imputeCML {BLOQ} | R Documentation |

## imputing BLOQ's using censored maximum likelihood

### Description

function to impute BLOQ's using quantiles of a normal distribution with mean and standard error estimates using censored maximum likelihood

### Usage

```
imputeCML(
inputData,
LOQ,
isMultiplicative = FALSE,
useSeed = runif(1),
printCMLmessage = TRUE,
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 |

`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 |

### Value

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)

### 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))
imputeCML(genDataFixedEffects, 0.1, FALSE, 1)
```

*BLOQ*version 0.1-1 Index]