R: Imputing missing values by assuming that the distribution of...

impute.igcda {imp4p} | R Documentation

## Imputing missing values by assuming that the distribution of complete values is Gaussian in each column of an input matrix. This algorithm is named "Imputation under a Gaussian Complete Data Assumption" (IGCDA).

### Description

This function allows imputing missing values under the assumption that the distribution of complete values has to be Gaussian in each column.

Note that the imputed values are not necessary small values (compared to observed values).

### Usage

    impute.igcda(tab, tab.imp, conditions, q=0.95)

### Arguments

`tab` | A numeric vector or matrix with observed and missing values.
`tab.imp` | A matrix where the missing values of `tab` have been imputed under the assumption that they are all MCAR. For instance, such a matrix can be obtained by using the function `impute.slsa` of this package.
`conditions` | A vector of factors indicating the biological condition to which each column (experimental sample) belongs.
`q` | A quantile value (see Details).

### Details

The mean and variance of the Gaussian distribution are determined using a linear regression between the quantiles of the observed values `q_{obs}` and the ones of the standard normal distribution `q_{N(0,1)}`.

The quantile value is used for determining the minimum of imputed values. This minimum is determined by the minimum observed value in the dataset minus `quant_diff(q)` where `quant_diff(q)` corresponds to a quantile value of the differences between the maximum and the minimum of the observed values for all the peptides in the condition. As a result, if `q` is close to 1, `quant_diff(q)` represents an extrem value between the maximum and the minimum of the intensity values in a condition for a peptide.

### Value

The numeric input matrix with imputed values. The distribution of the intensity values in each of its columns is supposed to be Gaussian.

### Author(s)

Quentin Giai Gianetto <quentin2g@yahoo.fr>

### Examples

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

    #Imputation of missing values with a MCAR-devoted algorithm: here the slsa algorithm
    dat.slsa=impute.slsa(tab=res.sim$dat.obs,conditions=res.sim$condition,repbio=res.sim$repbio);

    #Imputation of missing values under a Gaussian assumption
    dat.gauss=impute.igcda(tab=res.sim$dat.obs, tab.imp=dat.slsa, conditions=res.sim$conditions);

[Package imp4p version 1.1 Index]