This function imputes missing values by small values.

1 | ```
impute.pa(tab, conditions, q.min=0, q.norm=3, eps=2)
``` |

`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. |

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

`q.min` |
A quantile value of the observed values allowing defining the maximal value which can be generated. This maximal value is defined by the quantile |

`q.norm` |
A quantile value of a normal distribution allowing defining the minimal value which can be generated. Default is 3 (the minimal value is the maximal value minus qn*median(sd(observed values)) where sd is the standard deviation of a row in a condition). |

`eps` |
A value allowing defining the maximal value which can be generated. This maximal value is defined by the quantile |

This function replaces the missing values of the rows by random draws from an uniform distribution between the defined minimal value and maximal value. The value of `eps`

can be interpreted as a minimal fold-change value above which the present/absent peptides appear.

The input matrix `tab`

with imputed values instead of missing values.

Quentin Giai Gianetto <quentin2g@yahoo.fr>

1 2 3 4 5 6 | ```
#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);
#Imputation of the simulated data set with small values
data.small.val=impute.pa(res.sim$dat.obs,res.sim$conditions);
``` |

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