# Bootstrap based test for testing an allometric model

### Description

Bootstrap-based procedure that tests whether the data can be modelled by an allometric model.

### Usage

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### Arguments

`formula` |
An object of class |

`data` |
A data frame argumment or matrix containing the model response variable
and covariates required by the |

`na.action` |
A function which indicates what should happen when the data contain 'NA's. The default is 'na.omit'. |

`nboot` |
Number of bootstrap repeats. |

`kbin` |
Number of binning nodes over which the function is to be estimated. |

`seed` |
Seed to be used in the bootstrap procedure. |

### Details

In order to facilitate the choice of a model appropriate
to the data while at the same time endeavouring to minimise the
loss of information, a bootstrap-based procedure, that test whether the
data can be modelled by an allometric model, was developed. Therefore,
`allotest`

tests the null hypothesis of an allometric model taking
into account the logarithm of the original variable
(*X^* = log(X)* and *Y^* = log(Y)*).

Based on a general model of the type

*Y^*=m(X^*)+\varepsilon*

the aim here is to test the null hypothesis of an allometric model

*H_0 = m(x^*) = a^*+ b^* x^**

*vs.* the general hypothesis
*H_1*, with *m*
being an unknown nonparametric function; or analogously,

*H_1: m(x^*)= a^*+ b^* x^* + g(x^*)*

with *g(x^*)* being an unknown function not equal to zero.

To implement this test we have used the wild bootstrap.

### Value

An object is returned with the following elements:

`statistic` |
the value of the test statistic. |

`value` |
the p-value of the test. |

### Author(s)

Marta Sestelo, Nora M. Villanueva and Javier Roca-Pardinas.

### References

Sestelo, M. and Roca-Pardinas, J. (2011). A new approach to estimation of
length-weight relationship of *Pollicipes* *pollicipes* (Gmelin, 1789)
on the Atlantic coast of Galicia (Northwest Spain): some aspects of its
biology and management. Journal of Shellfish Research, 30 (3), 939–948.

Sestelo, M. (2013). Development and computational implementation of estimation and inference methods in flexible regression models. Applications in Biology, Engineering and Environment. PhD Thesis, Department of Statistics and O.R. University of Vigo.

### Examples

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