imputeCML {BLOQ} | R Documentation |
function to impute BLOQ's using quantiles of a normal distribution with mean and standard error estimates using censored maximum likelihood
imputeCML(
inputData,
LOQ,
isMultiplicative = FALSE,
useSeed = runif(1),
printCMLmessage = TRUE,
CMLcontrol = NULL
)
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 |
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)
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(0.5,3,0.5))
imputeCML(genDataFixedEffects, 0.1, FALSE, 1)