impute_rand: Imputation of peptides with a random value.
In imp4p: Imputation for Proteomics

Description Usage Arguments Details Value Author(s) Examples

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 for the specific peptide 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>

#Simulating data
res.sim=sim.data(nb.pept=2000,nb.miss=600);

#Imputation of the simulated data set with random values
data.rand=impute.rand(res.sim$dat.obs,res.sim$conditions);