imputeKernelDensityEstimation {BLOQ} | R Documentation |

## imputing BLOQ's using kernel density estimation

### Description

function to impute BLOQ observations using kernel density estimation.

### Usage

```
imputeKernelDensityEstimation(
inputData,
LOQ,
epsilon = 1e-07,
maxIter = 1000,
useSeed = runif(1)
)
```

### 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 |

`epsilon` |
scalar with 1e-07 as default, the difference between two iterations which achieving it would stop the procedure (convergence). |

`maxIter` |
scalar, the maximum number of iterations with 1000 as default. |

`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) |

### 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

### 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))
imputeKernelDensityEstimation(genDataFixedEffects, 0.1, epsilon = 1e-05)
```

[Package

*BLOQ*version 0.1-1 Index]