imputeROS: imputing BLOQ's using regression on order statistics

In BLOQ: Impute and Analyze Data with BLOQ Observations

Description

function to impute BLOQ's with regression on order statistics (ROS) approach.

Usage

imputeROS(inputData, LOQ, isMultiplicative = FALSE, 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
isMultiplicative	logical variable indicating whether an additive error model (FALSE) or a multiplicative model (TRUE) should be used
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))
imputeROS(genDataFixedEffects, 0.1)

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