From 6476f5f49b373cd4cf05f2e73389df83e437d597 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 13 Feb 2025 16:30:31 +0100 Subject: Axis legend formatting, update vignettes --- docs/dev/reference/mkinerrmin.html | 210 ------------------------------------- 1 file changed, 210 deletions(-) delete mode 100644 docs/dev/reference/mkinerrmin.html (limited to 'docs/dev/reference/mkinerrmin.html') diff --git a/docs/dev/reference/mkinerrmin.html b/docs/dev/reference/mkinerrmin.html deleted file mode 100644 index 3a8b9610..00000000 --- a/docs/dev/reference/mkinerrmin.html +++ /dev/null @@ -1,210 +0,0 @@ - -Calculate the minimum error to assume in order to pass the variance test — mkinerrmin • mkin - - -
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Calculate the minimum error to assume in order to pass the variance test

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

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mkinerrmin(fit, alpha = 0.05)
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Arguments

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fit
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an object of class mkinfit.

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

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Value

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A dataframe with the following components:

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err.min
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The -relative error, expressed as a fraction.

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

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df
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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.

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

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Details

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This function is used internally by summary.mkinfit.

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References

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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://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

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Examples

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-SFO_SFO = mkinmod(parent = mkinsub("SFO", to = "m1"),
-                  m1 = mkinsub("SFO"),
-                  use_of_ff = "max")
-#> Temporary DLL for differentials generated and loaded
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-fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
-#> Warning: Observations with value of zero were removed from the data
-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
-# \dontrun{
-  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
-# }
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