colSds: Standard deviation of columns and rows of large matrices
In proxyC: Computes Proximity in Large Sparse Matrices
colSds R Documentation
Standard deviation of columns and rows of large matrices
Description
Produces the same result as apply(x, 1, sd) or apply(x, 2, sd)
without coercing matrix to dense matrix. Values are not identical to
sd because of the floating point precision issue in C++.
Usage
colSds(x)
rowSds(x)
Arguments
x
matrix or Matrix object
Examples
mt <- Matrix::rsparsematrix(100, 100, 0.01)
colSds(mt)
apply(mt, 2, sd) # the same
proxyC documentation built on May 29, 2024, 8:50 a.m.
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| colSds | R Documentation |
Standard deviation of columns and rows of large matrices
Description
Produces the same result as apply(x, 1, sd) or apply(x, 2, sd)
without coercing matrix to dense matrix. Values are not identical to
sd because of the floating point precision issue in C++.
Usage
colSds(x)
rowSds(x)
Arguments
x |
matrix or Matrix object |
Examples
mt <- Matrix::rsparsematrix(100, 100, 0.01)
colSds(mt)
apply(mt, 2, sd) # the same
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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