estimateAUCwithPairwiseCML {BLOQ} | R Documentation |
function to estimate mean and and covariance matrix of censored data using a full censored maximum likelihood approach via fitting all possible pairs, then use these estimates for estimating AUC and its standard error
estimateAUCwithPairwiseCML( inputData, LOQ, timePoints, isMultiplicative = FALSE, onlyFitCML = FALSE, optimizationMethod = NULL, CMLcontrol = NULL, na.rm = TRUE )
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. |
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 |
na.rm |
logical variable indicating whether the lines with missing values should be ignored (TRUE, default) or not (FALSE). Note that, it will be applied for the sub-datasets regarding each pair. |
a list with three components: output of maxLik function, estimated parameters (mean vector and the covariance matrix) using censored maximum likelihood, and estimated AUC and its standard error.
Vahid Nassiri, Helen Yvette Barnett
# generate data from Beal model with only fixed effects set.seed(111) genDataFixedEffects <- simulateBealModelFixedEffects(10, 0.693, 1, 1, seq(0.5,3,1.5)) estimateAUCwithPairwiseCML(genDataFixedEffects, 0.1, seq(0.5,3,1.5))