imputeBLOQ {BLOQ}

impute BLOQ's with various methods

Description

function to impute BLOQ's. The user can define column-specific methods to impute the BLOQ's.

Usage

imputeBLOQ(inputData, LOQ, imputationMethod, progressPrint = FALSE, ...)

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

imputationMethod

could be a single string or a vector of strings with the same length as the number of time points (ncol(inputData)). If it is left blank, then the imputation is done using kernel density estimation method for the columns with at least one non-BLOQ component. For all the rest (only BLOQ) the constant imputation is used. The allowed values are "constant", "ros", "kernel", "cml" corresponding to constant imputation, imputing using regression on order statistics, imputing using kernel density estimator, and imputing using censored maximum likelihood, respectively.

progressPrint

logical variable indicating whether the imputation progress should be printed or not.

...

any other argument which should be changed according to the input arguments regarding the functions corresponding to different imputation methods.

Value

a list with two components: imputed dataset, and the methods used to impute each column.

Author(s)

Vahid Nassiri, Helen Yvette Barnett

Examples

set.seed(111)
inputData <- simulateBealModelFixedEffects(10, 0.693,1, 1, seq(0.5,3,0.5))
LOQ = 0.125
imputeBLOQ(inputData, LOQ, 
		imputationMethod = c("cml", "ros", "kernel","constant", "constant", "constant"), 
		maxIter = 500, isMultiplicative = TRUE, constantValue = LOQ)
imputeBLOQ(inputData, LOQ, maxIter = 500, isMultiplicative = TRUE, 
constantValue = LOQ/5, epsilon = 1e-04)