tunelocal {CA3variants} | R Documentation |

This function allows to select the optimal dimension number
for correspondence biplot, given the set of possible dimension combination
of the original data.
For exploring, it is also possible to check the optimal model dimension by using
boostrap samples which have the same marginal proportions and the total number
of the original table. When the input parameter `boots = T`

, it does bootstrap sampling.
There are three kinds of possible bootstrap sampling.
When `boottype = "bootnp"`

it performs a non parametric bootstrap sampling.
When `boottype = "bootpsimple"`

it performs a parametric simple bootstrap sampling.
When `boottype = "bootpstrat"`

, it performs a parametric stratified bootstrap sampling.
In particular in case of parametric bootstrap types,
when `resamptype=1`

it considers a multinomial distribution, and when `resamptype = 2`

it considers a poisson distribution.

tunelocal(Xdata, ca3type = "CA3", resp = "row", norder = 3, digits = 3, boots = FALSE, nboots = 0, boottype= "bootpsimple", resamptype = 1)

`Xdata` |
The three-way data. It can be a |

`ca3type` |
The specification of the analysis to be performed.
If |

`resp` |
The input parameter for specifying in non-symmetrical three-way correspondence analysis variants ( |

`norder` |
The input parameter for specifying the number of ordered variable when |

`digits` |
The input parameter specifying the digital number. By default, |

`boots` |
The flag parameter to perform the search of optimal dimensions using
bootstrap samples. By defaults, |

`nboots` |
The number of bootstrap samples to generate when |

`boottype` |
The specification of the kind of bootstrap sampling to be performed.
If |

`resamptype` |
When the kind of bootstrap is parametric you can set the data distribution using
the input parameter |

`XG` |
The list of tables on which is performed the three-way CA variant.
It consists of the original array and (when |

`output1` |
Chi-square criterion and df of models on the convex hull when using the original array. |

`output2` |
Chi-square criterion and df of models on the convex hull when using bootstrapped arrays. |

`output3` |
Badness of fit criterion and df of models on the convex hull when using the original array. |

`output4` |
Badness of fit criterion and df of models on the convex hull when using bootstrapped arrays. |

`output5` |
Goodness of fit criterion and df of models on the convex hull when using the original array. |

`output6` |
Goodness of fit criterion and df of models on the convex hull when using bootstrapped arrays. |

Rosaria Lombardo, Michel van de Velden, Eric J Beh.

Beh EJ and Lombardo R (2014). Correspondence Analysis, Theory, Practice and New Strategies. John Wiley & Sons.\ Wilderjans T F, Ceulemans E, and Meers K (2013). CHull: A generic convex hull based model selection method. Behavior Research Methods, 45, 1-15.\ Ceulemans E, and Kiers H A L (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method. British Journal of Mathematical & Statistical Psychology, 59, 133-150.

data(happy) tunelocal(Xdata = happy, ca3type = "CA3", boots = FALSE)

[Package *CA3variants* version 3.0 Index]