Another technique for analyzing data is principal components analysis. Principal components analysis breaks a set of (possibly correlated) variables into a set of uncorrelated variables.

In R, principal components analysis is available through the
function `prcomp`

in the `stats`

package:

## S3 method for class 'formula': prcomp(formula, data = NULL, subset, na.action, ...) ## Default S3 method: prcomp(x, retx = TRUE, center = TRUE, scale. = FALSE, tol = NULL, ...)

Here is a description of the arguments to `prcomp`

.

Argument | Description | Default |
---|---|---|

formula | In the formula method, specifies formula with no response variable, indicating columns of a data frame to use in the analysis. | |

data | An optional data frame containing the data specified in
`formula` . | |

subset | An optional vector specifying observations to include in the analysis. | |

na.action | A function specifying how to deal with `NA` values. | |

x | In the default method, specifies a numeric or complex matrix of data for the analysis. | |

retx | A logical value specifying whether rotated variables should be returned. | `TRUE` |

center | A logical value specifying whether values should be zero centered. | `TRUE` |

scale | A logical value specifying whether values should be scaled to have unit variance. | `TRUE` |

tol | A numeric value specifying a tolerance value below which components should be omitted. | `NULL` |

... | Additional arguments passed to other methods. |

As an example, let’s try principal components analysis on a matrix of team batting statistics. Let’s start by loading the ...

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