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LeverageScore.Rd
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LeverageScore.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generics.R, R/sketching.R
\name{LeverageScore}
\alias{LeverageScore}
\alias{LeverageScore.default}
\alias{LeverageScore.DelayedMatrix}
\alias{LeverageScore.StdAssay}
\alias{LeverageScore.Assay}
\alias{LeverageScore.Seurat}
\title{Leverage Score Calculation}
\usage{
LeverageScore(object, ...)
\method{LeverageScore}{default}(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
\method{LeverageScore}{DelayedMatrix}(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
eps = 0.5,
seed = 123L,
block.size = 1e+08,
verbose = TRUE,
...
)
\method{LeverageScore}{StdAssay}(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
vf.method = NULL,
layer = "data",
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
\method{LeverageScore}{Assay}(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
vf.method = NULL,
layer = "data",
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
\method{LeverageScore}{Seurat}(
object,
assay = NULL,
nsketch = 5000L,
ndims = NULL,
var.name = "leverage.score",
over.write = FALSE,
method = CountSketch,
vf.method = NULL,
layer = "data",
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
}
\arguments{
\item{object}{A matrix-like object}
\item{...}{Arguments passed to other methods}
\item{nsketch}{A positive integer. The number of sketches to be used in the approximation.
Default is 5000.}
\item{ndims}{A positive integer or NULL. The number of dimensions to use. If NULL, the number
of dimensions will default to the number of columns in the object.}
\item{method}{The sketching method to use, defaults to CountSketch.}
\item{eps}{A numeric. The error tolerance for the approximation in Johnson–Lindenstrauss embeddings,
defaults to 0.5.}
\item{seed}{A positive integer. The seed for the random number generator, defaults to 123.}
\item{verbose}{Print progress and diagnostic messages}
}
\description{
This function computes the leverage scores for a given object
It uses the concept of sketching and random projections. The function provides an approximation
to the leverage scores using a scalable method suitable for large matrices.
}
\references{
Clarkson, K. L. & Woodruff, D. P.
Low-rank approximation and regression in input sparsity time.
JACM 63, 1–45 (2017). \url{https://dl.acm.org/doi/10.1145/3019134};
}