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<title>mkinpredict. mkin 0.9.44.9000</title>
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Johannes Ranke
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<h1>
Produce predictions from a kinetic model using specific parameters
</h1>
<div class="row">
<div class="span8">
<h2>Usage</h2>
<pre><div>mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve",
use_compiled = "auto", method.ode = "lsoda", atol = 1e-08, rtol = 1e-10, map_output = 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 <- mkinmod(degradinol = list(type = "SFO"))
# Compare solution types
mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
solution_type = "analytical")
</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 = "deSolve")
</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 = "deSolve", 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 = "eigen")
</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 = "analytical")[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 = "lsoda")[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 = "ode45")[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 = "rk4")[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 = "SFO", to = "m1"),
m1 = list(type = "SFO"))
</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 = "eigen")[201,]))
</div>
<div class='output'> time parent m1
201 20 4.978707 27.46227
</div>
<div class='output'> user system elapsed
0.004 0.028 0.005
</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 = "deSolve")[201,]))
</div>
<div class='output'> time parent m1
201 20 4.978707 27.46227
</div>
<div class='output'> user system elapsed
0.016 0.004 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 = "deSolve", use_compiled = FALSE)[201,]))
</div>
<div class='output'> time parent m1
201 20 4.978707 27.46227
</div>
<div class='output'> user system elapsed
0.032 0.000 0.035
</div></pre>
</div>
<div class="span4">
<!-- <ul>
<li>mkinpredict</li>
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<li> manip </li>
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<h2>Author</h2>
Johannes Ranke
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