Description Usage Arguments Details Value Author(s) Examples

This function imputes missing values by small values.

1 2 | ```
impute.pa(tab, conditions, q.min = 0.025, q.norm = 3, eps = 0,
distribution = "unif", param1 = 3, param2 = 1, R.q.min=1)
``` |

`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 |

`distribution` |
Distribution used to generated missing values. You have the choice between "unif" for the uniform distribution, "beta" for the Beta distribution or "dirac" for the Dirac distribution. Default is "unif". |

`param1` |
Parameter |

`param2` |
Parameter |

`R.q.min` |
Parameter used for the Dirac distribution. In this case, all the missing values are imputed by a single value which is equal to |

This function replaces the missing values in a column by random draws from a specified distribution. The value of `eps`

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

A list composed of :

- `tab.imp`

: the input matrix `tab`

with imputed values instead of missing values.

- `para`

: the parameters of the distribution which has been used to impute.

Quentin Giai Gianetto <[email protected]>

1 2 3 4 5 6 | ```
#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600,pi.mcar=0.2,para=0.5,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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