removePrincipalComponents: Remove leading principal components from data
In nosarcasm/WGCNA: Weighted Correlation Network Analysis
Description
Usage
Arguments
Value
Author(s)
See Also
View source: R/votingLinearPredictor.R
Description
This function calculates a fixed number of the first principal components of the given data and returns the
residuals of a linear regression of each column on the principal components.
Usage
1
Arguments
x
Input data, a numeric matrix. All entries must be non-missing and finite.
n
Number of principal components to remove. This must be smaller than the smaller of the number of rows and
columns in x.
Value
A matrix of residuals of the same dimensions as x.
Author(s)
Peter Langfelder
See Also
svd for singular value decomposition,
lm for linear regression
nosarcasm/WGCNA documentation built on May 28, 2019, 1:01 p.m.
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Description Usage Arguments Value Author(s) See Also
View source: R/votingLinearPredictor.R
Description
This function calculates a fixed number of the first principal components of the given data and returns the residuals of a linear regression of each column on the principal components.
Usage
1 |
Arguments
x |
Input data, a numeric matrix. All entries must be non-missing and finite. |
n |
Number of principal components to remove. This must be smaller than the smaller of the number of rows and
columns in |
Value
A matrix of residuals of the same dimensions as x.
Author(s)
Peter Langfelder
See Also
svd for singular value decomposition,
lm for linear regression
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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