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<title>Datasets on aerobic soil metabolism of 2,4,5-T in six soils — mccall81_245T • mkin</title>
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<h1>Datasets on aerobic soil metabolism of 2,4,5-T in six soils</h1>
</div>
<p>Time course of 2,4,5-trichlorophenoxyacetic acid, and the corresponding
2,4,5-trichlorophenol and 2,4,5-trichloroanisole as recovered in diethylether
extracts.</p>
<pre><span class='no'>mccall81_245T</span></pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>A dataframe containing the following variables.
<dl class='dl-horizontal'>
<dt><code>name</code></dt><dd>the name of the compound observed. Note that T245 is used as
an acronym for 2,4,5-T. T245 is a legitimate object name
in R, which is necessary for specifying models using
<code><a href='mkinmod.html'>mkinmod</a></code>.</dd>
<dt><code>time</code></dt><dd>a numeric vector containing sampling times in days after
treatment</dd>
<dt><code>value</code></dt><dd>a numeric vector containing concentrations in percent of applied radioactivity</dd>
<dt><code>soil</code></dt><dd>a factor containing the name of the soil</dd>
</dl></p>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>McCall P, Vrona SA, Kelley SS (1981) Fate of uniformly carbon-14 ring labeled 2,4,5-Trichlorophenoxyacetic acid and 2,4-dichlorophenoxyacetic acid. J Agric Chem 29, 100-107
<a href = 'http://dx.doi.org/10.1021/jf00103a026'>http://dx.doi.org/10.1021/jf00103a026</a></p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'> <span class='no'>SFO_SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>T245</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"phenol"</span>),
<span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"anisole"</span>),
<span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'>list</span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
<span class='no'>fit.1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>))</div><div class='output co'>#> Model cost at call 1 : 13655.2
#> Model cost at call 2 : 13655.2
#> Model cost at call 8 : 9191.59
#> Model cost at call 16 : 5663.584
#> Model cost at call 17 : 5663.582
#> Model cost at call 18 : 5663.566
#> Model cost at call 19 : 5663.55
#> Model cost at call 23 : 3035.567
#> Model cost at call 24 : 3035.566
#> Model cost at call 26 : 3035.561
#> Model cost at call 27 : 3035.559
#> Model cost at call 28 : 3035.558
#> Model cost at call 30 : 2252.97
#> Model cost at call 31 : 2252.965
#> Model cost at call 32 : 2252.965
#> Model cost at call 36 : 2252.958
#> Model cost at call 37 : 1220.201
#> Model cost at call 38 : 1220.2
#> Model cost at call 39 : 1220.199
#> Model cost at call 41 : 1220.183
#> Model cost at call 45 : 507.1358
#> Model cost at call 48 : 507.1353
#> Model cost at call 52 : 198.5695
#> Model cost at call 55 : 198.5694
#> Model cost at call 60 : 175.8239
#> Model cost at call 63 : 175.8239
#> Model cost at call 67 : 170.6316
#> Model cost at call 69 : 170.6316
#> Model cost at call 74 : 165.881
#> Model cost at call 77 : 165.881
#> Model cost at call 81 : 161.6423
#> Model cost at call 84 : 161.6423
#> Model cost at call 88 : 158.2953
#> Model cost at call 91 : 158.2953
#> Model cost at call 95 : 156.7311
#> Model cost at call 97 : 156.731
#> Model cost at call 103 : 155.998
#> Model cost at call 106 : 155.998
#> Model cost at call 110 : 155.6263
#> Model cost at call 113 : 155.6262
#> Model cost at call 116 : 155.6262
#> Model cost at call 117 : 155.1202
#> Model cost at call 120 : 155.1202
#> Model cost at call 124 : 154.4133
#> Model cost at call 125 : 153.8489
#> Model cost at call 126 : 152.7413
#> Model cost at call 129 : 152.7413
#> Model cost at call 132 : 152.7413
#> Model cost at call 133 : 150.3995
#> Model cost at call 134 : 148.7256
#> Model cost at call 135 : 147.2618
#> Model cost at call 137 : 147.2618
#> Model cost at call 139 : 147.2618
#> Model cost at call 142 : 145.115
#> Model cost at call 144 : 145.115
#> Model cost at call 145 : 145.115
#> Model cost at call 146 : 145.115
#> Model cost at call 147 : 145.115
#> Model cost at call 150 : 143.7878
#> Model cost at call 157 : 142.8531
#> Model cost at call 160 : 142.8531
#> Model cost at call 164 : 142.7325
#> Model cost at call 167 : 142.7325
#> Model cost at call 169 : 142.7325
#> Model cost at call 172 : 141.8393
#> Model cost at call 173 : 141.8393
#> Model cost at call 174 : 141.8393
#> Model cost at call 179 : 141.3446
#> Model cost at call 180 : 141.3446
#> Model cost at call 181 : 141.3446
#> Model cost at call 186 : 141.1111
#> Model cost at call 193 : 140.7786
#> Model cost at call 200 : 140.451
#> Model cost at call 207 : 140.1395
#> Model cost at call 214 : 139.7903
#> Model cost at call 221 : 139.5476
#> Model cost at call 228 : 139.4441
#> Model cost at call 235 : 139.3204
#> Model cost at call 242 : 139.2508
#> Model cost at call 249 : 139.1891
#> Model cost at call 256 : 139.1561
#> Model cost at call 263 : 139.137
#> Model cost at call 270 : 139.1278
#> Model cost at call 271 : 139.1278
#> Model cost at call 279 : 139.1211
#> Model cost at call 280 : 139.1211
#> Model cost at call 281 : 139.1211
#> Model cost at call 282 : 139.1211
#> Model cost at call 286 : 139.1179
#> Model cost at call 287 : 139.1179
#> Model cost at call 291 : 139.1179
#> Model cost at call 294 : 139.1179
#> Model cost at call 298 : 139.1158
#> Model cost at call 299 : 139.1158
#> Model cost at call 302 : 139.1158
#> Model cost at call 310 : 139.1148
#> Model cost at call 311 : 139.1148
#> Model cost at call 315 : 139.1148
#> Model cost at call 322 : 139.1143
#> Model cost at call 323 : 139.1143
#> Model cost at call 326 : 139.1143
#> Model cost at call 334 : 139.114
#> Model cost at call 335 : 139.114
#> Model cost at call 338 : 139.114
#> Model cost at call 339 : 139.114
#> Model cost at call 346 : 139.1139
#> Model cost at call 347 : 139.1139
#> Model cost at call 350 : 139.1139
#> Model cost at call 351 : 139.1139
#> Model cost at call 358 : 139.1139
#> Model cost at call 359 : 139.1139
#> Model cost at call 362 : 139.1139
#> Model cost at call 363 : 139.1139
#> Model cost at call 370 : 139.1138
#> Model cost at call 382 : 139.1138
#> Model cost at call 390 : 139.1138
#> Model cost at call 395 : 139.1138
#> Model cost at call 403 : 139.1138
#> Model cost at call 408 : 139.1138
#> Model cost at call 416 : 139.1138
#> Model cost at call 421 : 139.1138
#> Model cost at call 429 : 139.1138
#> Model cost at call 434 : 139.1138
#> Model cost at call 442 : 139.1138
#> Model cost at call 447 : 139.1138
#> Model cost at call 455 : 139.1138
#> Model cost at call 460 : 139.1138
#> Model cost at call 468 : 139.1138
#> Model cost at call 473 : 139.1138
#> Model cost at call 481 : 139.1138
#> Model cost at call 486 : 139.1138
#> Model cost at call 494 : 139.1138
#> Model cost at call 499 : 139.1138
#> Model cost at call 507 : 139.1138
#> Model cost at call 512 : 139.1138
#> Model cost at call 519 : 139.1138
#> Model cost at call 520 : 139.1138
#> Model cost at call 525 : 139.1138
#> Model cost at call 538 : 139.1138
#> Model cost at call 545 : 139.1138
#> Model cost at call 546 : 139.1138
#> Model cost at call 551 : 139.1138
#> Model cost at call 558 : 139.1138
#> Model cost at call 559 : 139.1138
#> Model cost at call 564 : 139.1138
#> Model cost at call 571 : 139.1138
#> Model cost at call 580 : 139.1138
#> Model cost at call 588 : 139.1138 </div><div class='output co'>#> <span class='warning'>Warning: Optimisation by method Port did not converge.</span>
#> <span class='warning'>Convergence code is 1</span></div><div class='input'> <span class='fu'>summary</span>(<span class='no'>fit.1</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.44.9000
#> R version: 3.3.2
#> Date of fit: Fri Nov 18 15:19:33 2016
#> Date of summary: Fri Nov 18 15:19:33 2016
#>
#>
#> Warning: Optimisation by method Port did not converge.
#> Convergence code is 1
#>
#>
#> Equations:
#> d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
#> d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol -
#> k_phenol_anisole * phenol
#> d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole
#>
#> Model predictions using solution type deSolve
#>
#> Fitted with method Port using 590 model solutions performed in 3.45 s
#>
#> Weighting: none
#>
#> Starting values for parameters to be optimised:
#> value type
#> T245_0 100.9000 state
#> k_T245_sink 0.1000 deparm
#> k_T245_phenol 0.1001 deparm
#> k_phenol_sink 0.1002 deparm
#> k_phenol_anisole 0.1003 deparm
#> k_anisole_sink 0.1004 deparm
#>
#> Starting values for the transformed parameters actually optimised:
#> value lower upper
#> T245_0 100.900000 -Inf Inf
#> log_k_T245_sink -2.302585 -Inf Inf
#> log_k_T245_phenol -2.301586 -Inf Inf
#> log_k_phenol_sink -2.300587 -Inf Inf
#> log_k_phenol_anisole -2.299590 -Inf Inf
#> log_k_anisole_sink -2.298593 -Inf Inf
#>
#> Fixed parameter values:
#> value type
#> phenol_0 0 state
#> anisole_0 0 state
#>
#> Optimised, transformed parameters with symmetric confidence intervals:
#> Estimate Std. Error Lower Upper
#> T245_0 103.9000 NA NA NA
#> log_k_T245_sink -4.1130 NA NA NA
#> log_k_T245_phenol -3.6120 NA NA NA
#> log_k_phenol_sink -26.3900 NA NA NA
#> log_k_phenol_anisole -0.9037 NA NA NA
#> log_k_anisole_sink -5.0090 NA NA NA
#>
#> Parameter correlation:</div><div class='output co'>#> <span class='warning'>Warning: Could not estimate covariance matrix; singular system:</span></div><div class='output co'>#> Could not estimate covariance matrix; singular system:
#>
#> Residual standard error: 2.78 on 18 degrees of freedom
#>
#> 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
#> T245_0 1.039e+02 4.282e+01 7.236e-20 NA NA
#> k_T245_sink 1.636e-02 8.901e-01 1.926e-01 NA NA
#> k_T245_phenol 2.701e-02 1.504e+00 7.498e-02 NA NA
#> k_phenol_sink 3.457e-12 1.230e-11 5.000e-01 NA NA
#> k_phenol_anisole 4.051e-01 2.518e+00 1.075e-02 NA NA
#> k_anisole_sink 6.679e-03 8.146e+00 9.469e-08 NA NA
#>
#> Chi2 error levels in percent:
#> err.min n.optim df
#> All data 10.070 6 16
#> T245 7.908 3 5
#> phenol 106.445 2 5
#> anisole 5.379 1 6
#>
#> Resulting formation fractions:
#> ff
#> T245_sink 3.772e-01
#> T245_phenol 6.228e-01
#> phenol_sink 8.534e-12
#> phenol_anisole 1.000e+00
#> anisole_sink 1.000e+00
#>
#> Estimated disappearance times:
#> DT50 DT90
#> T245 15.982 53.091
#> phenol 1.711 5.685
#> anisole 103.784 344.763</div><div class='input'>
<span class='co'># No covariance matrix and k_phenol_sink is really small, therefore fix it to zero</span>
<span class='no'>fit.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'>subset</span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>),
<span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='kw'>k_phenol_sink</span> <span class='kw'>=</span> <span class='fl'>0</span>),
<span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'>summary</span>(<span class='no'>fit.2</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> mkin version: 0.9.44.9000
#> R version: 3.3.2
#> Date of fit: Fri Nov 18 15:19:35 2016
#> Date of summary: Fri Nov 18 15:19:35 2016
#>
#> Equations:
#> d_T245/dt = - k_T245_sink * T245 - k_T245_phenol * T245
#> d_phenol/dt = + k_T245_phenol * T245 - k_phenol_sink * phenol -
#> k_phenol_anisole * phenol
#> d_anisole/dt = + k_phenol_anisole * phenol - k_anisole_sink * anisole
#>
#> Model predictions using solution type deSolve
#>
#> Fitted with method Port using 246 model solutions performed in 1.477 s
#>
#> Weighting: none
#>
#> Starting values for parameters to be optimised:
#> value type
#> T245_0 100.9000 state
#> k_T245_sink 0.1000 deparm
#> k_T245_phenol 0.1001 deparm
#> k_phenol_anisole 0.1002 deparm
#> k_anisole_sink 0.1003 deparm
#>
#> Starting values for the transformed parameters actually optimised:
#> value lower upper
#> T245_0 100.900000 -Inf Inf
#> log_k_T245_sink -2.302585 -Inf Inf
#> log_k_T245_phenol -2.301586 -Inf Inf
#> log_k_phenol_anisole -2.300587 -Inf Inf
#> log_k_anisole_sink -2.299590 -Inf Inf
#>
#> Fixed parameter values:
#> value type
#> phenol_0 0 state
#> anisole_0 0 state
#> k_phenol_sink 0 deparm
#>
#> Optimised, transformed parameters with symmetric confidence intervals:
#> Estimate Std. Error Lower Upper
#> T245_0 103.9000 2.35200 98.930 108.8000
#> log_k_T245_sink -4.1130 0.13250 -4.390 -3.8350
#> log_k_T245_phenol -3.6120 0.05002 -3.716 -3.5070
#> log_k_phenol_anisole -0.9037 0.30580 -1.544 -0.2637
#> log_k_anisole_sink -5.0090 0.11180 -5.243 -4.7750
#>
#> Parameter correlation:
#> T245_0 log_k_T245_sink log_k_T245_phenol
#> T245_0 1.00000 0.63761 -0.1742
#> log_k_T245_sink 0.63761 1.00000 -0.3831
#> log_k_T245_phenol -0.17416 -0.38313 1.0000
#> log_k_phenol_anisole -0.05948 0.08745 -0.3047
#> log_k_anisole_sink -0.16208 -0.60469 0.5227
#> log_k_phenol_anisole log_k_anisole_sink
#> T245_0 -0.05948 -0.1621
#> log_k_T245_sink 0.08745 -0.6047
#> log_k_T245_phenol -0.30470 0.5227
#> log_k_phenol_anisole 1.00000 -0.1774
#> log_k_anisole_sink -0.17744 1.0000
#>
#> Residual standard error: 2.706 on 19 degrees of freedom
#>
#> 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
#> T245_0 1.039e+02 44.160 6.462e-21 98.930000 108.80000
#> k_T245_sink 1.636e-02 7.545 1.978e-07 0.012400 0.02159
#> k_T245_phenol 2.701e-02 19.990 1.607e-14 0.024320 0.02999
#> k_phenol_anisole 4.051e-01 3.270 2.014e-03 0.213600 0.76820
#> k_anisole_sink 6.679e-03 8.942 1.544e-08 0.005285 0.00844
#>
#> Chi2 error levels in percent:
#> err.min n.optim df
#> All data 9.831 5 17
#> T245 7.908 3 5
#> phenol 99.808 1 6
#> anisole 5.379 1 6
#>
#> Resulting formation fractions:
#> ff
#> T245_sink 0.3772
#> T245_phenol 0.6228
#> phenol_anisole 1.0000
#> phenol_sink 0.0000
#> anisole_sink 1.0000
#>
#> Estimated disappearance times:
#> DT50 DT90
#> T245 15.982 53.091
#> phenol 1.711 5.685
#> anisole 103.784 344.763</div></pre>
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<h2>Contents</h2>
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<li><a href="#format">Format</a></li>
<li><a href="#source">Source</a></li>
<li><a href="#examples">Examples</a></li>
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