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

This function finds the smallest relative error still resulting in passing the chi-squared test as defined in the FOCUS kinetics report from 2006.

mkinerrmin(fit, alpha = 0.05)

Arguments

fit

an object of class mkinfit.

alpha

The confidence level chosen for the chi-squared test.

Value

A dataframe with the following components:

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

Details

This function is used internally by summary.mkinfit.

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

SFO_SFO = mkinmod(parent = mkinsub("SFO", to = "m1"),
                  m1 = mkinsub("SFO"),
                  use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_D), 4)
#>          err.min n.optim df
#> All data  0.0640       4 15
#> parent    0.0646       2  7
#> m1        0.0469       2  8
fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)
round(mkinerrmin(fit_FOCUS_E), 4)
#>          err.min n.optim df
#> All data  0.1544       4 13
#> parent    0.1659       2  7
#> m1        0.1095       2  6