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

`tuneM`

can be used to determine the appropriate number
of imputed datasets needed to obtain satisfactory results
with MI-MFA.

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`object` |
an object of class |

`x` |
an object of class inheriting from |

`ncomp` |
a number of components to include in MFA. |

`Mmax` |
an integer corresponding to the maximum number of imputed datasets. See Details. |

`inc` |
integer. The increment of the sequence for the
number |

`N` |
integer. Collections of size |

`tol` |
a positive value, the tolerance used for assessing stabilization. |

`showPlot` |
logical. If |

`...` |
not currently used. |

The appropriate number of imputations can be informally determined
by carrying out MI-MFA on *N* replicate sets of *M_l*
imputations for *l = 0, 1, 2, . . . ,* with
*M_0 < M_1 < M_2 < . . . < M_max*,
until the estimate compromise configurations are stabilized.

`tuneM`

function implements such a procedure. Collections
of size `N`

are generated for each number of imputations
`M`

, with `M = seq(inc, Mmax, by = inc)`

. The stability
of the estimated MI-MFA configurations is then determined by
calculating the RV coefficient between the configurations obtained
using `M`

*_l* and `M`

*_{l+1}*
imputations.

If `showPlot = TRUE`

a plot showing the stability of the
estimated MFA configurations is displayed. The values shown are
the mean RV coefficients for the `N`

configurations as a
function of the number of imputations. Error bars represent the
standard deviation of the RV coefficients.

A list with the following components:

`stats` |
a |

`ggp` |
an object of class |

Ignacio GonzÃ¡lez, Valentin Voillet

Voillet V., Besse P., Liaubet L., Cristobal M.S., GonzÃ¡lez I.
(2016). Handling missing rows in multi-omics data integration:
Multiple Imputation in Multiple Factor Analysis framework.
*BMC Bioinformatics*, 17(40).

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