chi2x3way {chi2x3way} | R Documentation |

It performs

1) the computation of the Pearson's index and its partitioning for three-way contingency tables under two Scenarios. When the input parameter `scen==1`

then the theoretical probabilities are
prescribed by the analyst (by default they are set homogeneous). When the input parameter `scen==2`

then the theoretical probabilities are estimated from the data.

2) the computation of the Marcotorchino's index and its partitioning for three-way contingency tables under the two Scenarios. When the input parameter `scen==1`

then the theoretical probabilities are
prescribed by the analyst (by default they are set homogeneous). When the input parameter `scen==2`

then the theoretical probabilities are
estimated from the data. In order to check the distribution of the Marcotorchino's index under the two Scenarios, it is possible to look at the results of a simulation study setting the input parameter
`simulation=TRUE`

.

chi2x3way(X, indextype = "chi2", scen = 2, simulation = FALSE, nboots = 1000, nran = 1000, pi = rep(1/dim(X)[[1]],dim(X)[[1]]), pj = rep(1/dim(X)[[2]],dim(X)[[2]]),pk = rep(1/dim(X)[[3]],dim(X)[[3]]), digits = 3)

`X` |
The three-way contingency table. |

`indextype` |
The input parameter for specifying what index should be considered.
By default, the partition of the classical three-way
Pearson index |

`scen` |
The input parameter for specifying what scenario should be considered. By default, |

`simulation` |
A flag parameter, |

`nboots` |
The input parameter for specifying the number of random three-way contingency tables to be generated when |

`nran` |
The input parameter for specifying the total number of samples of each randomly generated contingency table when |

`pi` |
The input parameter |

`pj` |
The input parameter |

`pk` |
The input parameter |

`digits` |
The minimum number of decimal places used for displaying the numerical summaries of the analysis is set by the parameter By default, |

`X` |
The three-way contingency table of dimension IxJxK. |

`indexparts` |
The three-way index partition |

`simulaout` |
When the input parameter When |

This function recalls internally many other functions, depending on the setting of the input parameter `indexype`

.
It recalls one of the four functions which does a partition under two different Scenarios.
These two Scenarios depend on the theoretical probabilities: 1) the theoretical probabilities can be prescribed by the analysy. By default, when `scen = 1`

,
they are set all equal (homogeneity margins); 2) when `scen = 2`

, the theoretical probabilities are estimated from the data.
After performing a partition, it gives the output object necessary for printing the results. The print function is `print.Chi2for3way`

.
This function belongs to the class `chi3class`

.

Lombardo R, Takane Y and Beh EJ

Beh EJ and Lombardo R (2014) Correspondence Analysis: Theory, Practice and New Strategies. John Wiley & Sons.

Carlier A Kroonenberg PM (1996) Biplots and decompositions in two-way and three-way correspondence analysis. Psychometrika, 61, 355-373.

Lancaster H O (1951) Complex contingency tables treated by the partition of the chi-square. Journal of Royal Statistical Society, Series B, 13, 242-249.

Loisel S and Takane Y (2016) Partitions of Pearson's chi-square ststistic for frequency tables: A comprehensive account. Computational Statistics, 31, 1429-1452.

Lombardo R Carlier A D'Ambra L (1996) Nonsymmetric correspondence analysis for three-way contingency tables. Methodologica, 4, 59-80.

##---- Should be DIRECTLY executable !! ---- ##-- ==> Define data, use random, ## The function is currently defined as data(olive) chi2x3way(olive, scen = 2, indextype = "tauM", simulation = FALSE, nboots = 100, nran = 1000, pi = rep(1/dim(olive)[[1]],dim(olive)[[1]]), pj = rep(1/dim(olive)[[2]],dim(olive)[[2]]), pk = rep(1/dim(olive)[[3]],dim(olive)[[3]]), digits = 3)

[Package *chi2x3way* version 1.1 Index]