rppca: Fast pedigree PCA using sparse matrices and randomised linear algebra

Description

Fast pedigree PCA using sparse matrices and randomised linear algebra

Usage

rppca(X, ...)
# S3 method for spam
rppca(
  X,
  method = "randSVD",
  rank = 10,
  depth = 3,
  numVectors = 15,
  totVar = NULL,
  center = FALSE,
  ...
)
# S3 method for pedigree
rppca(
  X,
  method = "randSVD",
  rank = 10,
  depth = 3,
  numVectors = 15,
  totVar = NULL,
  center = FALSE,
  ...
)

Value

A list containing:

x: the principal components
sdev: the variance components of each PC. Note that the total variance is not known per se and this these components cannot be used to compute the proportion of the total variance accounted for by each PC. However, if nVecTraceEst is specified, rppca will estimate the total variance and return variance proportions.
vProp: the estimated variance proportions accounted for by each PC. Only returned if totVar is set.
scale: always FALSE
center: logical indicating whether or not the implicit data matrix was centred
rotation: the right singular values of the relationship matrix. Only returned if returnRotation == TRUE
varProps: proportion of the total variance explained by each PC. Only returned if starting from a pedigree object without centring, or if totVar is supplied.

Arguments

X: A representation of a pedigree, see Details.
...: optional arguments passed to methods
method: string only randSVD (the default) is implemented
rank: integer how many principal components to return
depth: integer number of iterations for generating the range matrix
numVectors: integer > rank to specify the oversampling for the range matrix
totVar: scalar (optional) the total variance, required for computation of variance proportions when using an L-inverse matrix a input
center: logical whether or not to (implicitly) centre the additive relationship matrix

Details

The output slots are named like those of R's built in prcomp function. Rotation is not returned by default as it is the transpose of the PC scores, which are returned in x. scale and center are set to FALSE.

Examples

Run this code

pc <- rppca(pedLInv)
ped <- pedigree(sire=pedMeta$fid,
                dam=pedMeta$mid,
                label=pedMeta$id
                )
pc2 <- rppca(ped)

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