Description Usage Arguments Details Value Note Author(s) References See Also Examples

The bootstrap-CFA tries to replicate the pattern of significant configurations by re-sampling.

1 |

`configs` |
Contains the configurations. This can be a dataframe or a matrix. The dataframe can contain numbers, characters, factors, or booleans. The matrix can consist of numbers, characters or booleans (factors are implicitely re-converted to numerical levels). There must be >=3 columns. |

`cnts` |
Contains the counts for the configuration. If it is set to NA, a count of one is assumed for every
row. This allows untabulated data to be processed. |

`runs` |
Number of samples to be drawn. |

`sig.item` |
Indicator of significance in the result table (sig.z,sig.chisq,sig.perli,sig.zl, sig.zl.corr). Do not forget to set the proper parameters for the CFA if sig.perli,sig.zl or sig.zl.corr are to be used! |

`...` |
Parameters to be to relayed to the CFA |

Takes 'runs' samples and does as many CFAs while counting how many times this configuration was considered to be significant.

Repeated-measures CFAs (mcfa) are not provided.

This is a heuristic method rather than a strict test of significance since there is no adjustment for multiple testing whatsoever. The advantage is a more reliable picture compared to splitting the original data, doing a CFA, and checking if the configurations re-appear in a CFA with the other half of the data.

`cnt.antitype` |
Number of antiypes |

`cnt.type ` |
Number of types |

`pct.types` |
Number of types in percent |

`cnt.sig` |
Number of significant results |

`pct.cnt.sig` |
Number of significant results in percent |

`bcfa()`

performs many CFAs which are by themselves slow, so the execution can
be **very** time-consuming, especially if a sufficiently high value for `runs`

was selected

Stefan Funke <s.funke@t-online.de>

Lautsch, E., von Weber S. (1995) Methoden und Anwendungen der Konfigurationsfrequenzanalyse Psychologie und Medizin, Beltz Psychologie Verlagsunion

1 2 3 4 5 6 |

```
cnt.antitype cnt.type pct.types cnt.sig pct.cnt.sig
B D F G 23 2 8 0 0
B D E G 4 21 84 0 0
B C F H 25 0 0 5 20
B C F G 3 22 88 0 0
B C E H 16 9 36 5 20
B C E G 18 7 28 0 0
A D F G 7 18 72 0 0
A D E H 25 0 0 5 20
A D E G 24 1 4 0 0
A C F H 25 0 0 5 20
A C F G 19 6 24 0 0
A C E H 5 20 80 5 20
A C E G 10 15 60 0 0
```

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.