getMeaningfulPCs | R Documentation |

get number of meaningful Principal components

```
getMeaningfulPCs(values, n, expect = 2, sdev = FALSE)
```

`values` |
eigenvalues from a PCA |

`n` |
sample size |

`expect` |
expectation value for chi-square distribution of df=2 |

`sdev` |
logical: if TRUE, it is assumed that the values are square roots of eigenvalues. |

This implements the method suggested by Bookstein (2014, pp. 324), to determine whether a PC is entitled to interpretation. I.e. a PC is regarded meaningful (its direction) if the ratio of this PC and its successor is above a threshold based on a log-likelihood ratio (and dependend on sample size).

`tol` |
threshold of ratio specific for |

`good` |
integer vector specifying the meaningful Principal Components |

Bookstein, F. L. Measuring and reasoning: numerical inference in the sciences. Cambridge University Press, 2014

`getPCtol`

```
data(boneData)
proc <- procSym(boneLM)
getMeaningfulPCs(proc$eigenvalues,n=nrow(proc$PCscores))
## the first 3 PCs are reported as meaningful
## show barplot that seem to fit the bill
barplot(proc$eigenvalues)
```

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