# pca: Principal Components Analysis In KODAMA: Knowledge Discovery by Accuracy Maximization

 pca R Documentation

## Principal Components Analysis

### Description

Performs a principal components analysis on the given data matrix and returns the results as an object of class "`prcomp`".

### Usage

```pca(x, ...)
```

### Arguments

 `x` a matrix of data. `...` arguments passed to `prcomp` function.

### Value

The function returns a list with class `prcomp` containing the following components:

 `sdev` the standard deviations of the principal components (i.e., the square roots of the eigenvalues of the covariance/correlation matrix, though the calculation is actually done with the singular values of the data matrix). `rotation` the matrix of variable loadings (i.e., a matrix whose columns contain the eigenvectors). The function `princomp` returns this in the element `loadings`. `x` if `retx` is `TRUE` the value of the rotated data (the centred (and scaled if requested) data multiplied by the `rotation` matrix) is returned. Hence, `cov(x)` is the diagonal matrix `diag(sdev^2)`. For the formula method, `napredict()` is applied to handle the treatment of values omitted by the `na.action`. `center, scale` the centering and scaling used, or `FALSE`. `txt` the component of variance of each Principal Component.

### Author(s)

Stefano Cacciatore

### References

Pearson, K
On Lines and Planes of Closest Fit to Systems of Points in Space.
Philosophical Magazine 1901;2 (11): 559-572. doi:10.1080/14786440109462720. Link

`prcomp`

### Examples

```data(MetRef)
u=MetRef\$data;
u=u[,-which(colSums(u)==0)]
u=normalization(u)\$newXtrain
u=scaling(u)\$newXtrain
class=as.numeric(as.factor(MetRef\$gender))
cc=pca(u)
plot(cc\$x,pch=21,bg=class,xlab=cc\$txt,ylab=cc\$txt)
```

KODAMA documentation built on April 1, 2022, 5:06 p.m.