Produce predictions from a kinetic model using specific parameters

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 model as specified by mkinmod, using a specific set of kinetic parameters and initial values for the state variables.

mkinpredict(x, odeparms, odeini, outtimes, ...)
-
-# S3 method for mkinmod
-mkinpredict(
-  x,
-  odeparms = c(k_parent_sink = 0.1),
-  odeini = c(parent = 100),
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  map_output = TRUE,
-  na_stop = TRUE,
-  ...
-)
-
-# S3 method for mkinfit
-mkinpredict(
-  x,
-  odeparms = x$bparms.ode,
-  odeini = x$bparms.state,
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  map_output = TRUE,
-  ...
-)

mkinpredict(x, odeparms, odeini, outtimes, ...)
+
+# S3 method for mkinmod
+mkinpredict(
+  x,
+  odeparms = c(k_parent_sink = 0.1),
+  odeini = c(parent = 100),
+  outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve",
+  use_compiled = "auto",
+  method.ode = "lsoda",
+  atol = 1e-08,
+  rtol = 1e-10,
+  map_output = TRUE,
+  na_stop = TRUE,
+  ...
+)
+
+# S3 method for mkinfit
+mkinpredict(
+  x,
+  odeparms = x$bparms.ode,
+  odeini = x$bparms.state,
+  outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve",
+  use_compiled = "auto",
+  method.ode = "lsoda",
+  atol = 1e-08,
+  rtol = 1e-10,
+  map_output = TRUE,
+  ...
+)

Arguments

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

x	A kinetic model as produced by `mkinmod`, or a kinetic -fit as fitted by `mkinfit`. In the latter case, the fitted -parameters are used for the prediction.
odeparms	A numeric vector specifying the parameters used in the + + Arguments + x + A kinetic model as produced by `mkinmod`, or a kinetic +fit as fitted by `mkinfit`. In the latter case, the fitted +parameters are used for the prediction. + 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 vector containing the initial values of the state +differential equations. + odeini + A numeric vector 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.
...	Further arguments passed to the ode solver in case such a -solver is used.
solution_type	The method that should be used for producing 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. + ... + Further arguments passed to the ode solver in case such a +solver is used. + 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.
use_compiled	If set to `FALSE`, no compiled version of the -`mkinmod` model is used, even if is present.
method.ode	The solution method passed via `mkinpredict` +some models e.g. using FOMC for the parent compound. + use_compiled + If set to `FALSE`, no compiled version of the +`mkinmod` model is used, even if is present. + 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.
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 +"lsoda" is performant, but sometimes fails to converge. + 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). Setting this to FALSE has no effect for analytical solutions, -as these always return mapped output.
na_stop	Should it be an error if deSolve::ode returns NaN values

- -

Value

- +as these always return mapped output.

na_stop

Should it be an error if deSolve::ode returns NaN values

Value

A matrix with the numeric solution in wide format

Author

- +

Author

Johannes Ranke

Examples


-SFO <- mkinmod(degradinol = mkinsub("SFO"))
-# Compare solution types
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")
-
#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108997
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve")
-
#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108996
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve", use_compiled = FALSE)
-
#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108996
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "eigen")
-
#>    time  degradinol
-#> 0     0 100.0000000
-#> 1     1  74.0818221
-#> 2     2  54.8811636
-#> 3     3  40.6569660
-#> 4     4  30.1194212
-#> 5     5  22.3130160
-#> 6     6  16.5298888
-#> 7     7  12.2456428
-#> 8     8   9.0717953
-#> 9     9   6.7205513
-#> 10   10   4.9787068
-#> 11   11   3.6883167
-#> 12   12   2.7323722
-#> 13   13   2.0241911
-#> 14   14   1.4995577
-#> 15   15   1.1108997
-#> 16   16   0.8229747
-#> 17   17   0.6096747
-#> 18   18   0.4516581
-#> 19   19   0.3345965
-#> 20   20   0.2478752

-# Compare integration methods to analytical solution
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")[21,]
-
#>       time degradinol 
-#> 20.0000000  0.2478752 
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "lsoda")[21,]
-
#>       time degradinol 
-#> 20.0000000  0.2478752 
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "ode45")[21,]
-
#>       time degradinol 
-#> 20.0000000  0.2478752 
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "rk4")[21,]
-
#>       time degradinol 
-#> 20.0000000  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 = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.1))[201,]
-
#>       time degradinol 
-#> 20.0000000  0.2478752 
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.01))[2001,]
-
#>       time degradinol 
-#> 20.0000000  0.2478752 

-# Comparison of the performance of solution types
-SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
-                  m1 = list(type = "SFO"), use_of_ff = "max")
-
#> Temporary DLL for differentials generated and loaded
if(require(rbenchmark)) {
-  benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
-    eigen = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "eigen")[201,],
-    deSolve_compiled = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve")[201,],
-    deSolve = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve", use_compiled = FALSE)[201,],
-    analytical = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "analytical", use_compiled = FALSE)[201,])
-}
-
#>               test relative elapsed
-#> 2 deSolve_compiled      1.0   0.005
-#> 4       analytical      1.0   0.005
-#> 1            eigen      4.0   0.020
-#> 3          deSolve     46.2   0.231

-# \dontrun{
-  # Predict from a fitted model
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
-  head(mkinpredict(f))
-
#>     time   parent       m1
-#> 0    0.0 82.49216 0.000000
-#> 0.1  0.1 80.00562 1.236394
-#> 0.2  0.2 77.59404 2.423201
-#> 0.3  0.3 75.25514 3.562040
-#> 0.4  0.4 72.98675 4.654478
-#> 0.5  0.5 70.78673 5.702033
# }
-
-

Examples


+SFO <- mkinmod(degradinol = mkinsub("SFO"))
+# Compare solution types
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "analytical")
+#>    time  degradinol
+#> 0     0 100.0000000
+#> 1     1  74.0818221
+#> 2     2  54.8811636
+#> 3     3  40.6569660
+#> 4     4  30.1194212
+#> 5     5  22.3130160
+#> 6     6  16.5298888
+#> 7     7  12.2456428
+#> 8     8   9.0717953
+#> 9     9   6.7205513
+#> 10   10   4.9787068
+#> 11   11   3.6883167
+#> 12   12   2.7323722
+#> 13   13   2.0241911
+#> 14   14   1.4995577
+#> 15   15   1.1108997
+#> 16   16   0.8229747
+#> 17   17   0.6096747
+#> 18   18   0.4516581
+#> 19   19   0.3345965
+#> 20   20   0.2478752
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "deSolve")
+#>    time  degradinol
+#> 0     0 100.0000000
+#> 1     1  74.0818221
+#> 2     2  54.8811636
+#> 3     3  40.6569660
+#> 4     4  30.1194212
+#> 5     5  22.3130160
+#> 6     6  16.5298888
+#> 7     7  12.2456428
+#> 8     8   9.0717953
+#> 9     9   6.7205513
+#> 10   10   4.9787068
+#> 11   11   3.6883167
+#> 12   12   2.7323722
+#> 13   13   2.0241911
+#> 14   14   1.4995577
+#> 15   15   1.1108996
+#> 16   16   0.8229747
+#> 17   17   0.6096747
+#> 18   18   0.4516581
+#> 19   19   0.3345965
+#> 20   20   0.2478752
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "deSolve", use_compiled = FALSE)
+#>    time  degradinol
+#> 0     0 100.0000000
+#> 1     1  74.0818221
+#> 2     2  54.8811636
+#> 3     3  40.6569660
+#> 4     4  30.1194212
+#> 5     5  22.3130160
+#> 6     6  16.5298888
+#> 7     7  12.2456428
+#> 8     8   9.0717953
+#> 9     9   6.7205513
+#> 10   10   4.9787068
+#> 11   11   3.6883167
+#> 12   12   2.7323722
+#> 13   13   2.0241911
+#> 14   14   1.4995577
+#> 15   15   1.1108996
+#> 16   16   0.8229747
+#> 17   17   0.6096747
+#> 18   18   0.4516581
+#> 19   19   0.3345965
+#> 20   20   0.2478752
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "eigen")
+#>    time  degradinol
+#> 0     0 100.0000000
+#> 1     1  74.0818221
+#> 2     2  54.8811636
+#> 3     3  40.6569660
+#> 4     4  30.1194212
+#> 5     5  22.3130160
+#> 6     6  16.5298888
+#> 7     7  12.2456428
+#> 8     8   9.0717953
+#> 9     9   6.7205513
+#> 10   10   4.9787068
+#> 11   11   3.6883167
+#> 12   12   2.7323722
+#> 13   13   2.0241911
+#> 14   14   1.4995577
+#> 15   15   1.1108997
+#> 16   16   0.8229747
+#> 17   17   0.6096747
+#> 18   18   0.4516581
+#> 19   19   0.3345965
+#> 20   20   0.2478752
+
+# Compare integration methods to analytical solution
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "analytical")[21,]
+#>       time degradinol 
+#> 20.0000000  0.2478752 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "lsoda")[21,]
+#>       time degradinol 
+#> 20.0000000  0.2478752 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "ode45")[21,]
+#>       time degradinol 
+#> 20.0000000  0.2478752 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "rk4")[21,]
+#>       time degradinol 
+#> 20.0000000  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 = 0.3), c(degradinol = 100),
+      seq(0, 20, by = 0.1))[201,]
+#>       time degradinol 
+#> 20.0000000  0.2478752 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
+      seq(0, 20, by = 0.01))[2001,]
+#>       time degradinol 
+#> 20.0000000  0.2478752 
+
+# Comparison of the performance of solution types
+SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
+                  m1 = list(type = "SFO"), use_of_ff = "max")
+#> Temporary DLL for differentials generated and loaded
+if(require(rbenchmark)) {
+  benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
+    eigen = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "eigen")[201,],
+    deSolve_compiled = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "deSolve")[201,],
+    deSolve = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "deSolve", use_compiled = FALSE)[201,],
+    analytical = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "analytical", use_compiled = FALSE)[201,])
+}
+#>               test relative elapsed
+#> 4       analytical      1.0   0.005
+#> 2 deSolve_compiled      1.2   0.006
+#> 1            eigen      4.2   0.021
+#> 3          deSolve     40.8   0.204
+
+# \dontrun{
+  # Predict from a fitted model
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
+  head(mkinpredict(f))
+#>     time   parent       m1
+#> 0    0.0 82.49216 0.000000
+#> 0.1  0.1 80.00562 1.236394
+#> 0.2  0.2 77.59404 2.423201
+#> 0.3  0.3 75.25514 3.562040
+#> 0.4  0.4 72.98675 4.654478
+#> 0.5  0.5 70.78673 5.702033
+# }
+
+