# Example evaluation of FOCUS Example Dataset D

#### Johannes Ranke

#### Last change 31 January 2019 (rebuilt 2025-05-13)

This is just a very simple vignette showing how to fit a degradation model for a parent compound with one transformation product using `mkin`. After loading the library we look at the data. We have observed concentrations in the column named `value` at the times specified in column `time` for the two observed variables named `parent` and `m1`.

    library(mkin, quietly = TRUE)
    print(FOCUS_2006_D)

    ##      name time  value
    ## 1  parent    0  99.46
    ## 2  parent    0 102.04
    ## 3  parent    1  93.50
    ## 4  parent    1  92.50
    ## 5  parent    3  63.23
    ## 6  parent    3  68.99
    ## 7  parent    7  52.32
    ## 8  parent    7  55.13
    ## 9  parent   14  27.27
    ## 10 parent   14  26.64
    ## 11 parent   21  11.50
    ## 12 parent   21  11.64
    ## 13 parent   35   2.85
    ## 14 parent   35   2.91
    ## 15 parent   50   0.69
    ## 16 parent   50   0.63
    ## 17 parent   75   0.05
    ## 18 parent   75   0.06
    ## 19 parent  100     NA
    ## 20 parent  100     NA
    ## 21 parent  120     NA
    ## 22 parent  120     NA
    ## 23     m1    0   0.00
    ## 24     m1    0   0.00
    ## 25     m1    1   4.84
    ## 26     m1    1   5.64
    ## 27     m1    3  12.91
    ## 28     m1    3  12.96
    ## 29     m1    7  22.97
    ## 30     m1    7  24.47
    ## 31     m1   14  41.69
    ## 32     m1   14  33.21
    ## 33     m1   21  44.37
    ## 34     m1   21  46.44
    ## 35     m1   35  41.22
    ## 36     m1   35  37.95
    ## 37     m1   50  41.19
    ## 38     m1   50  40.01
    ## 39     m1   75  40.09
    ## 40     m1   75  33.85
    ## 41     m1  100  31.04
    ## 42     m1  100  33.13
    ## 43     m1  120  25.15
    ## 44     m1  120  33.31

Next we specify the degradation model: The parent compound degrades with simple first-order kinetics (SFO) to one metabolite named m1, which also degrades with SFO kinetics.

The call to mkinmod returns a degradation model. The differential equations represented in R code can be found in the character vector `$diffs` of the `mkinmod` object. If a C compiler (gcc) is installed and functional, the differential equation model will be compiled from auto-generated C code.

    SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))

    ## Temporary DLL for differentials generated and loaded

    print(SFO_SFO$diffs)

    ##                                                    parent
    ##                          "d_parent = - k_parent * parent"
    ##                                                        m1
    ## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"

We do the fitting without progress report (`quiet = TRUE`).

    fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)

    ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
    ## value of zero were removed from the data

A plot of the fit including a residual plot for both observed variables is obtained using the `plot_sep` method for `mkinfit` objects, which shows separate graphs for all compounds and their residuals.

    plot_sep(fit, lpos = c("topright", "bottomright"))

Confidence intervals for the parameter estimates are obtained using the `mkinparplot` function.

    mkinparplot(fit)

A comprehensive report of the results is obtained using the `summary` method for `mkinfit` objects.

    summary(fit)

    ## mkin version used for fitting:    1.2.10
    ## R version used for fitting:       4.5.0
    ## Date of fit:     Tue May 13 07:13:20 2025
    ## Date of summary: Tue May 13 07:13:20 2025
    ##
    ## Equations:
    ## d_parent/dt = - k_parent * parent
    ## d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
    ##
    ## Model predictions using solution type analytical
    ##
    ## Fitted using 401 model solutions performed in 0.053 s
    ##
    ## Error model: Constant variance
    ##
    ## Error model algorithm: OLS
    ##
    ## Starting values for parameters to be optimised:
    ##                   value   type
    ## parent_0       100.7500  state
    ## k_parent         0.1000 deparm
    ## k_m1             0.1001 deparm
    ## f_parent_to_m1   0.5000 deparm
    ##
    ## Starting values for the transformed parameters actually optimised:
    ##                      value lower upper
    ## parent_0        100.750000  -Inf   Inf
    ## log_k_parent     -2.302585  -Inf   Inf
    ## log_k_m1         -2.301586  -Inf   Inf
    ## f_parent_qlogis   0.000000  -Inf   Inf
    ##
    ## Fixed parameter values:
    ##      value  type
    ## m1_0     0 state
    ##
    ##
    ## Warning(s):
    ## Observations with value of zero were removed from the data
    ##
    ## Results:
    ##
    ##        AIC      BIC    logLik
    ##   204.4486 212.6365 -97.22429
    ##
    ## Optimised, transformed parameters with symmetric confidence intervals:
    ##                 Estimate Std. Error   Lower    Upper
    ## parent_0        99.60000    1.57000 96.4000 102.8000
    ## log_k_parent    -2.31600    0.04087 -2.3990  -2.2330
    ## log_k_m1        -5.24700    0.13320 -5.5180  -4.9770
    ## f_parent_qlogis  0.05792    0.08926 -0.1237   0.2395
    ## sigma            3.12600    0.35850  2.3960   3.8550
    ##
    ## Parameter correlation:
    ##                   parent_0 log_k_parent   log_k_m1 f_parent_qlogis      sigma
    ## parent_0         1.000e+00    5.174e-01 -1.688e-01      -5.471e-01 -1.172e-06
    ## log_k_parent     5.174e-01    1.000e+00 -3.263e-01      -5.426e-01 -8.479e-07
    ## log_k_m1        -1.688e-01   -3.263e-01  1.000e+00       7.478e-01  8.211e-07
    ## f_parent_qlogis -5.471e-01   -5.426e-01  7.478e-01       1.000e+00  1.305e-06
    ## sigma           -1.172e-06   -8.479e-07  8.211e-07       1.305e-06  1.000e+00
    ##
    ## Backtransformed parameters:
    ## Confidence intervals for internally transformed parameters are asymmetric.
    ## t-test (unrealistically) based on the assumption of normal distribution
    ## for estimators of untransformed parameters.
    ##                 Estimate t value    Pr(>t)     Lower     Upper
    ## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
    ## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
    ## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
    ## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
    ## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
    ##
    ## FOCUS Chi2 error levels in percent:
    ##          err.min n.optim df
    ## All data   6.398       4 15
    ## parent     6.459       2  7
    ## m1         4.690       2  8
    ##
    ## Resulting formation fractions:
    ##                 ff
    ## parent_m1   0.5145
    ## parent_sink 0.4855
    ##
    ## Estimated disappearance times:
    ##           DT50   DT90
    ## parent   7.023  23.33
    ## m1     131.761 437.70
    ##
    ## Data:
    ##  time variable observed predicted   residual
    ##     0   parent    99.46  99.59848 -1.385e-01
    ##     0   parent   102.04  99.59848  2.442e+00
    ##     1   parent    93.50  90.23787  3.262e+00
    ##     1   parent    92.50  90.23787  2.262e+00
    ##     3   parent    63.23  74.07319 -1.084e+01
    ##     3   parent    68.99  74.07319 -5.083e+00
    ##     7   parent    52.32  49.91207  2.408e+00
    ##     7   parent    55.13  49.91207  5.218e+00
    ##    14   parent    27.27  25.01258  2.257e+00
    ##    14   parent    26.64  25.01258  1.627e+00
    ##    21   parent    11.50  12.53462 -1.035e+00
    ##    21   parent    11.64  12.53462 -8.946e-01
    ##    35   parent     2.85   3.14787 -2.979e-01
    ##    35   parent     2.91   3.14787 -2.379e-01
    ##    50   parent     0.69   0.71624 -2.624e-02
    ##    50   parent     0.63   0.71624 -8.624e-02
    ##    75   parent     0.05   0.06074 -1.074e-02
    ##    75   parent     0.06   0.06074 -7.382e-04
    ##     1       m1     4.84   4.80296  3.704e-02
    ##     1       m1     5.64   4.80296  8.370e-01
    ##     3       m1    12.91  13.02400 -1.140e-01
    ##     3       m1    12.96  13.02400 -6.400e-02
    ##     7       m1    22.97  25.04476 -2.075e+00
    ##     7       m1    24.47  25.04476 -5.748e-01
    ##    14       m1    41.69  36.69003  5.000e+00
    ##    14       m1    33.21  36.69003 -3.480e+00
    ##    21       m1    44.37  41.65310  2.717e+00
    ##    21       m1    46.44  41.65310  4.787e+00
    ##    35       m1    41.22  43.31313 -2.093e+00
    ##    35       m1    37.95  43.31313 -5.363e+00
    ##    50       m1    41.19  41.21832 -2.832e-02
    ##    50       m1    40.01  41.21832 -1.208e+00
    ##    75       m1    40.09  36.44704  3.643e+00
    ##    75       m1    33.85  36.44704 -2.597e+00
    ##   100       m1    31.04  31.98162 -9.416e-01
    ##   100       m1    33.13  31.98162  1.148e+00
    ##   120       m1    25.15  28.78984 -3.640e+00
    ##   120       m1    33.31  28.78984  4.520e+00