imputeCML: imputing BLOQ's using censored maximum likelihood

In BLOQ: Impute and Analyze Data with BLOQ Observations

Description

function to impute BLOQ's using quantiles of a normal distribution with mean and standard error estimates using censored maximum likelihood

Usage

imputeCML(
  inputData,
  LOQ,
  isMultiplicative = FALSE,
  useSeed = runif(1),
  printCMLmessage = TRUE,
  CMLcontrol = NULL
)

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 error 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)
printCMLmessage	logical variable with TRUE as default, if TRUE then messages regarding the convergence status of censored log-likelihood maximization will be printed.
CMLcontrol	list of arguments to control convergence of maximization algorithm. It is the same argument as control in the function maxLik in the R package maxLik

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

See Also

maxLik

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))
imputeCML(genDataFixedEffects, 0.1, FALSE, 1)

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