impute.pa2: Missing values imputation from a 'MSnSet' object

In prostarproteomics/DAPAR: Tools for the Differential Analysis of Proteins Abundance with R

Missing values imputation from a MSnSet object

Description

This method is a variation to the function impute.pa() from the package imp4p.

Usage

impute.pa2(
  tab,
  conditions,
  q.min = 0,
  q.norm = 3,
  eps = 0,
  distribution = "unif"
)

Arguments

tab	An object of class MSnSet.
conditions	A vector of conditions in the dataset
q.min	A quantile value of the observed values allowing defining the maximal value which can be generated. This maximal value is defined by the quantile q.min of the observed values distribution minus eps. Default is 0 (the maximal value is the minimum of observed values minus eps).
q.norm	A quantile value of a normal distribution allowing defining the minimal value which can be generated. Default is 3 (the minimal value is the maximal value minus qn*median(sd(observed values)) where sd is the standard deviation of a row in a condition).
eps	A value allowing defining the maximal value which can be generated. This maximal value is defined by the quantile q.min of the observed values distribution minus eps. Default is 0.
distribution	The type of distribution used. Values are unif or beta.

Value

The object obj which has been imputed

Author(s)

Thomas Burger, Samuel Wieczorek

Examples

utils::data(Exp1_R25_pept, package = "DAPARdata")
obj.imp <- wrapper.impute.pa2(Exp1_R25_pept[seq_len(100)], 
distribution = "beta")

prostarproteomics/DAPAR documentation built on Nov. 26, 2024, 6:32 p.m.