imputeKernelDensityEstimation: imputing BLOQ's using kernel density estimation

In BLOQ: Impute and Analyze Data with BLOQ Observations

Description

function to impute BLOQ observations using kernel density estimation.

Usage

imputeKernelDensityEstimation(
  inputData,
  LOQ,
  epsilon = 1e-07,
  maxIter = 1000,
  useSeed = runif(1)
)

Arguments

inputData	numeric matrix or data frame of the size n by J (n the sample size and J the number of time points) the input dataset
LOQ	scalar, limit of quantification value
epsilon	scalar with 1e-07 as default, the difference between two iterations which achieving it would stop the procedure (convergence).
maxIter	scalar, the maximum number of iterations with 1000 as default.
useSeed	scalar, set a seed to make the results reproducible, default is runif(1), it is used to randomly order the first imputed column (if the first column has any BLOQ's)

Value

the imputed dataset: a numeric matrix or data frame of the size n by J (n the sample size and J the number of time points)

Author(s)

Vahid Nassiri, Helen Yvette Barnett

Examples

# generate data from Beal model with only fixed effects
set.seed(111)
genDataFixedEffects <- simulateBealModelFixedEffects(10, 0.693,
+ 		1, 1, seq(0.5,3,0.5))
imputeKernelDensityEstimation(genDataFixedEffects, 0.1, epsilon = 1e-05)

BLOQ documentation built on July 1, 2020, 11:37 p.m.