Binomial(2, p) scaling where p is estimated.

bed_scaleBinom(
  obj.bed,
  ind.row = rows_along(obj.bed),
  ind.col = cols_along(obj.bed)
)

Arguments

obj.bed

Object of type bed, which is the mapping of some bed file. Use obj.bed <- bed(bedfile) to get this object.

ind.row

An optional vector of the row indices (individuals) that are used. If not specified, all rows are used.
Don't use negative indices.

ind.col

An optional vector of the column indices (SNPs) that are used. If not specified, all columns are used.
Don't use negative indices.

Value

A data frame with $center and $scale.

Details

You will probably not use this function as is but as parameter fun.scaling of other functions (e.g. bed_autoSVD and bed_randomSVD).

References

This scaling is widely used for SNP arrays. Patterson N, Price AL, Reich D (2006). Population Structure and Eigenanalysis. PLoS Genet 2(12): e190. http://dx.doi.org/10.1371/journal.pgen.0020190.

Examples

bedfile <- system.file("extdata", "example-missing.bed", package = "bigsnpr") obj.bed <- bed(bedfile) str(bed_scaleBinom(obj.bed))
#> 'data.frame': 500 obs. of 2 variables: #> $ center: num 0.0419 0.0829 0.1198 0.1744 0.2194 ... #> $ scale : num 0.203 0.282 0.336 0.399 0.442 ...
str(bed_randomSVD(obj.bed, bed_scaleBinom))
#> List of 7 #> $ d : num [1:10] 145.8 105.8 89.8 80 68.8 ... #> $ u : num [1:200, 1:10] -0.10613 -0.03125 -0.02387 -0.00184 -0.0563 ... #> $ v : num [1:500, 1:10] 0.0545 0.0518 0.0535 0.0551 0.04 ... #> $ niter : num 3 #> $ nops : num 76 #> $ center: num [1:500] 0.0419 0.0829 0.1198 0.1744 0.2194 ... #> $ scale : num [1:500] 0.203 0.282 0.336 0.399 0.442 ... #> - attr(*, "class")= chr "big_SVD"