# Calculate the minimum error to assume in order to pass the variance test

### Description

This function uses `optimize`

in order to iteratively find the
smallest relative error still resulting in passing the chi-squared test
as defined in the FOCUS kinetics report from 2006.

### Usage

1 | ```
mkinerrmin(fit, alpha = 0.05)
``` |

### Arguments

`fit` |
an object of class |

`alpha` |
The confidence level chosen for the chi-squared test. |

### Details

This function is used internally by `summary.mkinfit`

.

### Value

A dataframe with the following components:

`err.min` |
The relative error, expressed as a fraction. |

`n.optim` |
The number of optimised parameters attributed to the data series. |

`df` |
The number of remaining degrees of freedom for the chi2 error level calculations. Note that mean values are used for the chi2 statistic and therefore every time point with observed values in the series only counts one time. |

The dataframe has one row for the total dataset and one further row for each observed state variable in the model.

### References

FOCUS (2006) “Guidance Document on Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://focus.jrc.ec.europa.eu/dk

### Examples

1 2 3 4 5 6 7 8 | ```
SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
m1 = list(type = "SFO"),
use_of_ff = "max")
fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_D), 4)
fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_E), 4)
``` |