diff options
Diffstat (limited to 'docs/reference/mkinfit.html')
| -rw-r--r-- | docs/reference/mkinfit.html | 441 |
1 files changed, 216 insertions, 225 deletions
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index e07d1f13..6c98bc38 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -8,11 +8,13 @@ <title>Fit a kinetic model to data with one or more state variables — mkinfit • mkin</title> + <!-- jquery --> <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script> <!-- Bootstrap --> <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" /> + <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script> <!-- Font Awesome icons --> @@ -32,21 +34,20 @@ -<meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit" /> -<meta property="og:description" content="This function maximises the likelihood of the observed data using - the Port algorithm nlminb, and the specified initial or fixed - parameters and starting values. In each step of the optimsation, the kinetic - model is solved using the function mkinpredict. The parameters - of the selected error model are fitted simultaneously with the degradation - model parameters, as both of them are arguments of the likelihood function. -Per default, parameters in the kinetic models are internally transformed in - order to better satisfy the assumption of a normal distribution of their - estimators." /> +<meta property="og:title" content="Fit a kinetic model to data with one or more state variables — mkinfit" /> +<meta property="og:description" content="This function maximises the likelihood of the observed data using the Port +algorithm nlminb, and the specified initial or fixed +parameters and starting values. In each step of the optimsation, the +kinetic model is solved using the function mkinpredict. The +parameters of the selected error model are fitted simultaneously with the +degradation model parameters, as both of them are arguments of the +likelihood function." /> <meta name="twitter:card" content="summary" /> + <!-- mathjax --> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> @@ -117,7 +118,6 @@ Per default, parameters in the kinetic models are internally transformed in <a href="../news/index.html">News</a> </li> </ul> - <ul class="nav navbar-nav navbar-right"> </ul> @@ -139,135 +139,126 @@ Per default, parameters in the kinetic models are internally transformed in </div> <div class="ref-description"> - - <p>This function maximises the likelihood of the observed data using - the Port algorithm <code><a href='https://rdrr.io/r/stats/nlminb.html'>nlminb</a></code>, and the specified initial or fixed - parameters and starting values. In each step of the optimsation, the kinetic - model is solved using the function <code><a href='mkinpredict.html'>mkinpredict</a></code>. The parameters - of the selected error model are fitted simultaneously with the degradation - model parameters, as both of them are arguments of the likelihood function.</p> -<p>Per default, parameters in the kinetic models are internally transformed in - order to better satisfy the assumption of a normal distribution of their - estimators.</p> - + <p>This function maximises the likelihood of the observed data using the Port +algorithm <code><a href='https://rdrr.io/r/stats/nlminb.html'>nlminb</a></code>, and the specified initial or fixed +parameters and starting values. In each step of the optimsation, the +kinetic model is solved using the function <code><a href='mkinpredict.html'>mkinpredict</a></code>. The +parameters of the selected error model are fitted simultaneously with the +degradation model parameters, as both of them are arguments of the +likelihood function.</p> </div> - <pre class="usage"><span class='fu'>mkinfit</span>(<span class='no'>mkinmod</span>, <span class='no'>observed</span>, - <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, - <span class='kw'>state.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, - <span class='kw'>err.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, - <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, <span class='kw'>fixed_initials</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>mkinmod</span>$<span class='no'>diffs</span>)[-<span class='fl'>1</span>], - <span class='kw'>from_max_mean</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, + <pre class="usage"><span class='fu'>mkinfit</span>(<span class='no'>mkinmod</span>, <span class='no'>observed</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, <span class='kw'>state.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, + <span class='kw'>err.ini</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='kw'>NULL</span>, + <span class='kw'>fixed_initials</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>mkinmod</span>$<span class='no'>diffs</span>)[-<span class='fl'>1</span>], <span class='kw'>from_max_mean</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"auto"</span>, <span class='st'>"analytical"</span>, <span class='st'>"eigen"</span>, <span class='st'>"deSolve"</span>), - <span class='kw'>method.ode</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>, - <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, + <span class='kw'>method.ode</span> <span class='kw'>=</span> <span class='st'>"lsoda"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='st'>"auto"</span>, <span class='kw'>control</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>eval.max</span> <span class='kw'>=</span> <span class='fl'>300</span>, <span class='kw'>iter.max</span> <span class='kw'>=</span> <span class='fl'>200</span>), - <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, - <span class='kw'>transform_fractions</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, - <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, - <span class='kw'>atol</span> <span class='kw'>=</span> <span class='fl'>1e-8</span>, <span class='kw'>rtol</span> <span class='kw'>=</span> <span class='fl'>1e-10</span>, <span class='kw'>n.outtimes</span> <span class='kw'>=</span> <span class='fl'>100</span>, + <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>transform_fractions</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, + <span class='kw'>atol</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>, <span class='kw'>rtol</span> <span class='kw'>=</span> <span class='fl'>1e-10</span>, <span class='kw'>n.outtimes</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"const"</span>, <span class='st'>"obs"</span>, <span class='st'>"tc"</span>), - <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"auto"</span>, <span class='st'>"d_3"</span>, <span class='st'>"direct"</span>, <span class='st'>"twostep"</span>, <span class='st'>"threestep"</span>, - <span class='st'>"fourstep"</span>, <span class='st'>"IRLS"</span>, <span class='st'>"OLS"</span>), - <span class='kw'>reweight.tol</span> <span class='kw'>=</span> <span class='fl'>1e-8</span>, <span class='kw'>reweight.max.iter</span> <span class='kw'>=</span> <span class='fl'>10</span>, - <span class='kw'>trace_parms</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='no'>...</span>)</pre> - + <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"auto"</span>, <span class='st'>"d_3"</span>, <span class='st'>"direct"</span>, <span class='st'>"twostep"</span>, + <span class='st'>"threestep"</span>, <span class='st'>"fourstep"</span>, <span class='st'>"IRLS"</span>, <span class='st'>"OLS"</span>), <span class='kw'>reweight.tol</span> <span class='kw'>=</span> <span class='fl'>1e-08</span>, + <span class='kw'>reweight.max.iter</span> <span class='kw'>=</span> <span class='fl'>10</span>, <span class='kw'>trace_parms</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='no'>...</span>)</pre> + <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> <table class="ref-arguments"> <colgroup><col class="name" /><col class="desc" /></colgroup> <tr> <th>mkinmod</th> - <td><p>A list of class <code><a href='mkinmod.html'>mkinmod</a></code>, containing the kinetic model to be - fitted to the data, or one of the shorthand names ("SFO", "FOMC", "DFOP", - "HS", "SFORB", "IORE"). If a shorthand name is given, a parent only degradation - model is generated for the variable with the highest value in - <code>observed</code>.</p></td> + <td><p>A list of class <code><a href='mkinmod.html'>mkinmod</a></code>, containing the kinetic +model to be fitted to the data, or one of the shorthand names ("SFO", +"FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a +parent only degradation model is generated for the variable with the +highest value in <code>observed</code>.</p></td> </tr> <tr> <th>observed</th> - <td><p>A dataframe with the observed data. The first column called "name" must - contain the name of the observed variable for each data point. The second - column must contain the times of observation, named "time". The third - column must be named "value" and contain the observed values. Zero values - in the "value" column will be removed, with a warning, in order to - avoid problems with fitting the two-component error model. This is not - expected to be a problem, because in general, values of zero are not - observed in degradation data, because there is a lower limit of detection.</p></td> + <td><p>A dataframe with the observed data. The first column called +"name" must contain the name of the observed variable for each data point. +The second column must contain the times of observation, named "time". +The third column must be named "value" and contain the observed values. +Zero values in the "value" column will be removed, with a warning, in +order to avoid problems with fitting the two-component error model. This +is not expected to be a problem, because in general, values of zero are +not observed in degradation data, because there is a lower limit of +detection.</p></td> </tr> <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> + <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> <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> - <td><p>A named vector of initial values for the state variables of the model. In - case the observed variables are represented by more than one model - variable, the names will differ from the names of the observed variables - (see <code>map</code> component of <code><a href='mkinmod.html'>mkinmod</a></code>). The default is to set - the initial value of the first model variable to the mean of the time zero - values for the variable with the maximum observed value, and all others to 0. - If this variable has no time zero observations, its initial value is set to 100.</p></td> + <td><p>A named vector of initial values for the state variables of +the model. In case the observed variables are represented by more than one +model variable, the names will differ from the names of the observed +variables (see <code>map</code> component of <code><a href='mkinmod.html'>mkinmod</a></code>). The default +is to set the initial value of the first model variable to the mean of the +time zero values for the variable with the maximum observed value, and all +others to 0. If this variable has no time zero observations, its initial +value is set to 100.</p></td> </tr> <tr> <th>err.ini</th> - <td><p>A named vector of initial values for the error model parameters to be - optimised. If set to "auto", initial values are set to default values. - Otherwise, inital values for all error model parameters must be - given.</p></td> + <td><p>A named vector of initial values for the error model +parameters to be optimised. If set to "auto", initial values are set to +default values. Otherwise, inital values for all error model parameters +must be given.</p></td> </tr> <tr> <th>fixed_parms</th> - <td><p>The names of parameters that should not be optimised but rather kept at the - values specified in <code>parms.ini</code>.</p></td> + <td><p>The names of parameters that should not be optimised but +rather kept at the values specified in <code>parms.ini</code>.</p></td> </tr> <tr> <th>fixed_initials</th> - <td><p>The names of model variables for which the initial state at time 0 should - be excluded from the optimisation. Defaults to all state variables except - for the first one.</p></td> + <td><p>The names of model variables for which the initial +state at time 0 should be excluded from the optimisation. Defaults to all +state variables except for the first one.</p></td> </tr> <tr> <th>from_max_mean</th> - <td><p>If this is set to TRUE, and the model has only one observed variable, then - data before the time of the maximum observed value (after averaging for each - sampling time) are discarded, and this time is subtracted from all - remaining time values, so the time of the maximum observed mean value is - the new time zero.</p></td> + <td><p>If this is set to TRUE, and the model has only one +observed variable, then data before the time of the maximum observed value +(after averaging for each sampling time) are discarded, and this time is +subtracted from all remaining time values, so the time of the maximum +observed mean value is the new time zero.</p></td> </tr> <tr> <th>solution_type</th> - <td><p>If set to "eigen", the solution of the system of differential equations is - based on the spectral decomposition of the coefficient matrix in cases that - this is possible. If set to "deSolve", a numerical ode solver from package - <code>deSolve</code> is used. If set to "analytical", an analytical - solution of the model is used. This is only implemented for simple - degradation experiments with only one state variable, i.e. with no - metabolites. The default is "auto", which uses "analytical" if possible, - otherwise "deSolve" if a compiler is present, and "eigen" if no - compiler is present and the model can be expressed using eigenvalues and - eigenvectors. This argument is passed on to the helper function - <code><a href='mkinpredict.html'>mkinpredict</a></code>.</p></td> + <td><p>If set to "eigen", the solution of the system of +differential equations is based on the spectral decomposition of the +coefficient matrix in cases that this is possible. If set to "deSolve", a +numerical ode solver from package <code>deSolve</code> is used. If set to +"analytical", an analytical solution of the model is used. This is only +implemented for simple degradation experiments with only one state +variable, i.e. with no metabolites. The default is "auto", which uses +"analytical" if possible, otherwise "deSolve" if a compiler is present, +and "eigen" if no compiler is present and the model can be expressed using +eigenvalues and eigenvectors. This argument is passed on to the helper +function <code><a href='mkinpredict.html'>mkinpredict</a></code>.</p></td> </tr> <tr> <th>method.ode</th> - <td><p>The solution method passed via <code><a href='mkinpredict.html'>mkinpredict</a></code> to - <code>ode</code> in case the solution type is "deSolve". The default - "lsoda" is performant, but sometimes fails to converge.</p></td> + <td><p>The solution method passed via <code><a href='mkinpredict.html'>mkinpredict</a></code> +to <code>ode</code> in case the solution type is "deSolve". The default +"lsoda" is performant, but sometimes fails to converge.</p></td> </tr> <tr> <th>use_compiled</th> - <td><p>If set to <code>FALSE</code>, no compiled version of the <code><a href='mkinmod.html'>mkinmod</a></code> - model is used in the calls to <code><a href='mkinpredict.html'>mkinpredict</a></code> even if a compiled - version is present.</p></td> + <td><p>If set to <code>FALSE</code>, no compiled version of the +<code><a href='mkinmod.html'>mkinmod</a></code> model is used in the calls to +<code><a href='mkinpredict.html'>mkinpredict</a></code> even if a compiled version is present.</p></td> </tr> <tr> <th>control</th> @@ -275,92 +266,90 @@ Per default, parameters in the kinetic models are internally transformed in </tr> <tr> <th>transform_rates</th> - <td><p>Boolean specifying if kinetic rate constants should be transformed in the - model specification used in the fitting for better compliance with the - assumption of normal distribution of the estimator. If TRUE, also - alpha and beta parameters of the FOMC model are log-transformed, as well - as k1 and k2 rate constants for the DFOP and HS models and the break point - tb of the HS model. If FALSE, zero is used as a lower bound for the rates - in the optimisation.</p></td> + <td><p>Boolean specifying if kinetic rate constants should +be transformed in the model specification used in the fitting for better +compliance with the assumption of normal distribution of the estimator. If +TRUE, also alpha and beta parameters of the FOMC model are +log-transformed, as well as k1 and k2 rate constants for the DFOP and HS +models and the break point tb of the HS model. If FALSE, zero is used as +a lower bound for the rates in the optimisation.</p></td> </tr> <tr> <th>transform_fractions</th> - <td><p>Boolean specifying if formation fractions constants should be transformed in the - model specification used in the fitting for better compliance with the - assumption of normal distribution of the estimator. The default (TRUE) is - to do transformations. If TRUE, the g parameter of the DFOP and HS - models are also transformed, as they can also be seen as compositional - data. The transformation used for these transformations is the - <code><a href='ilr.html'>ilr</a></code> transformation.</p></td> + <td><p>Boolean specifying if formation fractions +constants should be transformed in the model specification used in the +fitting for better compliance with the assumption of normal distribution +of the estimator. The default (TRUE) is to do transformations. If TRUE, +the g parameter of the DFOP and HS models are also transformed, as they +can also be seen as compositional data. The transformation used for these +transformations is the <code><a href='ilr.html'>ilr</a></code> transformation.</p></td> </tr> <tr> <th>quiet</th> - <td><p>Suppress printing out the current value of the negative log-likelihood - after each improvement?</p></td> + <td><p>Suppress printing out the current value of the negative +log-likelihood after each improvement?</p></td> </tr> <tr> <th>atol</th> - <td><p>Absolute error tolerance, passed to <code>ode</code>. Default is 1e-8, - lower than in <code>lsoda</code>.</p></td> + <td><p>Absolute error tolerance, passed to <code>ode</code>. Default +is 1e-8, lower than in <code>lsoda</code>.</p></td> </tr> <tr> <th>rtol</th> - <td><p>Absolute error tolerance, passed to <code>ode</code>. Default is 1e-10, - much lower than in <code>lsoda</code>.</p></td> + <td><p>Absolute error tolerance, passed to <code>ode</code>. Default +is 1e-10, much lower than in <code>lsoda</code>.</p></td> </tr> <tr> <th>n.outtimes</th> - <td><p>The length of the dataseries that is produced by the model prediction - function <code><a href='mkinpredict.html'>mkinpredict</a></code>. This impacts the accuracy of - the numerical solver if that is used (see <code>solution_type</code> argument. - The default value is 100.</p></td> + <td><p>The length of the dataseries that is produced by the model +prediction function <code><a href='mkinpredict.html'>mkinpredict</a></code>. This impacts the accuracy +of the numerical solver if that is used (see <code>solution_type</code> +argument. The default value is 100.</p></td> </tr> <tr> <th>error_model</th> - <td><p>If the error model is "const", a constant standard deviation - is assumed.</p> + <td><p>If the error model is "const", a constant standard + 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", nonlinear least squares fitting ("OLS") is - selected. If the error model is "obs", iteratively reweighted least squares - fitting ("IRLS") is selected. If the error model is "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> -<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> -<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> -<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> + <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> +<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> +<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> +<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> <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> - <td><p>Tolerance for the convergence criterion calculated from the error model - parameters in IRLS fits.</p></td> + <td><p>Tolerance for the convergence criterion calculated from +the error model parameters in IRLS fits.</p></td> </tr> <tr> <th>reweight.max.iter</th> @@ -372,50 +361,54 @@ Per default, parameters in the kinetic models are internally transformed in </tr> <tr> <th>...</th> - <td><p>Further arguments that will be passed on to <code>deSolve</code>.</p></td> + <td><p>Further arguments that will be passed on to +<code>deSolve</code>.</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> - - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</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> + <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</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> -<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> -<p>Fitting of several models to several datasets in a single call to - <code><a href='mmkin.html'>mmkin</a></code>.</p></div> - + <p>Per default, parameters in the kinetic models are internally transformed in +order to better satisfy the assumption of a normal distribution of their +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> - - <h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2> + <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</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> - + <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> +<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> +<p>Fitting of several models to several datasets in a single call to + <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> + <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: 0.9.49.6 #> R version used for fitting: 3.6.1 -#> Date of fit: Mon Oct 21 12:07:39 2019 -#> Date of summary: Mon Oct 21 12:07:39 2019 +#> Date of fit: Fri Oct 25 02:08:07 2019 +#> Date of summary: Fri Oct 25 02:08:07 2019 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.459 s +#> Fitted using 222 model solutions performed in 0.453 s #> #> Error model: Constant variance #> @@ -487,7 +480,7 @@ Per default, parameters in the kinetic models are internally transformed in <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 -#> 1.462 0.000 1.463 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> NULL</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 +#> 1.447 0.000 1.448 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> NULL</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_sink parent_m1 m1_sink #> 0.485524 0.514476 1.000000 #> @@ -560,7 +553,7 @@ Per default, parameters in the kinetic models are internally transformed in #> Sum of squared residuals at call 126: 371.2134 #> Sum of squared residuals at call 135: 371.2134 #> Negative log-likelihood at call 145: 97.22429</div><div class='output co'>#> <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#> User System verstrichen -#> 1.04 0.00 1.04 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> NULL</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 +#> 1.032 0.000 1.032 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/coef.html'>coef</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> NULL</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_sink parent_m1 m1_sink #> 0.485524 0.514476 1.000000 #> @@ -596,8 +589,8 @@ Per default, parameters in the kinetic models are internally transformed in <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: 0.9.49.6 #> R version used for fitting: 3.6.1 -#> Date of fit: Mon Oct 21 12:07:54 2019 -#> Date of summary: Mon Oct 21 12:07:54 2019 +#> Date of fit: Fri Oct 25 02:08:22 2019 +#> Date of summary: Fri Oct 25 02:08:22 2019 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -605,7 +598,7 @@ Per default, parameters in the kinetic models are internally transformed in #> #> Model predictions using solution type deSolve #> -#> Fitted using 421 model solutions performed in 1.07 s +#> Fitted using 421 model solutions performed in 1.062 s #> #> Error model: Constant variance #> @@ -713,8 +706,8 @@ Per default, parameters in the kinetic models are internally transformed in #> 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: 0.9.49.6 #> R version used for fitting: 3.6.1 -#> Date of fit: Mon Oct 21 12:07:56 2019 -#> Date of summary: Mon Oct 21 12:07:56 2019 +#> Date of fit: Fri Oct 25 02:08:25 2019 +#> Date of summary: Fri Oct 25 02:08:25 2019 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -722,11 +715,12 @@ Per default, parameters in the kinetic models are internally transformed in #> #> Model predictions using solution type deSolve #> -#> Fitted using 897 model solutions performed in 2.363 s +#> Fitted using 978 model solutions performed in 2.523 s #> #> Error model: Variance unique to each observed variable #> -#> Error model algorithm: IRLS +#> Error model algorithm: d_3 +#> Direct fitting and three-step fitting yield approximately the same likelihood #> #> Starting values for parameters to be optimised: #> value type @@ -761,16 +755,16 @@ Per default, parameters in the kinetic models are internally transformed in #> #> Parameter correlation: #> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_parent -#> parent_0 1.00000 0.51078 -0.19132 -0.59997 0.035676 -#> log_k_parent 0.51078 1.00000 -0.37457 -0.59239 0.069834 -#> log_k_m1 -0.19132 -0.37457 1.00000 0.74398 -0.026158 -#> f_parent_ilr_1 -0.59997 -0.59239 0.74398 1.00000 -0.041371 -#> sigma_parent 0.03568 0.06983 -0.02616 -0.04137 1.000000 -#> sigma_m1 -0.03385 -0.06626 0.02482 0.03926 -0.004628 +#> parent_0 1.00000 0.51078 -0.19133 -0.59997 0.035670 +#> log_k_parent 0.51078 1.00000 -0.37458 -0.59239 0.069833 +#> log_k_m1 -0.19133 -0.37458 1.00000 0.74398 -0.026158 +#> f_parent_ilr_1 -0.59997 -0.59239 0.74398 1.00000 -0.041369 +#> sigma_parent 0.03567 0.06983 -0.02616 -0.04137 1.000000 +#> sigma_m1 -0.03385 -0.06627 0.02482 0.03926 -0.004628 #> sigma_m1 #> parent_0 -0.033847 #> log_k_parent -0.066265 -#> log_k_m1 0.024824 +#> log_k_m1 0.024823 #> f_parent_ilr_1 0.039256 #> sigma_parent -0.004628 #> sigma_m1 1.000000 @@ -784,7 +778,7 @@ Per default, parameters in the kinetic models are internally transformed in #> k_parent 0.098970 22.850 1.099e-21 0.090530 1.082e-01 #> k_m1 0.005245 8.046 1.732e-09 0.004072 6.756e-03 #> f_parent_to_m1 0.513600 23.560 4.352e-22 0.469300 5.578e-01 -#> sigma_parent 3.401000 5.985 5.661e-07 2.244000 4.559e+00 +#> sigma_parent 3.401000 5.985 5.662e-07 2.244000 4.559e+00 #> sigma_m1 2.855000 6.311 2.215e-07 1.934000 3.777e+00 #> #> FOCUS Chi2 error levels in percent: @@ -805,47 +799,47 @@ Per default, parameters in the kinetic models are internally transformed in #> #> Data: #> time variable observed predicted residual -#> 0 parent 99.46 99.65420 -1.942e-01 -#> 0 parent 102.04 99.65420 2.386e+00 -#> 1 parent 93.50 90.26335 3.237e+00 -#> 1 parent 92.50 90.26335 2.237e+00 -#> 3 parent 63.23 74.05308 -1.082e+01 -#> 3 parent 68.99 74.05308 -5.063e+00 -#> 7 parent 52.32 49.84326 2.477e+00 -#> 7 parent 55.13 49.84326 5.287e+00 +#> 0 parent 99.46 99.65417 -1.942e-01 +#> 0 parent 102.04 99.65417 2.386e+00 +#> 1 parent 93.50 90.26332 3.237e+00 +#> 1 parent 92.50 90.26332 2.237e+00 +#> 3 parent 63.23 74.05306 -1.082e+01 +#> 3 parent 68.99 74.05306 -5.063e+00 +#> 7 parent 52.32 49.84325 2.477e+00 +#> 7 parent 55.13 49.84325 5.287e+00 #> 14 parent 27.27 24.92971 2.340e+00 #> 14 parent 26.64 24.92971 1.710e+00 #> 21 parent 11.50 12.46890 -9.689e-01 #> 21 parent 11.64 12.46890 -8.289e-01 #> 35 parent 2.85 3.11925 -2.692e-01 #> 35 parent 2.91 3.11925 -2.092e-01 -#> 50 parent 0.69 0.70678 -1.678e-02 -#> 50 parent 0.63 0.70678 -7.678e-02 +#> 50 parent 0.69 0.70679 -1.679e-02 +#> 50 parent 0.63 0.70679 -7.679e-02 #> 75 parent 0.05 0.05952 -9.523e-03 #> 75 parent 0.06 0.05952 4.772e-04 #> 1 m1 4.84 4.81075 2.925e-02 #> 1 m1 5.64 4.81075 8.292e-01 -#> 3 m1 12.91 13.04197 -1.320e-01 -#> 3 m1 12.96 13.04197 -8.197e-02 -#> 7 m1 22.97 25.06848 -2.098e+00 -#> 7 m1 24.47 25.06848 -5.985e-01 +#> 3 m1 12.91 13.04196 -1.320e-01 +#> 3 m1 12.96 13.04196 -8.196e-02 +#> 7 m1 22.97 25.06847 -2.098e+00 +#> 7 m1 24.47 25.06847 -5.985e-01 #> 14 m1 41.69 36.70308 4.987e+00 #> 14 m1 33.21 36.70308 -3.493e+00 #> 21 m1 44.37 41.65115 2.719e+00 #> 21 m1 46.44 41.65115 4.789e+00 #> 35 m1 41.22 43.29465 -2.075e+00 #> 35 m1 37.95 43.29465 -5.345e+00 -#> 50 m1 41.19 41.19948 -9.477e-03 +#> 50 m1 41.19 41.19948 -9.479e-03 #> 50 m1 40.01 41.19948 -1.189e+00 #> 75 m1 40.09 36.44035 3.650e+00 #> 75 m1 33.85 36.44035 -2.590e+00 -#> 100 m1 31.04 31.98772 -9.477e-01 -#> 100 m1 33.13 31.98772 1.142e+00 -#> 120 m1 25.15 28.80428 -3.654e+00 -#> 120 m1 33.31 28.80428 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: 0.9.49.6 +#> 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: 0.9.49.6 #> R version used for fitting: 3.6.1 -#> Date of fit: Mon Oct 21 12:08:06 2019 -#> Date of summary: Mon Oct 21 12:08:06 2019 +#> Date of fit: Fri Oct 25 02:08:34 2019 +#> Date of summary: Fri Oct 25 02:08:34 2019 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -853,7 +847,7 @@ Per default, parameters in the kinetic models are internally transformed in #> #> Model predictions using solution type deSolve #> -#> Fitted using 2289 model solutions performed in 9.175 s +#> Fitted using 2289 model solutions performed in 9.136 s #> #> Error model: Two-component variance function #> @@ -969,21 +963,18 @@ Per default, parameters in the kinetic models are internally transformed in #> 120 m1 25.15 29.04130 -3.891304 #> 120 m1 33.31 29.04130 4.268696</div><div class='input'># } + </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="sidebar"> <h2>Contents</h2> <ul class="nav nav-pills nav-stacked"> <li><a href="#arguments">Arguments</a></li> - + <li><a href="#source">Source</a></li> <li><a href="#value">Value</a></li> - - <li><a href="#see-also">See also</a></li> - + <li><a href="#details">Details</a></li> <li><a href="#note">Note</a></li> - - <li><a href="#source">Source</a></li> - + <li><a href="#see-also">See also</a></li> <li><a href="#examples">Examples</a></li> </ul> |