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-rw-r--r--docs/reference/mkinfit.html133
1 files changed, 69 insertions, 64 deletions
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 9974b66b..ceac59bf 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -78,7 +78,7 @@ likelihood function." />
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mkin</a>
- <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50</span>
+ <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.50.1</span>
</span>
</div>
@@ -209,15 +209,15 @@ detection.</p></td>
<tr>
<th>parms.ini</th>
<td><p>A named vector of initial values for the parameters,
- including parameters to be optimised and potentially also fixed parameters
- as indicated by <code>fixed_parms</code>. If set to "auto", initial values for
- rate constants are set to default values. Using parameter names that are
- not in the model gives an error.</p>
+including parameters to be optimised and potentially also fixed parameters
+as indicated by <code>fixed_parms</code>. If set to "auto", initial values for
+rate constants are set to default values. Using parameter names that are
+not in the model gives an error.</p>
<p>It is possible to only specify a subset of the parameters that the model
- needs. You can use the parameter lists "bparms.ode" from a previously
- fitted model, which contains the differential equation parameters from
- this model. This works nicely if the models are nested. An example is
- given below.</p></td>
+needs. You can use the parameter lists "bparms.ode" from a previously
+fitted model, which contains the differential equation parameters from
+this model. This works nicely if the models are nested. An example is
+given below.</p></td>
</tr>
<tr>
<th>state.ini</th>
@@ -326,42 +326,44 @@ is 1e-10, much lower than in <code>lsoda</code>.</p></td>
<tr>
<th>error_model</th>
<td><p>If the error model is "const", a constant standard
- deviation is assumed.</p>
+deviation is assumed.</p>
<p>If the error model is "obs", each observed variable is assumed to have its
- own variance.</p>
+own variance.</p>
<p>If the error model is "tc" (two-component error model), a two component
- error model similar to the one described by Rocke and Lorenzato (1995) is
- used for setting up the likelihood function. Note that this model
- deviates from the model by Rocke and Lorenzato, as their model implies
- that the errors follow a lognormal distribution for large values, not a
- normal distribution as assumed by this method.</p></td>
+error model similar to the one described by Rocke and Lorenzato (1995) is
+used for setting up the likelihood function. Note that this model
+deviates from the model by Rocke and Lorenzato, as their model implies
+that the errors follow a lognormal distribution for large values, not a
+normal distribution as assumed by this method.</p></td>
</tr>
<tr>
<th>error_model_algorithm</th>
<td><p>If "auto", the selected algorithm depends on
- the error model. If the error model is "const", unweighted nonlinear
- least squares fitting ("OLS") is selected. If the error model is "obs", or
- "tc", the "d_3" algorithm is selected.</p>
-<p>The algorithm "d_3" will directly minimize the negative log-likelihood and
- - independently - also use the three step algorithm described below. The
- fit with the higher likelihood is returned.</p>
+the error model. If the error model is "const", unweighted nonlinear
+least squares fitting ("OLS") is selected. If the error model is "obs", or
+"tc", the "d_3" algorithm is selected.</p>
+<p>The algorithm "d_3" will directly minimize the negative log-likelihood and</p><ul>
+<li><p>independently - also use the three step algorithm described below. The
+fit with the higher likelihood is returned.</p></li>
+</ul>
+
<p>The algorithm "direct" will directly minimize the negative log-likelihood.</p>
<p>The algorithm "twostep" will minimize the negative log-likelihood after an
- initial unweighted least squares optimisation step.</p>
+initial unweighted least squares optimisation step.</p>
<p>The algorithm "threestep" starts with unweighted least squares, then
- optimizes only the error model using the degradation model parameters
- found, and then minimizes the negative log-likelihood with free
- degradation and error model parameters.</p>
+optimizes only the error model using the degradation model parameters
+found, and then minimizes the negative log-likelihood with free
+degradation and error model parameters.</p>
<p>The algorithm "fourstep" starts with unweighted least squares, then
- optimizes only the error model using the degradation model parameters
- found, then optimizes the degradation model again with fixed error model
- parameters, and finally minimizes the negative log-likelihood with free
- degradation and error model parameters.</p>
+optimizes only the error model using the degradation model parameters
+found, then optimizes the degradation model again with fixed error model
+parameters, and finally minimizes the negative log-likelihood with free
+degradation and error model parameters.</p>
<p>The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with
- unweighted least squares, and then iterates optimization of the error
- model parameters and subsequent optimization of the degradation model
- using those error model parameters, until the error model parameters
- converge.</p></td>
+unweighted least squares, and then iterates optimization of the error
+model parameters and subsequent optimization of the degradation model
+using those error model parameters, until the error model parameters
+converge.</p></td>
</tr>
<tr>
<th>reweight.tol</th>
@@ -383,14 +385,10 @@ the error model parameters in IRLS fits.</p></td>
</tr>
</table>
- <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
-
- <p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model
- for measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A list with "mkinfit" in the class attribute. A summary can be
- obtained by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p>
+obtained by <code><a href='summary.mkinfit.html'>summary.mkinfit</a></code>.</p>
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>Per default, parameters in the kinetic models are internally transformed in
@@ -399,33 +397,40 @@ estimators.</p>
<h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
<p>When using the "IORE" submodel for metabolites, fitting with
- "transform_rates = TRUE" (the default) often leads to failures of the
- numerical ODE solver. In this situation it may help to switch off the
- internal rate transformation.</p>
+"transform_rates = TRUE" (the default) often leads to failures of the
+numerical ODE solver. In this situation it may help to switch off the
+internal rate transformation.</p>
+ <h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
+
+ <p>Rocke DM and Lorenzato S (1995) A two-component model
+for measurement error in analytical chemistry. <em>Technometrics</em> 37(2), 176-184.</p>
+<p>Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical
+Degradation Data. <em>Environments</em> 6(12) 124
+<a href='https://doi.org/10.3390/environments6120124'>doi:10.3390/environments6120124</a>.</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>Plotting methods <code><a href='plot.mkinfit.html'>plot.mkinfit</a></code> and
- <code><a href='mkinparplot.html'>mkinparplot</a></code>.</p>
+<code><a href='mkinparplot.html'>mkinparplot</a></code>.</p>
<p>Comparisons of models fitted to the same data can be made using
- <code><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></code> by virtue of the method <code><a href='logLik.mkinfit.html'>logLik.mkinfit</a></code>.</p>
+<code><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></code> by virtue of the method <code><a href='logLik.mkinfit.html'>logLik.mkinfit</a></code>.</p>
<p>Fitting of several models to several datasets in a single call to
- <code><a href='mmkin.html'>mmkin</a></code>.</p></div>
+<code><a href='mmkin.html'>mmkin</a></code>.</p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'>
<span class='co'># Use shorthand notation for parent only degradation</span>
<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
-<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50
+<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50.1
#&gt; R version used for fitting: 4.0.0
-#&gt; Date of fit: Mon May 11 05:14:26 2020
-#&gt; Date of summary: Mon May 11 05:14:26 2020
+#&gt; Date of fit: Tue May 12 08:36:07 2020
+#&gt; Date of summary: Tue May 12 08:36:07 2020
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 222 model solutions performed in 0.043 s
+#&gt; Fitted using 222 model solutions performed in 0.047 s
#&gt;
#&gt; Error model: Constant variance
#&gt;
@@ -502,7 +507,7 @@ estimators.</p>
<span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span>
<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>,
<span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.407 0.002 0.409 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; parent_0 k_parent k_m1 f_parent_to_m1 sigma
+#&gt; 0.408 0.008 0.416 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; parent_0 k_parent k_m1 f_parent_to_m1 sigma
#&gt; 99.598483222 0.098697734 0.005260651 0.514475962 3.125503875 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#&gt; $ff
#&gt; parent_m1 parent_sink
#&gt; 0.514476 0.485524
@@ -592,7 +597,7 @@ estimators.</p>
#&gt; Sum of squared residuals at call 166: 371.2134
#&gt; Sum of squared residuals at call 168: 371.2134
#&gt; Negative log-likelihood at call 178: 97.22429</div><div class='output co'>#&gt; <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#&gt; User System verstrichen
-#&gt; 0.349 0.000 0.350 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; parent_0 k_parent k_m1 f_parent_to_m1 sigma
+#&gt; 0.350 0.001 0.351 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; parent_0 k_parent k_m1 f_parent_to_m1 sigma
#&gt; 99.598480759 0.098697739 0.005260651 0.514475958 3.125503874 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#&gt; $ff
#&gt; parent_m1 parent_sink
#&gt; 0.514476 0.485524
@@ -622,12 +627,12 @@ estimators.</p>
<span class='no'>fit.SFORB_SFO</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#&gt; <span class='warning'>Warning: Initial parameter(s) k_parent_free_sink not used in the model</span></div><div class='input'><span class='co'># }</span>
<span class='co'># \dontrun{</span>
-<span class='co'># Weighted fits, including IRLS</span>
+<span class='co'># Weighted fits, including IRLS (error_model = "obs")</span>
<span class='no'>SFO_SFO.ff</span> <span class='kw'>&lt;-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>),
- <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50
+ <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50.1
#&gt; R version used for fitting: 4.0.0
-#&gt; Date of fit: Mon May 11 05:14:31 2020
-#&gt; Date of summary: Mon May 11 05:14:31 2020
+#&gt; Date of fit: Tue May 12 08:36:12 2020
+#&gt; Date of summary: Tue May 12 08:36:12 2020
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -635,7 +640,7 @@ estimators.</p>
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 421 model solutions performed in 0.124 s
+#&gt; Fitted using 421 model solutions performed in 0.146 s
#&gt;
#&gt; Error model: Constant variance
#&gt;
@@ -746,10 +751,10 @@ estimators.</p>
#&gt; 100 m1 31.04 31.98163 -9.416e-01
#&gt; 100 m1 33.13 31.98163 1.148e+00
#&gt; 120 m1 25.15 28.78984 -3.640e+00
-#&gt; 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50
+#&gt; 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50.1
#&gt; R version used for fitting: 4.0.0
-#&gt; Date of fit: Mon May 11 05:14:32 2020
-#&gt; Date of summary: Mon May 11 05:14:32 2020
+#&gt; Date of fit: Tue May 12 08:36:13 2020
+#&gt; Date of summary: Tue May 12 08:36:13 2020
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -757,7 +762,7 @@ estimators.</p>
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 978 model solutions performed in 0.336 s
+#&gt; Fitted using 978 model solutions performed in 0.337 s
#&gt;
#&gt; Error model: Variance unique to each observed variable
#&gt;
@@ -883,10 +888,10 @@ estimators.</p>
#&gt; 100 m1 31.04 31.98773 -9.477e-01
#&gt; 100 m1 33.13 31.98773 1.142e+00
#&gt; 120 m1 25.15 28.80429 -3.654e+00
-#&gt; 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50
+#&gt; 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'>&lt;-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#&gt; <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#&gt; mkin version used for fitting: 0.9.50.1
#&gt; R version used for fitting: 4.0.0
-#&gt; Date of fit: Mon May 11 05:14:32 2020
-#&gt; Date of summary: Mon May 11 05:14:32 2020
+#&gt; Date of fit: Tue May 12 08:36:14 2020
+#&gt; Date of summary: Tue May 12 08:36:14 2020
#&gt;
#&gt; Equations:
#&gt; d_parent/dt = - k_parent * parent
@@ -894,7 +899,7 @@ estimators.</p>
#&gt;
#&gt; Model predictions using solution type analytical
#&gt;
-#&gt; Fitted using 1875 model solutions performed in 0.642 s
+#&gt; Fitted using 1875 model solutions performed in 0.647 s
#&gt;
#&gt; Error model: Two-component variance function
#&gt;

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