eigen_centered: Sample Matrix Centralization for Eigen-decomposition
In chenqi57/MMDS: Modified Multidimensional Scaling
Description
Usage
Arguments
Details
Value
Examples
Description
The function centers the sample matrix X to Y and returns Y's distance matrix for futher eigen-decomposition.
Usage
1
Arguments
X
A d by n sample matrix with each column being a sample and each row being a variable.
Details
The sample matrix X is a d by n matrix, where d is the dimension of each observed data point and n is the number of such data points. The output S will be a d by d symmetric matrix.
Value
A d by d symmetric matrix which is X's distance matrix after centralization.
Examples
1
2
3
4
5
6
set.seed(496)
n = 10
d = 20
X = matrix(rnorm(d * n), n, d)
S = eigen_centered(X)
S
chenqi57/MMDS documentation built on Aug. 20, 2021, 6:34 p.m.
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Description Usage Arguments Details Value Examples
Description
The function centers the sample matrix X to Y and returns Y's distance matrix for futher eigen-decomposition.
Usage
1 |
Arguments
X |
A d by n sample matrix with each column being a sample and each row being a variable. |
Details
The sample matrix X is a d by n matrix, where d is the dimension of each observed data point and n is the number of such data points. The output S will be a d by d symmetric matrix.
Value
A d by d symmetric matrix which is X's distance matrix after centralization.
Examples
1 2 3 4 5 6 | set.seed(496)
n = 10
d = 20
X = matrix(rnorm(d * n), n, d)
S = eigen_centered(X)
S
|
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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