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Diffstat (limited to 'docs/reference/mkinfit.html')
| -rw-r--r-- | docs/reference/mkinfit.html | 133 |
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'><-</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'>#> 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'>#> mkin version used for fitting: 0.9.50.1 #> R version used for fitting: 4.0.0 -#> Date of fit: Mon May 11 05:14:26 2020 -#> Date of summary: Mon May 11 05:14:26 2020 +#> Date of fit: Tue May 12 08:36:07 2020 +#> Date of summary: Tue May 12 08:36:07 2020 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.043 s +#> Fitted using 222 model solutions performed in 0.047 s #> #> Error model: Constant variance #> @@ -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'>#> <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'><-</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'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> User System verstrichen -#> 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'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma +#> 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'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma #> 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'>#> $ff #> parent_m1 parent_sink #> 0.514476 0.485524 @@ -592,7 +597,7 @@ estimators.</p> #> Sum of squared residuals at call 166: 371.2134 #> Sum of squared residuals at call 168: 371.2134 #> Negative log-likelihood at call 178: 97.22429</div><div class='output co'>#> <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#> User System verstrichen -#> 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'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma +#> 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'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma #> 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'>#> $ff #> parent_m1 parent_sink #> 0.514476 0.485524 @@ -622,12 +627,12 @@ estimators.</p> <span class='no'>fit.SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<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'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <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'><-</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'>#> <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'><-</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'>#> <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'>#> 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'>#> <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'><-</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'>#> <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'>#> mkin version used for fitting: 0.9.50.1 #> R version used for fitting: 4.0.0 -#> Date of fit: Mon May 11 05:14:31 2020 -#> Date of summary: Mon May 11 05:14:31 2020 +#> Date of fit: Tue May 12 08:36:12 2020 +#> Date of summary: Tue May 12 08:36:12 2020 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -635,7 +640,7 @@ estimators.</p> #> #> Model predictions using solution type analytical #> -#> Fitted using 421 model solutions performed in 0.124 s +#> Fitted using 421 model solutions performed in 0.146 s #> #> Error model: Constant variance #> @@ -746,10 +751,10 @@ estimators.</p> #> 100 m1 31.04 31.98163 -9.416e-01 #> 100 m1 33.13 31.98163 1.148e+00 #> 120 m1 25.15 28.78984 -3.640e+00 -#> 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'><-</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'>#> <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'>#> mkin version used for fitting: 0.9.50 +#> 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'><-</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'>#> <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'>#> mkin version used for fitting: 0.9.50.1 #> R version used for fitting: 4.0.0 -#> Date of fit: Mon May 11 05:14:32 2020 -#> Date of summary: Mon May 11 05:14:32 2020 +#> Date of fit: Tue May 12 08:36:13 2020 +#> Date of summary: Tue May 12 08:36:13 2020 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -757,7 +762,7 @@ estimators.</p> #> #> Model predictions using solution type analytical #> -#> Fitted using 978 model solutions performed in 0.336 s +#> Fitted using 978 model solutions performed in 0.337 s #> #> Error model: Variance unique to each observed variable #> @@ -883,10 +888,10 @@ estimators.</p> #> 100 m1 31.04 31.98773 -9.477e-01 #> 100 m1 33.13 31.98773 1.142e+00 #> 120 m1 25.15 28.80429 -3.654e+00 -#> 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'><-</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'>#> <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'>#> mkin version used for fitting: 0.9.50 +#> 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'><-</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'>#> <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'>#> mkin version used for fitting: 0.9.50.1 #> R version used for fitting: 4.0.0 -#> Date of fit: Mon May 11 05:14:32 2020 -#> Date of summary: Mon May 11 05:14:32 2020 +#> Date of fit: Tue May 12 08:36:14 2020 +#> Date of summary: Tue May 12 08:36:14 2020 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -894,7 +899,7 @@ estimators.</p> #> #> Model predictions using solution type analytical #> -#> Fitted using 1875 model solutions performed in 0.642 s +#> Fitted using 1875 model solutions performed in 0.647 s #> #> Error model: Two-component variance function #> |