simulateEngine: simulateEngine

In BeanLabASU/metabimpute: Missingness Evaluation, Simulation and Imputation in Metabolomics

simulateEngine

Description

This function is going to be the main engine that actually runs the simulations and tracks the data and the error calculations at differing levels of missingness, ratios of missingness and iterations of the sim.

Usage

simulateEngine(
  data,
  simIter,
  simMissIter,
  missMax,
  missMin,
  missInc,
  missRatios,
  methodsImp,
  methodsEval,
  simulate_Data = T,
  reps
)

Arguments

data	the original data set
simIter	this is the number of unique simulation matrices to be made from the covMat of the origData
simMissIter	this is the number of simulated missing data matrices to be created for a given missingness proportion
missMax	the highest amount of missingness wanted in the sim
missMin	the min amount of missingness wanted in the sim
missInc	the amount of missingness to increment by
missRatios	this is a list of triplets defining the ratio of MCAR, MAR, MNAR in form c(0,0.5, 0.5) would specify that we want a split of 50-50 MAR MNAR for each missingness level
methodsImp	the imputation methods to be utilized.
medthodsEval	the methods of error evaluation to be utilized.

Value

a collection of dataframes containing the errors of imp methods for each eval method listed by proportion of missingness and total percent of missingness.

BeanLabASU/metabimpute documentation built on Feb. 5, 2023, 11:41 p.m.