LeverageScore | R Documentation |
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.
LeverageScore(object, ...)
## Default S3 method:
LeverageScore(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
## S3 method for class 'StdAssay'
LeverageScore(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
vf.method = NULL,
layer = "data",
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
## S3 method for class 'Assay'
LeverageScore(
object,
nsketch = 5000L,
ndims = NULL,
method = CountSketch,
vf.method = NULL,
layer = "data",
eps = 0.5,
seed = 123L,
verbose = TRUE,
...
)
## S3 method for class 'Seurat'
LeverageScore(
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,
...
)
object |
A matrix-like object |
... |
Arguments passed to other methods |
nsketch |
A positive integer. The number of sketches to be used in the approximation. Default is 5000. |
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. |
method |
The sketching method to use, defaults to CountSketch. |
eps |
A numeric. The error tolerance for the approximation in Johnson–Lindenstrauss embeddings, defaults to 0.5. |
seed |
A positive integer. The seed for the random number generator, defaults to 123. |
verbose |
Print progress and diagnostic messages |
vf.method |
VariableFeatures method |
layer |
layer to use |
assay |
assay to use |
var.name |
name of slot to store leverage scores |
over.write |
whether to overwrite slot that currently stores leverage scores. Defaults to FALSE, in which case the 'var.name' is modified if it already exists in the object |
Clarkson, K. L. & Woodruff, D. P. Low-rank approximation and regression in input sparsity time. JACM 63, 1–45 (2017). https://dl.acm.org/doi/10.1145/3019134;
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