Description Usage Arguments Details Value References See Also Examples

A transformation function for three-class ROC data in order to obtain normally distributed classes.

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`x, y, z` |
vectors containing the data of the three classes "healthy", "intermediate" and "diseased" to be transformed. In two-class ROC analysis only. |

`lambda` |
vector of possible lambdas the log-likelihood function is evaluated. |

`lambda2` |
numeric shifting parameter. For the implemented Box-Cox
transformation positive measurements in |

`eps` |
numeric; indicating the bandwith around zero, where |

`verbose` |
logical; indicating whether output should be displayed (default) or not. |

A Box-Cox transformation computing

*X^{(λ)} = log(X) if λ = 0 and X^{(λ)}
= (X^λ -1)/λ otherwise*

with optimal *λ* estimated from the likelihood kernel function,
as formally described in the supplementary
material in Bantis et al. (2017). If the data include any nonpositive
observations, a shifting parameter `lambda2`

can be included in the
transformation given by:

*X^{(λ)} = log(X+λ_2), if λ = 0
and X^{(λ)} = ((X+λ_2)^λ -1)/λ
otherwise.
*

A list with components:

`xbc, ybc, zbc` |
The transformed vectors. |

`lambda` |
estimated optimal parameter. |

`shapiro.p.value` |
p-values obtained from |

Bantis LE, Nakas CT, Reiser B, Myall D and Dalrymple-Alford JC
(2015) Construction of joint confidence regions for the optimal true class
fractions of receiver operating characteristic (roc) surfaces and
manifolds. *Statistical Methods in Medical Research* **26**(3): 1429–1442.

Box, G. E. P. and Cox, D. R. (1964). An analysis of
transformations (with discussion). *Journal of the Royal Statistical Society,
Series B*, **26**, 211–252.

`shapiro.test`

and `boxcox`

from the package `MASS`

.

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