Randomized partial SVD
Partial SVD (or PCA) of a genotype matrix stored as a PLINK (.bed) file.#'
bed_randomSVD( obj.bed, fun.scaling = bed_scaleBinom, ind.row = rows_along(obj.bed), ind.col = cols_along(obj.bed), k = 10, tol = 1e-04, verbose = FALSE, ncores = 1 )
obj.bed |
Object of type bed, which is the mapping of some bed file.
Use |
fun.scaling |
A function with parameters \frac{X_{i,j} - center_j}{scale_j}. Default doesn't use any scaling.
You can also provide your own |
ind.row |
An optional vector of the row indices (individuals) that
are used. If not specified, all rows are used. |
ind.col |
An optional vector of the column indices (SNPs) that are used.
If not specified, all columns are used. |
k |
Number of singular vectors/values to compute. Default is |
tol |
Precision parameter of svds. Default is |
verbose |
Should some progress be printed? Default is |
ncores |
Number of cores used. Default doesn't use parallelism. You may use nb_cores. |
A named list (an S3 class "big_SVD") of
d, the singular values,
u, the left singular vectors,
v, the right singular vectors,
niter, the number of the iteration of the algorithm,
nops, number of Matrix-Vector multiplications used,
center, the centering vector,
scale, the scaling vector.
Note that to obtain the Principal Components, you must use predict on the result. See examples.
bedfile <- system.file("extdata", "example.bed", package = "bigsnpr")
obj.bed <- bed(bedfile)
str(bed_randomSVD(obj.bed))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.