print.tunelocal {CA3variants} | R Documentation |

## Print of tunelocal function results

### Description

This function prints the results of `tunelocal`

for choosing the optimal model dimension of a variant of three-way correspondence analysis.
When `boots = T`

the number of different models that is assessed is based on the size of the original data being analysed.

For example, for a 4 x 5 x 4, there are 80 different models that are assessed.

When `boots = T`

, the number of different models that is assessed is based on the size of all models

obtained from the combination of dimensions of the bootstrapped data.

For example, for a 4 x 5 x 4 array, there are 800 different models that are assessed. By default`nboots = 100`

,

you can change the parameter value in input of `tunelocal`

function.

### Usage

```
## S3 method for class 'tunelocal'
print(x, digits = 3,...)
```

### Arguments

`x` |
The name of the output of the function |

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

`...` |
Further arguments passed to or from other methods. |

### Value

The value of output returned depends on the kind of sampling chosen. The sampling for making the convex hull can be based on the original data or on the bootstrapped data samples. In detail:

`XG` |
The data samples used for assessing the optimal model dimension (original and/or bootstrapped). |

`output1` |
The results of |

`ca3type` |
It gives information about the kind of variant of three-way CA considered. |

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

### Author(s)

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

### References

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.

### Examples

```
res.tunelocal<-tunelocal(happy, ca3type = "CA3")
print(res.tunelocal)
```

*CA3variants*version 3.3.1 Index]