PCA is a multivariate data reduction technique. It summarises the data in a smaller number of Principal Components that maximise variance.
Usage
PCA(number_components = 2, ...)
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 with the following output slots:
scores | (DatasetExperiment) A matrix of PCA scores where each column corresponds to a Principal Component. |
loadings | (data.frame) |
eigenvalues | (data.frame) |
ssx | (numeric) |
correlation | (data.frame) |
that | (DatasetExperiment) |
Inheritance
A PCA object inherits the following struct classes:
[PCA] >> [model] >> [struct_class]
Examples
M = PCA(
number_components = 2)