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

View source: R/optimizeParameters.R

`optimizeParameters`

returns the optimal parameters to be
used in the removal of unwanted variation procedure when using simulated data.

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`Y` |
An object of the class |

`kW.hat` |
A vector of integers for |

`nu.hat` |
A vector of values for |

`nc_index` |
A vector of indices of the negative controls
used in |

`methods` |
The method used for quality assessment;
if |

`cpus` |
A number specifiying how many workers to use for parallel computing. |

`parallel` |
Logical: if |

`check.input` |
Logical; if |

The simulated data is cleaned using removal of unwanted variation with all combinations of the input parameters. The quality of each cleaning is judged by the Frobenius Norm of the correlation as estimated from the cleaned data and the known data or the percentage of correlations with estimated to have the wrong sign.

`optimizeParameters`

returns output of the class
`optimizeParameters`

.
An object of class `optimizeParameters`

is a list containing the
following components:

`All.results`

A matrix of output of the quality assessment for all combinations of input parameters.

`Compare.raw`

A vector of the quality assessment for the uncorrected data.

`Optimal.parameter`

A matrix or a vector giving the optimal parameter combination.

Saskia Freytag

`assessQuality`

, `RUVNaiveRidge`

,
`funcPara`

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