PCA: Principal Component Analysis (PCA)
In structToolbox: Data processing & analysis tools for Metabolomics and other omics
Description
Usage
Arguments
Value
Examples
Description
PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance.
Usage
1
Arguments
number_components
(numeric, integer) The number of Principal Components calculated. The default is 2.
...
Additional slots and values passed to struct_class.
Value
A PCA object.
Examples
1
M = PCA()
structToolbox documentation built on Nov. 8, 2020, 6:54 p.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 Value Examples
Description
PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance.
Usage
1 |
Arguments
number_components |
(numeric, integer) The number of Principal Components calculated. The default is |
... |
Additional slots and values passed to |
Value
A PCA object.
Examples
1 | M = PCA()
|
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.