insertMVs

complete data matrix containing all measurements

original

- parameters of the threshold distribution which 
 controls the MVs rate (mean.THR should be initially set 
 such that the result of the initial thresholding, 
 in terms of no. of NAs, equals the desired total 
 missing data rate) 
- example: if one wants to generate 30
 mean.THR can be set as follows: 
 mean.THR = quantile(pepExprsData, probs = 0.3)
- sd.THR is usually set to a small value (e.g. 0.1)

mean.THR, sd.THR

percentage of MVs which are missing not at random

MNAR.rate

this function generates missing data in a complete data matrix

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.

insertMVs: Generates missing values in data.

Description

Usage

Arguments

Value