pca: Our implementation of principal component analysis (PCA)
In g-l-mansell/RcppRidge: Parallel Bayesian Ridge Regression
Description
Usage
Arguments
Value
Description
Given an data matrix x, a linear projection is applied to maximise sample variation.
The first two prinicpal components are returned.
Usage
1
Arguments
x
numeric matrix of data, where rows are samples
sigma
Value
dataframe of sample projections onto PC1 and PC2
g-l-mansell/RcppRidge documentation built on Dec. 20, 2021, 9:43 a.m.
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Description Usage Arguments Value
Description
Given an data matrix x, a linear projection is applied to maximise sample variation. The first two prinicpal components are returned.
Usage
1 |
Arguments
x |
numeric matrix of data, where rows are samples |
sigma |
Value
dataframe of sample projections onto PC1 and PC2
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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