This function allows imputing data sets with a MCAR-devoted algorithm and a MNAR-devoted algorithm using probabilities that missing values are MCAR. If such a probability is superior to 0.5, then the MCAR-devoted algorithm is used, otherwise it is the MNAR-devoted algorithm.

1 2 3 | ```
impute.mix(tab, prob.MCAR, conditions, repbio=NULL, reptech=NULL, method="slsa", nknn=15,
weight=1, selec="all", ind.comp=1, progress.bar=TRUE, q=0.95)
``` |

`tab` |
A data matrix containing numeric and missing values. Each column of this matrix is assumed to correspond to an experimental sample, and each row to an identified peptide. |

`prob.MCAR` |
A matrix of probabilities that each missing value is MCAR. For instance such a matrix can be obtained from the function |

`conditions` |
A vector of factors indicating the biological condition to which each column (experimental sample) belongs. |

`repbio` |
A vector of factors indicating the biological replicate to which each column belongs. Default is NULL (no experimental design is considered). |

`reptech` |
A vector of factors indicating the technical replicate to which each column belongs. Default is NULL (no experimental design is considered). |

`method` |
The method used for imputing MCAR data. If |

`nknn` |
The number of nearest neighbours used in the SLSA algorithm (see |

`weight` |
The way of weighting in the algorithm (see |

`selec` |
A parameter to select a part of the dataset to find nearest neighbours between rows. This can be useful for big data sets (see |

`ind.comp` |
If |

`progress.bar` |
If |

`q` |
A quantile value (see |

The missing values for which `prob.MCAR`

is superior to 0.5 are imputed with either the function `impute.slsa`

or the MLE algorithm (function `impute.wrapper.MLE`

of the R package imputeLCMD). The other missing values are considered MNAR and imputed with `impute.igcda`

.

The input matrix `tab`

with imputed values instead of missing values.

Quentin Giai Gianetto <quentin2g@yahoo.fr>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=10,nb.cond=2,nb.repbio=3,
nb.sample=5,m.c=25,sd.c=2,sd.rb=0.5,sd.r=0.2);
#Fast imputation of missing values with the impute.rand algorithm
dat.rand=impute.rand(tab=res.sim$dat.obs,conditions=res.sim$condition);
#Estimation of the mixture model
res=estim.mix(tab=res.sim$dat.obs, tab.imp=dat.rand, conditions=res.sim$condition);
#Computing probabilities to be MCAR
born=estim.bound(tab=res.sim$dat.obs,conditions=res.sim$condition);
proba=prob.mcar.tab(born$tab.lower,born$tab.upper,res);
#Imputation under the assumption of MCAR and MNAR values
tabi=impute.mix(tab=res.sim$dat.obs, prob.MCAR=proba, conditions=res.sim$conditions,
repbio=res.sim$repbio, method="slsa", nknn=15, weight=1, selec="all", ind.comp=1,
progress.bar=TRUE);
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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