estimateAUCwithCMLperTimePoint {BLOQ} | R Documentation |

## 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.

### Author(s)

Vahid Nassiri, Helen Yvette Barnett

### See Also

### 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))
# Multiplicative error model
estimateAUCwithCMLperTimePoint(genDataFixedEffects, 0.1, seq(0.5,3,0.5), TRUE)
```

*BLOQ*version 0.1-1 Index]