pca.cluster: K-means clustering and plotting function
In jmcole003/pcanalysis: Principal Components Analysis Package
Description
Usage
Arguments
Examples
Description
This function performs a k-means clustering analysis on a numerical data matrix with a specified k value. It assigns samples into k clusters and outputs a PCA biplot in which each sample is colored according to cluster.
Usage
1
pca.cluster(x, pca, k)
Arguments
x
input data matrix or data frame (numerical)
pca
principal component object returned from the run.pca() function
k
optimal k value (numerical)
Examples
1
pca.cluster(data, data_pca, 3)
jmcole003/pcanalysis documentation built on Dec. 21, 2021, 1:12 a.m.
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.
Description Usage Arguments Examples
Description
This function performs a k-means clustering analysis on a numerical data matrix with a specified k value. It assigns samples into k clusters and outputs a PCA biplot in which each sample is colored according to cluster.
Usage
1 | pca.cluster(x, pca, k)
|
Arguments
x |
input data matrix or data frame (numerical) |
pca |
principal component object returned from the run.pca() function |
k |
optimal k value (numerical) |
Examples
1 | pca.cluster(data, data_pca, 3)
|
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.