impute.MAR

expression matrix containing abundances with MVs 
(either peptides or proteins)

dataSet.mvs

binary vector; "1" indicates MAR/MCAR proteins

model.selector

the method to be used for MAR/MCAR missing values.
Possible values: MLE (default), SVD, KNN

method

This function performs missing values imputation under MAR/MCAR hypothesis. 
The imputation of MVs is performed for each protein containing MAR/MCAR missing values

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.

impute.MAR: imputation under MAR/MCAR hypothesis

Description

Usage

Arguments

Value