Description Usage Arguments Details Value Author(s) References See Also Examples

optMVtrans creates a new dataset where the columns of that dataset have been subjected to a non-linear transformation that maximizes the sum of squared lower-triangle correlations in the correlation matrix among the variables.

1 | ```
optMVtrans(dat, maxit = 25, rescale=TRUE, diff=.01)
``` |

`dat` |
A data frame containing only numeric variables. |

`maxit` |
The maximum number of transformations to compute before stopping. |

`rescale` |
Logical indicating whether the data should be rescaled such that all variables have minimum value=1. |

`diff` |
The difference between the sum of squared lower-triangle correlations of the current and previous iterations, below which the algorithm will stop trying to find better transformations |

The function starts by estimating the Box-Cox transformation on each variable and returns a new matrix where each variable was optimally, non-linearly transformed against all of the other original variables in the provided data matrix. Then, if that set of transformations resulted in a greater sum of squared lower-triangle correlations among the transformed variables (with respect to the original data), the algorithm estimates another set of Box-Cox transformations on the data transformed in the previous step. The algorithm continues until it reaches the maximum number of iteration or the sum of squared lower-triangle correlations converges.

`out` |
A matrix of transformed variables of the same dimension as the original input data |

Dave Armstrong

Box, G. E. P. and Cox, D. R. (1964) An analysis of transformations. *JRSS B* **26**: 211–246.

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icpsrmsda documentation built on May 31, 2017, 1:53 a.m.

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