From e5d1df9a9b1f0951d7dfbaf24eee4294470b73e2 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2022 14:54:20 +0100 Subject: Complete update of online docs for v1.2.0 --- docs/reference/mkinpredict.html | 283 +++++++++++++++++++++++----------------- 1 file changed, 162 insertions(+), 121 deletions(-) (limited to 'docs/reference/mkinpredict.html') diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index d888bb25..2aab0b50 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -19,7 +19,7 @@ kinetic parameters and initial values for the state variables."> mkin - 1.1.0 + 1.2.0 @@ -46,11 +46,14 @@ kinetic parameters and initial values for the state variables."> Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
    • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
    • @@ -58,7 +61,10 @@ kinetic parameters and initial values for the state variables."> Example evaluation of NAFTA SOP Attachment examples
  • - Some benchmark timings + Benchmark timings for mkin +
    • +
  • + Benchmark timings for saem.mmkin
  • @@ -86,95 +92,125 @@ kinetic parameters and initial values for the state variables.">

    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,
+  maxsteps = 20000,
+  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.

    +

    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.

    +kinetic model, which is generally defined as a set of ordinary 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 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.

    +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.

    +

    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.

    +

    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.

    +

    Absolute error tolerance, passed to ode. Default is 1e-10, +much lower than in lsoda.

    + + +
    maxsteps
    +

    Maximum number of steps, passed to ode.

    + +
    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

    +

    Should it be an error if ode returns NaN values

    +

    Value

    -

    A matrix with the numeric solution in wide format

    + + +

    A matrix with the numeric solution in wide format

    Author

    @@ -183,11 +219,11 @@ as these always return mapped output.

    Examples

    - 
    
-SFO <- mkinmod(degradinol = mkinsub("SFO"))
-# Compare solution types
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")
+    
    
+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
@@ -210,8 +246,8 @@ as these always return mapped output.
    
 #> 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")
+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
@@ -234,8 +270,8 @@ as these always return mapped output.
    
 #> 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)
+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
@@ -258,8 +294,8 @@ as these always return mapped output.
    
 #> 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")
+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
@@ -282,77 +318,82 @@ as these always return mapped output.
    
 #> 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,]
+
+# 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,]
+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,]
+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,]
+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,]
+# 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,]
+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")
+
+# 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,])
-}
-#> Loading required package: rbenchmark
-#> Warning: there is no package called ‘rbenchmark’
-
-# \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
-# }
-
+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   0.004
+#> 4       analytical      1.0   0.004
+#> 1            eigen      5.5   0.022
+#> 3          deSolve     51.0   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))
+#> DLSODA-  At current T (=R1), MXSTEP (=I1) steps   
+#>       taken on this call before reaching TOUT     
+#> In above message, I1 = 1
+#>  
+#> In above message, R1 = 9.99904e-07
+#>  
+#> Warning: an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps
+#> Warning: Returning early. Results are accurate, as far as they go
+#> Error in out[available, var]: (subscript) logical subscript too long
+# }
+
    @@ -367,7 +408,7 @@ as these always return mapped output.

    -

    Site built with pkgdown 2.0.3.

    +

    Site built with pkgdown 2.0.6.

    -- cgit v1.2.1