0 52    0
results$matAUROC.Call
#>   pcaD K Call         L       AUC         H
#> 1    5 4  EAS 0.4807257 0.6547619 0.8287981
#> 2    5 4  EUR 0.4064737 0.5666667 0.7268596
#> 3    5 4  AFR 0.8168697 0.9154135 1.0000000
#> 4    5 4  AMR 0.3743226 0.5056818 0.6370411
#> 5    5 4  SAS 0.3609393 0.5047619 0.6485845
results$listROC.Call
#> $EAS
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.6548
#> 95% CI: 0.4807-0.8288 (DeLong)
#> 
#> $EUR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.5667
#> 95% CI: 0.4065-0.7269 (DeLong)
#> 
#> $AFR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 38 controls (fCur 0) < 14 cases (fCur 1).
#> Area under the curve: 0.9154
#> 95% CI: 0.8169-1 (DeLong)
#> 
#> $AMR
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 44 controls (fCur 0) < 8 cases (fCur 1).
#> Area under the curve: 0.5057
#> 95% CI: 0.3743-0.637 (DeLong)
#> 
#> $SAS
#> 
#> Call:
#> roc.formula(formula = fCur ~ predMat[, j], ci = TRUE, quiet = TRUE)
#> 
#> Data: predMat[, j] in 42 controls (fCur 0) < 10 cases (fCur 1).
#> Area under the curve: 0.5048
#> 95% CI: 0.3609-0.6486 (DeLong)
#>