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+<title>mkinpredict. mkin 0.9-41</title>
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+ Johannes Ranke
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+ <header>
+
+ </header>
+
+ <h1>
+ Produce predictions from a kinetic model using specifc parameters
+</h1>
+
+<div class="row">
+ <div class="span8">
+ <h2>Usage</h2>
+ <pre><div>mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type&nbsp;=&nbsp;"deSolve",
+ use_compiled&nbsp;=&nbsp;"auto", method.ode&nbsp;=&nbsp;"lsoda", atol&nbsp;=&nbsp;1e-08, rtol&nbsp;=&nbsp;1e-10, map_output&nbsp;=&nbsp;TRUE, ...)</div></pre>
+
+ <h2>Arguments</h2>
+ <dl>
+ <dt>mkinmod</dt>
+ <dd>
+ A kinetic model as produced by <code><a href='mkinmod.html'>mkinmod</a></code>.
+ </dd>
+ <dt>odeparms</dt>
+ <dd>
+ A numeric vector specifying the parameters used in the kinetic model, which
+ is generally defined as a set of ordinary differential equations.
+ </dd>
+ <dt>odeini</dt>
+ <dd>
+ A numeric vectory containing the initial values of the state variables of
+ the model. Note that the state variables can differ from the observed
+ variables, for example in the case of the SFORB model.
+ </dd>
+ <dt>outtimes</dt>
+ <dd>
+ A numeric vector specifying the time points for which model predictions
+ should be generated.
+ </dd>
+ <dt>solution_type</dt>
+ <dd>
+ The method that should be used for producing the predictions. This should
+ generally be "analytical" if there is only one observed variable, and
+ usually "deSolve" in the case of several observed variables. The third
+ possibility "eigen" is faster but not applicable to some models e.g.
+ using FOMC for the parent compound.
+ </dd>
+ <dt>method.ode</dt>
+ <dd>
+ The solution method passed via <code><a href='mkinpredict.html'>mkinpredict</a></code> to
+ <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code> in case the solution type is "deSolve". The default
+ "lsoda" is performant, but sometimes fails to converge.
+ </dd>
+ <dt>use_compiled</dt>
+ <dd>
+ If set to <code>FALSE</code>, no compiled version of the <code><a href='mkinmod.html'>mkinmod</a></code>
+ model is used, even if is present.
+ </dd>
+ <dt>atol</dt>
+ <dd>
+ Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-8,
+ lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>.
+ </dd>
+ <dt>rtol</dt>
+ <dd>
+ Absolute error tolerance, passed to <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>. Default is 1e-10,
+ much lower than in <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/lsoda'>lsoda</a></code>.
+ </dd>
+ <dt>map_output</dt>
+ <dd>
+ Boolean to specify if the output should list values for the observed
+ variables (default) or for all state variables (if set to FALSE).
+ </dd>
+ <dt>...</dt>
+ <dd>
+ Further arguments passed to the ode solver in case such a solver is used.
+ </dd>
+ </dl>
+
+ <div class="Description">
+ <h2>Description</h2>
+
+ <p>This function produces a time series for all the observed variables in a
+ kinetic model as specified by <code><a href='mkinmod.html'>mkinmod</a></code>, using a specific set of
+ kinetic parameters and initial values for the state variables.</p>
+
+ </div>
+
+ <div class="Value">
+ <h2>Value</h2>
+
+ <p><dl>
+ A matrix in the same format as the output of <code><a href='http://www.inside-r.org/packages/cran/deSolve/docs/ode'>ode</a></code>.
+</dl></p>
+
+ </div>
+
+ <h2 id="examples">Examples</h2>
+ <pre class="examples"><div class='input'> SFO &lt;- mkinmod(degradinol = list(type = &quot;SFO&quot;))
+ # Compare solution types
+ mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = &quot;analytical&quot;)
+</div>
+<div class='output'> time degradinol
+1 0 100.0000000
+2 1 74.0818221
+3 2 54.8811636
+4 3 40.6569660
+5 4 30.1194212
+6 5 22.3130160
+7 6 16.5298888
+8 7 12.2456428
+9 8 9.0717953
+10 9 6.7205513
+11 10 4.9787068
+12 11 3.6883167
+13 12 2.7323722
+14 13 2.0241911
+15 14 1.4995577
+16 15 1.1108997
+17 16 0.8229747
+18 17 0.6096747
+19 18 0.4516581
+20 19 0.3345965
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = &quot;deSolve&quot;)
+</div>
+<div class='output'> time degradinol
+1 0 100.0000000
+2 1 74.0818221
+3 2 54.8811636
+4 3 40.6569660
+5 4 30.1194212
+6 5 22.3130160
+7 6 16.5298888
+8 7 12.2456428
+9 8 9.0717953
+10 9 6.7205513
+11 10 4.9787068
+12 11 3.6883167
+13 12 2.7323722
+14 13 2.0241911
+15 14 1.4995577
+16 15 1.1108996
+17 16 0.8229747
+18 17 0.6096747
+19 18 0.4516581
+20 19 0.3345965
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = &quot;deSolve&quot;, use_compiled = FALSE)
+</div>
+<div class='output'> time degradinol
+1 0 100.0000000
+2 1 74.0818221
+3 2 54.8811636
+4 3 40.6569660
+5 4 30.1194212
+6 5 22.3130160
+7 6 16.5298888
+8 7 12.2456428
+9 8 9.0717953
+10 9 6.7205513
+11 10 4.9787068
+12 11 3.6883167
+13 12 2.7323722
+14 13 2.0241911
+15 14 1.4995577
+16 15 1.1108996
+17 16 0.8229747
+18 17 0.6096747
+19 18 0.4516581
+20 19 0.3345965
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = &quot;eigen&quot;)
+</div>
+<div class='output'> time degradinol
+1 0 100.0000000
+2 1 74.0818221
+3 2 54.8811636
+4 3 40.6569660
+5 4 30.1194212
+6 5 22.3130160
+7 6 16.5298888
+8 7 12.2456428
+9 8 9.0717953
+10 9 6.7205513
+11 10 4.9787068
+12 11 3.6883167
+13 12 2.7323722
+14 13 2.0241911
+15 14 1.4995577
+16 15 1.1108997
+17 16 0.8229747
+18 17 0.6096747
+19 18 0.4516581
+20 19 0.3345965
+21 20 0.2478752
+</div>
+<div class='input'>
+
+ # Compare integration methods to analytical solution
+ mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = &quot;analytical&quot;)[21,]
+</div>
+<div class='output'> time degradinol
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = &quot;lsoda&quot;)[21,]
+</div>
+<div class='output'> time degradinol
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = &quot;ode45&quot;)[21,]
+</div>
+<div class='output'> time degradinol
+21 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = &quot;rk4&quot;)[21,]
+</div>
+<div class='output'> time degradinol
+21 20 0.2480043
+</div>
+<div class='input'> # rk4 is not as precise here
+
+ # The number of output times used to make a lot of difference until the
+ # default for atol was adjusted
+ mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+ seq(0, 20, by = 0.1))[201,]
+</div>
+<div class='output'> time degradinol
+201 20 0.2478752
+</div>
+<div class='input'> mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+ seq(0, 20, by = 0.01))[2001,]
+</div>
+<div class='output'> time degradinol
+2001 20 0.2478752
+</div>
+<div class='input'>
+ # Check compiled model versions - they are faster than the eigenvalue based solutions!
+ SFO_SFO = mkinmod(parent = list(type = &quot;SFO&quot;, to = &quot;m1&quot;),
+ m1 = list(type = &quot;SFO&quot;))
+</div>
+<strong class='message'>Successfully compiled differential equation model from auto-generated C code.</strong>
+<div class='input'> system.time(
+ print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = &quot;eigen&quot;)[201,]))
+</div>
+<div class='output'> time parent m1
+201 20 4.978707 27.46227
+</div>
+<div class='output'> user system elapsed
+ 0.000 0.028 0.004
+</div>
+<div class='input'> system.time(
+ print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = &quot;deSolve&quot;)[201,]))
+</div>
+<div class='output'> time parent m1
+201 20 4.978707 27.46227
+</div>
+<div class='output'> user system elapsed
+ 0.000 0.020 0.003
+</div>
+<div class='input'> system.time(
+ print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = &quot;deSolve&quot;, use_compiled = FALSE)[201,]))
+</div>
+<div class='output'> time parent m1
+201 20 4.978707 27.46227
+</div>
+<div class='output'> user system elapsed
+ 0.052 0.000 0.054
+</div></pre>
+ </div>
+ <div class="span4">
+ <!-- <ul>
+ <li>mkinpredict</li>
+ </ul>
+ <ul>
+ <li> manip </li>
+ </ul> -->
+
+
+ <h2>Author</h2>
+
+ Johannes Ranke
+
+
+ </div>
+</div>
+
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