Description Usage Arguments Value Author(s) Examples
function to estimate AUC and compute standard error of this estimate
1 2 3 4 5 6 | estimateAUCandStdErr(
imputedData,
timePoints,
isMultiplicative = FALSE,
na.rm = FALSE
)
|
imputedData |
numeric matrix or data frame of size n by J (n the sample size and J the number of time points) |
timePoints |
vector of time points |
isMultiplicative |
logical variable indicating whether an additive error model (FALSE) or a multiplicative error model (TRUE) should be used |
na.rm |
logical variable indicating whether the rows with missing values should be ignored or not. |
vector of length 2 with estimated AUC and its standard error
Vahid Nassiri, Helen Yvette Barnett
1 2 3 4 5 6 7 8 9 | # 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))
# Impute the data with BLOQ's with one of the provided methods,
# for example, here we use ROS
imputedDataROS <- imputeROS(genDataFixedEffects, 0.1)
# estimate AUC and its standard error
estimateAUCandStdErr(imputedDataROS,seq(0.5,3,0.5))
|
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