BLOQ (version 0.1-1)

estimateAUCwithCMLperTimePoint: estimate AUC with censored maximum likelihood per time point

Description

function to estimate mean and standard error of each column of data with BLOQ's using a censored maximum likelihood (CML) approach, then use these estimates for estimating AUC and its standard error

Usage

estimateAUCwithCMLperTimePoint(
  inputData,
  LOQ,
  timePoints,
  isMultiplicative = FALSE,
  onlyFitCML = FALSE,
  printCMLmessage = TRUE,
  optimizationMethod = NULL,
  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

timePoints

vector of time points

isMultiplicative

logical variable indicating whether an additive error model (FALSE) or a multiplicative error model (TRUE) should be used

onlyFitCML

logical variable with FALSE as default, if TRUE only the censored maximum likelihood estimates will be calculated

printCMLmessage

logical variable with TRUE as default, if TRUE then messages regarding the convergence status of censored log-likelihood maximization will be printed.

optimizationMethod

single string specifying the method to be used for optimizing the log-likelihood, the default is NULL that allows the function to decide the about the best method. Otherwise, one can select among choices available via R package maxLik: "NR" (for Newton-Raphson), "BFGS" (for Broyden-Fletcher-Goldfarb-Shanno), "BFGSR" (for the BFGS algorithm implemented in R), "BHHH" (for Berndt-Hall-Hall-Hausman), "SANN" (for Simulated ANNealing), "CG" (for Conjugate Gradients), or "NM" (for Nelder-Mead). Lower-case letters (such as "nr" for Newton-Raphson) are allowed.

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

a list with three components: output of maxLik function, estimated parameters for each column using censored maximum likelihood, and estimated AUC and its standard error.

See Also

maxLik

Examples

# NOT RUN {
# 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))
# Multiplicative error model
estimateAUCwithCMLperTimePoint(genDataFixedEffects, 0.1, seq(0.5,3,0.5), TRUE)
# }