From 234c9059a95e104917e488a6ddd2313234a96cdc Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 11 May 2020 05:15:19 +0200 Subject: Avoid merge() and data.frame() in cost function also for deSolve and eigenvalue based solutions. This noticeably increases performance for these methods, see test.log and benchmark vignette. --- docs/reference/mkinpredict.html | 253 ++++++++++++++++++++-------------------- 1 file changed, 126 insertions(+), 127 deletions(-) (limited to 'docs/reference/mkinpredict.html') diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index 21c13156..e48a0cdb 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -251,7 +251,8 @@ 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).

+FALSE). Setting this to FALSE has no effect for analytical solutions, +as these always return mapped output.

... @@ -262,7 +263,7 @@ solver is used.

Value

-

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

+

A matrix with the numeric solution in wide format

Examples


@@ -270,149 +271,147 @@ solver is used.

 # Compare solution types
 mkinpredict(SFO, c(k_degradinol = 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 = 0.3), c(degradinol = 100), 0:20,
+#> 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
-#> 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 = 0.3), c(degradinol = 100), 0:20,
+#> 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
-#> 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 = 0.3), c(degradinol = 100), 0:20,
+#> 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
-#> 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

+#> 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
-#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "lsoda")[21,]
#>    time degradinol
-#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "ode45")[21,]
#>    time degradinol
-#> 21   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "rk4")[21,]
#>    time degradinol
-#> 21   20  0.2480043
# rk4 is not as precise here
+      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
-#> 201   20  0.2478752
mkinpredict(SFO, c(k_degradinol = 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!
+      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 = "min")
#> Successfully compiled differential equation model from auto-generated C code.
if(require(rbenchmark)) {
-  benchmark(
-    eigen = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+                  m1 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
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_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+      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_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,],
-    replications = 10)
-}
#> Lade nötiges Paket: rbenchmark
#>               test replications elapsed relative user.self sys.self user.child
-#> 3          deSolve           10   0.229   28.625     0.229        0          0
-#> 2 deSolve_compiled           10   0.008    1.000     0.008        0          0
-#> 1            eigen           10   0.026    3.250     0.025        0          0
-#>   sys.child
-#> 3         0
-#> 2         0
-#> 1         0

-# Since mkin 0.9.49.11 we also have analytical solutions for some models, including SFO-SFO
-#    deSolve = 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 = "analytical", use_compiled = FALSE)[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     1.00   0.004
+#> 2 deSolve_compiled     1.25   0.005
+#> 1            eigen     5.00   0.020
+#> 3          deSolve    54.25   0.217

 # \dontrun{
   # Predict from a fitted model
   f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-  head(mkinpredict(f))
#>   time   parent       m1
-#> 1  0.0 82.49216 0.000000
-#> 2  0.1 80.00563 1.179963
-#> 3  0.2 77.59404 2.312596
-#> 4  0.3 75.25515 3.399443
-#> 5  0.4 72.98675 4.442000
-#> 6  0.5 70.78673 5.441717
# }
+  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 1.236198
+#> 0.2  0.2 77.59404 2.422818
+#> 0.3  0.3 75.25514 3.561476
+#> 0.4  0.4 72.98675 4.653740
+#> 0.5  0.5 70.78673 5.701130
# }
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