From a350a16bb2c11986faf5883a2804d46b03bd7c8f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sat, 10 Sep 2016 05:21:52 +0200 Subject: Static documentation rebuilt with current staticdocs Using hadley/staticdocs commit #8c1069d from 8 days ago --- docs/mkinpredict.html | 345 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 345 insertions(+) create mode 100644 docs/mkinpredict.html (limited to 'docs/mkinpredict.html') diff --git a/docs/mkinpredict.html b/docs/mkinpredict.html new file mode 100644 index 00000000..438b37c0 --- /dev/null +++ b/docs/mkinpredict.html @@ -0,0 +1,345 @@ + + + + +mkinpredict. mkin 0.9.44.9000 + + + + + + + + + + + + + + + +
+
+
+ mkin 0.9.44.9000 +
+ +
+
+
+
+ + +
+
+ +
+ +

+ Produce predictions from a kinetic model using specific parameters +

+ +
+
+

Usage

+ 
mkinpredict(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve",
+              use_compiled = "auto", method.ode = "lsoda", atol = 1e-08, rtol = 1e-10,
+        map_output = TRUE, ...)
+ +

Arguments

+
+
mkinmod
+
+ A kinetic model as produced by mkinmod. +
+
odeparms
+
+ A numeric vector specifying the parameters used in the kinetic model, which + is generally defined as a set of ordinary differential equations. +
+
odeini
+
+ 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. +
+
outtimes
+
+ A numeric vector specifying the time points for which model predictions + should be generated. +
+
solution_type
+
+ 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. +
+
method.ode
+
+ The solution method passed via mkinpredict to + ode in case the solution type is "deSolve". The default + "lsoda" is performant, but sometimes fails to converge. +
+
use_compiled
+
+ If set to FALSE, no compiled version of the mkinmod + model is used, even if is present. +
+
atol
+
+ Absolute error tolerance, passed to ode. Default is 1e-8, + lower than in lsoda. +
+
rtol
+
+ Absolute error tolerance, passed to ode. Default is 1e-10, + much lower than in lsoda. +
+
map_output
+
+ Boolean to specify if the output should list values for the observed + variables (default) or for all state variables (if set to FALSE). +
+
...
+
+ Further arguments passed to the ode solver in case such a solver is used. +
+
+ +
+

Description

+ +

This function produces a time series for all the observed variables in a + kinetic model as specified by mkinmod, using a specific set of + kinetic parameters and initial values for the state variables.

+ +
+ +
+

Value

+ +

+ A matrix in the same format as the output of ode. +

+ +
+ +

Examples

+ 
  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")

+
   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
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "deSolve")

+
   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
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "deSolve", use_compiled = FALSE)

+
   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
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "eigen")

+
   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
+

+

+
+  # Compare integration methods to analytical solution
+  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              solution_type = "analytical")[21,]

+
   time degradinol
+21   20  0.2478752
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "lsoda")[21,]

+
   time degradinol
+21   20  0.2478752
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "ode45")[21,]

+
   time degradinol
+21   20  0.2478752
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+              method = "rk4")[21,]

+
   time degradinol
+21   20  0.2480043
+

+
 # 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,]

+
    time degradinol
+201   20  0.2478752
+

+
  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+              seq(0, 20, by = 0.01))[2001,]

+
     time degradinol
+2001   20  0.2478752
+

+

+  # 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"))

+Successfully compiled differential equation model from auto-generated C code.
+
  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,]))

+
    time   parent       m1
+201   20 4.978707 27.46227
+

+
   user  system elapsed 
+  0.008   0.020   0.005 
+

+
  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,]))

+
    time   parent       m1
+201   20 4.978707 27.46227
+

+
   user  system elapsed 
+  0.012   0.004   0.002 
+

+
  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,]))

+
    time   parent       m1
+201   20 4.978707 27.46227
+

+
   user  system elapsed 
+  0.036   0.000   0.036 
+
+
+
+ + + + +

Author

+ + Johannes Ranke + + +
+
+ + +
+ + \ No newline at end of file -- cgit v1.2.1