From e05656d57668688b971c28e32b4cfd4d3eac4662 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 18 Sep 2019 13:03:50 +0200 Subject: Correct and rebuild docs - Reconcile docs and code for max_twa_parent - Correct links to docs in twa vignette - Static documentation rebuilt by pkgdown --- docs/reference/mkinpredict.html | 74 +++++++++++++++++++++++------------------ 1 file changed, 42 insertions(+), 32 deletions(-) (limited to 'docs/reference/mkinpredict.html') diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index fc3e8512..d162135a 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -16,13 +16,15 @@ - + + - - + + + @@ -49,6 +51,7 @@ + @@ -118,6 +121,7 @@ +
@@ -136,7 +140,7 @@
- 
mkinpredict(x, odeparms, odeini, outtimes = seq(0, 120, by = 0.1),
+    
mkinpredict(x, odeparms, odeini, 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, ...)

     
@@ -200,7 +204,7 @@
     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.

     
     
@@ -213,7 +217,7 @@
     
Examples

     
  SFO <- mkinmod(degradinol = mkinsub("SFO"))
   # Compare solution types
-  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+  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
@@ -235,7 +239,7 @@
 #> 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,
+#> 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
@@ -257,7 +261,7 @@
 #> 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,
+#> 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
@@ -279,7 +283,7 @@
 #> 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,
+#> 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
@@ -304,42 +308,43 @@
 #> 21   20   0.2478752

 
   # Compare integration methods to analytical solution
-  mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+  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,
+#> 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,
+#> 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,
+#> 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
+  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),
+  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 verstrichen 
-#>       0.003       0.000       0.003 
  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),
+#>       0.004       0.000       0.003 
  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 verstrichen 
-#>       0.001       0.000       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),
+#>       0.002       0.000       0.001 
  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 verstrichen 
-#>       0.022       0.000       0.021 

-  
    # Predict from a fitted model
+#>       0.021       0.000       0.021 

+  # \dontrun{
+    # Predict from a fitted model
     f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 552.5739
 #> Sum of squared residuals at call 3: 552.5739
 #> Sum of squared residuals at call 4: 552.5739
@@ -368,13 +373,14 @@
 #> Sum of squared residuals at call 59: 196.5334
 #> Sum of squared residuals at call 65: 196.5334
 #> Sum of squared residuals at call 73: 196.5334
-#> Negative log-likelihood at call 75: 26.64668
#> Optimisation successfully terminated.
    head(mkinpredict(f))
#>   time   parent       m1
+#> Negative log-likelihood at call 75: 26.64668
#> Optimisation successfully terminated.
    head(mkinpredict(f))
#>   time   parent       m1
 #> 1  0.0 82.49216 0.000000
 #> 2  0.1 80.00563 1.179955
 #> 3  0.2 77.59404 2.312580
 #> 4  0.3 75.25515 3.399419
 #> 5  0.4 72.98675 4.441969
-#> 6  0.5 70.78673 5.441679
  

+#> 6  0.5 70.78673 5.441679
  # }
+
+ + + -- cgit v1.2.1