For each row (peptide), this function imputes missing values by random values following a Gaussian distribution.

1 | ```
impute.rand(tab, conditions)
``` |

`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. |

For each row (peptide), this function imputes missing values by random values following a Gaussian distribution centered on the mean of the observed values in the condition and with a standard deviation equal to the first quartile of the distribution of the standard deviation the values observed for all the peptides. Rows with only missing values in a condition are not imputed (the `impute.pa`

function can be used for this purpose).

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.rand=impute.rand(res.sim$dat.obs,res.sim$conditions);
``` |

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