model.Selector

<p>expression matrix containing abundances with MVs 
(either peptides or proteins)</p>

dataSet.mvs

<p>- this function determines row in the data matrix affected by a MNAR missingness 
  mechanism
- it is based on the assumption that the distributions of the mean values  
  of proteins follows a normal distribution
- the method makes use of a decision function defined as a tradeoff between the empirical 
  CDF of the proteins' means and the theoretical CDF assuming that no MVs are present</p>

A collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.

Samuel Wieczorek

imputeLCMD

model.Selector: Identifies row in the data matrix affected by a MNAR missingness mechanism

Description

Usage

Arguments

Value