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/create_deg_func.html | 16 +- docs/reference/index.html | 6 + docs/reference/mkinfit.html | 328 ++++++++++++++++++++++++------------ docs/reference/mkinmod.html | 4 +- docs/reference/mkinpredict.html | 253 ++++++++++++++------------- docs/reference/plot.mkinfit-1.png | Bin 45157 -> 53973 bytes docs/reference/plot.mkinfit-2.png | Bin 62528 -> 75079 bytes docs/reference/plot.mkinfit-3.png | Bin 57593 -> 70266 bytes docs/reference/plot.mkinfit-4.png | Bin 61280 -> 74166 bytes docs/reference/plot.mkinfit-5.png | Bin 59080 -> 68692 bytes docs/reference/plot.mkinfit-6.png | Bin 64639 -> 75012 bytes docs/reference/plot.mkinfit-7.png | Bin 65273 -> 75692 bytes docs/reference/plot.mkinfit.html | 61 +++---- 13 files changed, 398 insertions(+), 270 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index e14857cc..67016be0 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -167,7 +167,21 @@ 

 SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
+ m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings +fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE) +fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE) +# \dontrun{ +if (require(rbenchmark)) + benchmark( + analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE), + deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), + replications = 1)
#> Lade nötiges Paket: rbenchmark
#> test replications elapsed relative user.self sys.self user.child +#> 1 analytical 1 0.198 1.000 0.198 0.000 0 +#> 2 deSolve 1 0.350 1.768 0.348 0.001 0 +#> sys.child +#> 1 0 +#> 2 0
# } +
@@ -174,7 +174,6 @@ likelihood function.

quiet = FALSE, atol = 1e-08, rtol = 1e-10, - n.outtimes = 100, error_model = c("const", "obs", "tc"), error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep", "fourstep", "IRLS", "OLS"), @@ -323,13 +322,6 @@ is 1e-8, lower than in lsoda.

rtol

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

- - - n.outtimes -

The length of the dataseries that is produced by the model -prediction function mkinpredict. This impacts the accuracy -of the numerical solver if that is used (see solution_type -argument. The default value is 100.

error_model @@ -422,7 +414,87 @@ estimators.

Examples


 # Use shorthand notation for parent only degradation
-fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
#> Error in (function (t, parent_0, alpha, beta) {    parent = parent_0/(t/beta + 1)^alpha})(t = c(0, 1, 1.2020202020202, 2.4040404040404, 3, 3.60606060606061, 4.80808080808081, 6.01010101010101, 7, 7.21212121212121, 8.41414141414141, 9.61616161616162, 10.8181818181818, 12.020202020202, 13.2222222222222, 14, 14.4242424242424, 15.6262626262626, 16.8282828282828, 18.030303030303, 19.2323232323232, 20.4343434343434, 21.6363636363636, 22.8383838383838, 24.040404040404, 25.2424242424242, 26.4444444444444, 27.6464646464646, 28, 28.8484848484848, 30.050505050505, 31.2525252525253, 32.4545454545455, 33.6565656565657, 34.8585858585859, 36.0606060606061, 37.2626262626263, 38.4646464646465, 39.6666666666667, 40.8686868686869, 42.0707070707071, 43.2727272727273, 44.4747474747475, 45.6767676767677, 46.8787878787879, 48.0808080808081, 49.2828282828283, 50.4848484848485, 51.6868686868687, 52.8888888888889, 54.0909090909091, 55.2929292929293, 56.4949494949495, 57.6969696969697, 58.8989898989899, 60.1010101010101, 61.3030303030303, 62.5050505050505, 63, 63.7070707070707, 64.9090909090909, 66.1111111111111, 67.3131313131313, 68.5151515151515, 69.7171717171717, 70.9191919191919, 72.1212121212121, 73.3232323232323, 74.5252525252525, 75.7272727272727, 76.9292929292929, 78.1313131313131, 79.3333333333333, 80.5353535353535, 81.7373737373737, 82.9393939393939, 84.1414141414141, 85.3434343434343, 86.5454545454545, 87.7474747474747, 88.9494949494949, 90.1515151515152, 91, 91.3535353535353, 92.5555555555556, 93.7575757575758, 94.959595959596, 96.1616161616162, 97.3636363636364, 98.5656565656566, 99.7676767676768, 100.969696969697, 102.171717171717, 103.373737373737, 104.575757575758, 105.777777777778, 106.979797979798, 108.181818181818, 109.383838383838, 110.585858585859, 111.787878787879, 112.989898989899, 114.191919191919, 115.393939393939, 116.59595959596, 117.79797979798, 119), parent.0 = c(parent = 85.1),     alpha = 1, beta = 10): unbenutztes Argument (parent.0 = 85.1)
#> Timing stopped at: 0 0 0.001
summary(fit)
#> Error in summary(fit): Objekt 'fit' nicht gefunden

+fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+summary(fit)
#> mkin version used for fitting:    0.9.50 
+#> R version used for fitting:       4.0.0 
+#> Date of fit:     Mon May 11 05:14:26 2020 
+#> Date of summary: Mon May 11 05:14:26 2020 
+#> 
+#> Equations:
+#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
+#> 
+#> Model predictions using solution type analytical 
+#> 
+#> Fitted using 222 model solutions performed in 0.043 s
+#> 
+#> Error model: Constant variance 
+#> 
+#> Error model algorithm: OLS 
+#> 
+#> Starting values for parameters to be optimised:
+#>          value   type
+#> parent_0  85.1  state
+#> alpha      1.0 deparm
+#> beta      10.0 deparm
+#> 
+#> Starting values for the transformed parameters actually optimised:
+#>               value lower upper
+#> parent_0  85.100000  -Inf   Inf
+#> log_alpha  0.000000  -Inf   Inf
+#> log_beta   2.302585  -Inf   Inf
+#> 
+#> Fixed parameter values:
+#> None
+#> 
+#> Results:
+#> 
+#>        AIC      BIC    logLik
+#>   44.68652 45.47542 -18.34326
+#> 
+#> Optimised, transformed parameters with symmetric confidence intervals:
+#>           Estimate Std. Error    Lower   Upper
+#> parent_0  85.87000     1.8070 81.23000 90.5200
+#> log_alpha  0.05192     0.1353 -0.29580  0.3996
+#> log_beta   0.65100     0.2287  0.06315  1.2390
+#> sigma      1.85700     0.4378  0.73200  2.9830
+#> 
+#> Parameter correlation:
+#>             parent_0  log_alpha   log_beta     sigma
+#> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.758e-08
+#> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.007e-07
+#> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.568e-08
+#> sigma      4.758e-08  1.007e-07  8.568e-08 1.000e+00
+#> 
+#> Backtransformed parameters:
+#> Confidence intervals for internally transformed parameters are asymmetric.
+#> t-test (unrealistically) based on the assumption of normal distribution
+#> for estimators of untransformed parameters.
+#>          Estimate t value    Pr(>t)   Lower  Upper
+#> parent_0   85.870  47.530 3.893e-08 81.2300 90.520
+#> alpha       1.053   7.393 3.562e-04  0.7439  1.491
+#> beta        1.917   4.373 3.601e-03  1.0650  3.451
+#> sigma       1.857   4.243 4.074e-03  0.7320  2.983
+#> 
+#> FOCUS Chi2 error levels in percent:
+#>          err.min n.optim df
+#> All data   6.657       3  6
+#> parent     6.657       3  6
+#> 
+#> Estimated disappearance times:
+#>         DT50  DT90 DT50back
+#> parent 1.785 15.15     4.56
+#> 
+#> Data:
+#>  time variable observed predicted residual
+#>     0   parent     85.1    85.875  -0.7749
+#>     1   parent     57.9    55.191   2.7091
+#>     3   parent     29.9    31.845  -1.9452
+#>     7   parent     14.6    17.012  -2.4124
+#>    14   parent      9.7     9.241   0.4590
+#>    28   parent      6.6     4.754   1.8460
+#>    63   parent      4.0     2.102   1.8977
+#>    91   parent      3.9     1.441   2.4590
+#>   119   parent      0.6     1.092  -0.4919

 # One parent compound, one metabolite, both single first order.
 # Use mkinsub for convenience in model formulation. Pathway to sink included per default.
 SFO_SFO <- mkinmod(
@@ -430,86 +502,105 @@ estimators.

   m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults
 print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D,
                            solution_type = "eigen", quiet = TRUE)))
#> Warning: Observations with value of zero were removed from the data
#>        User      System verstrichen 
-#>       1.526       0.000       1.526 
parms(fit)
#>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink         sigma 
-#>  99.598483069   0.047920122   0.050777612   0.005260651   3.125503875 
endpoints(fit)
#> $ff
-#> parent_sink   parent_m1     m1_sink 
-#>    0.485524    0.514476    1.000000 
+#>       0.407       0.002       0.409 
parms(fit)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#>   99.598483222    0.098697734    0.005260651    0.514475962    3.125503875 
endpoints(fit)
#> $ff
+#>   parent_m1 parent_sink 
+#>    0.514476    0.485524 
 #> 
 #> $distimes
 #>              DT50      DT90
 #> parent   7.022929  23.32967
-#> m1     131.760712 437.69961
+#> m1     131.760715 437.69962
 #> 
# \dontrun{
 # deSolve is slower when no C compiler (gcc) was available during model generation
 print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D,
-                           solution_type = "deSolve")))
#> Warning: Observations with value of zero were removed from the data
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 18915.53
-#> Sum of squared residuals at call 2: 18915.53
-#> Sum of squared residuals at call 6: 11424.02
-#> Sum of squared residuals at call 10: 11424
-#> Sum of squared residuals at call 12: 4094.396
-#> Sum of squared residuals at call 16: 4094.396
-#> Sum of squared residuals at call 19: 1340.595
-#> Sum of squared residuals at call 20: 1340.593
-#> Sum of squared residuals at call 25: 1072.239
-#> Sum of squared residuals at call 28: 1072.236
-#> Sum of squared residuals at call 30: 874.2615
-#> Sum of squared residuals at call 33: 874.2611
-#> Sum of squared residuals at call 35: 616.2375
-#> Sum of squared residuals at call 37: 616.237
-#> Sum of squared residuals at call 40: 467.4386
-#> Sum of squared residuals at call 42: 467.438
-#> Sum of squared residuals at call 46: 398.2913
-#> Sum of squared residuals at call 48: 398.2913
-#> Sum of squared residuals at call 49: 398.2912
-#> Sum of squared residuals at call 51: 395.0711
-#> Sum of squared residuals at call 54: 395.071
-#> Sum of squared residuals at call 56: 378.3298
-#> Sum of squared residuals at call 59: 378.3298
-#> Sum of squared residuals at call 62: 376.9812
-#> Sum of squared residuals at call 64: 376.9811
-#> Sum of squared residuals at call 67: 375.2085
-#> Sum of squared residuals at call 69: 375.2085
-#> Sum of squared residuals at call 70: 375.2085
-#> Sum of squared residuals at call 71: 375.2085
-#> Sum of squared residuals at call 72: 374.5723
-#> Sum of squared residuals at call 74: 374.5723
-#> Sum of squared residuals at call 77: 374.0075
-#> Sum of squared residuals at call 79: 374.0075
-#> Sum of squared residuals at call 80: 374.0075
-#> Sum of squared residuals at call 82: 373.1711
-#> Sum of squared residuals at call 84: 373.1711
-#> Sum of squared residuals at call 87: 372.6445
-#> Sum of squared residuals at call 88: 372.1614
-#> Sum of squared residuals at call 90: 372.1614
-#> Sum of squared residuals at call 91: 372.1614
-#> Sum of squared residuals at call 94: 371.6464
-#> Sum of squared residuals at call 99: 371.4299
-#> Sum of squared residuals at call 101: 371.4299
-#> Sum of squared residuals at call 104: 371.4071
-#> Sum of squared residuals at call 106: 371.4071
-#> Sum of squared residuals at call 107: 371.4071
-#> Sum of squared residuals at call 109: 371.2524
-#> Sum of squared residuals at call 113: 371.2524
-#> Sum of squared residuals at call 114: 371.2136
-#> Sum of squared residuals at call 115: 371.2136
-#> Sum of squared residuals at call 116: 371.2136
-#> Sum of squared residuals at call 119: 371.2134
-#> Sum of squared residuals at call 120: 371.2134
-#> Sum of squared residuals at call 122: 371.2134
-#> Sum of squared residuals at call 123: 371.2134
-#> Sum of squared residuals at call 125: 371.2134
-#> Sum of squared residuals at call 126: 371.2134
-#> Sum of squared residuals at call 135: 371.2134
-#> Negative log-likelihood at call 145: 97.22429
#> Optimisation successfully terminated.
#>        User      System verstrichen 
-#>       1.084       0.000       1.085 
parms(fit.deSolve)
#>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink         sigma 
-#>  99.598483072   0.047920122   0.050777612   0.005260651   3.125503874 
endpoints(fit.deSolve)
#> $ff
-#> parent_sink   parent_m1     m1_sink 
-#>    0.485524    0.514476    1.000000 
+                           solution_type = "deSolve")))
#> Warning: Observations with value of zero were removed from the data
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 15156.12
+#> Sum of squared residuals at call 2: 15156.12
+#> Sum of squared residuals at call 6: 8243.645
+#> Sum of squared residuals at call 12: 6290.712
+#> Sum of squared residuals at call 13: 6290.683
+#> Sum of squared residuals at call 15: 6290.452
+#> Sum of squared residuals at call 18: 1700.749
+#> Sum of squared residuals at call 20: 1700.611
+#> Sum of squared residuals at call 24: 1190.923
+#> Sum of squared residuals at call 26: 1190.922
+#> Sum of squared residuals at call 29: 1017.417
+#> Sum of squared residuals at call 31: 1017.417
+#> Sum of squared residuals at call 33: 1017.416
+#> Sum of squared residuals at call 34: 644.0471
+#> Sum of squared residuals at call 36: 644.0469
+#> Sum of squared residuals at call 38: 644.0469
+#> Sum of squared residuals at call 39: 590.5025
+#> Sum of squared residuals at call 41: 590.5022
+#> Sum of squared residuals at call 43: 590.5016
+#> Sum of squared residuals at call 44: 543.219
+#> Sum of squared residuals at call 45: 543.2187
+#> Sum of squared residuals at call 46: 543.2186
+#> Sum of squared residuals at call 50: 391.348
+#> Sum of squared residuals at call 51: 391.3479
+#> Sum of squared residuals at call 56: 386.4789
+#> Sum of squared residuals at call 58: 386.4789
+#> Sum of squared residuals at call 60: 386.4779
+#> Sum of squared residuals at call 61: 384.0686
+#> Sum of squared residuals at call 63: 384.0686
+#> Sum of squared residuals at call 66: 382.7813
+#> Sum of squared residuals at call 68: 382.7813
+#> Sum of squared residuals at call 70: 382.7813
+#> Sum of squared residuals at call 71: 378.9273
+#> Sum of squared residuals at call 73: 378.9273
+#> Sum of squared residuals at call 75: 378.9272
+#> Sum of squared residuals at call 76: 377.4847
+#> Sum of squared residuals at call 78: 377.4846
+#> Sum of squared residuals at call 81: 375.9738
+#> Sum of squared residuals at call 83: 375.9738
+#> Sum of squared residuals at call 86: 375.3387
+#> Sum of squared residuals at call 88: 375.3387
+#> Sum of squared residuals at call 91: 374.5774
+#> Sum of squared residuals at call 93: 374.5774
+#> Sum of squared residuals at call 95: 374.5774
+#> Sum of squared residuals at call 96: 373.5433
+#> Sum of squared residuals at call 100: 373.5433
+#> Sum of squared residuals at call 102: 373.2654
+#> Sum of squared residuals at call 104: 373.2654
+#> Sum of squared residuals at call 107: 372.6841
+#> Sum of squared residuals at call 111: 372.684
+#> Sum of squared residuals at call 114: 372.6374
+#> Sum of squared residuals at call 116: 372.6374
+#> Sum of squared residuals at call 119: 372.6223
+#> Sum of squared residuals at call 121: 372.6223
+#> Sum of squared residuals at call 123: 372.6223
+#> Sum of squared residuals at call 124: 372.5903
+#> Sum of squared residuals at call 126: 372.5903
+#> Sum of squared residuals at call 129: 372.5445
+#> Sum of squared residuals at call 130: 372.4921
+#> Sum of squared residuals at call 131: 372.2377
+#> Sum of squared residuals at call 132: 371.5434
+#> Sum of squared residuals at call 134: 371.5434
+#> Sum of squared residuals at call 137: 371.2857
+#> Sum of squared residuals at call 139: 371.2857
+#> Sum of squared residuals at call 143: 371.2247
+#> Sum of squared residuals at call 144: 371.2247
+#> Sum of squared residuals at call 149: 371.2189
+#> Sum of squared residuals at call 150: 371.2145
+#> Sum of squared residuals at call 153: 371.2145
+#> Sum of squared residuals at call 155: 371.2138
+#> Sum of squared residuals at call 156: 371.2138
+#> Sum of squared residuals at call 157: 371.2138
+#> Sum of squared residuals at call 161: 371.2134
+#> Sum of squared residuals at call 162: 371.2134
+#> Sum of squared residuals at call 165: 371.2134
+#> Sum of squared residuals at call 166: 371.2134
+#> Sum of squared residuals at call 168: 371.2134
+#> Negative log-likelihood at call 178: 97.22429
#> Optimisation successfully terminated.
#>        User      System verstrichen 
+#>       0.349       0.000       0.350 
parms(fit.deSolve)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#>   99.598480759    0.098697739    0.005260651    0.514475958    3.125503874 
endpoints(fit.deSolve)
#> $ff
+#>   parent_m1 parent_sink 
+#>    0.514476    0.485524 
 #> 
 #> $distimes
 #>              DT50      DT90
-#> parent   7.022929  23.32967
-#> m1     131.760712 437.69961
+#> parent   7.022929  23.32966
+#> m1     131.760731 437.69967
 #> 
# }
 
 # Use stepwise fitting, using optimised parameters from parent only fit, FOMC
@@ -518,31 +609,33 @@ estimators.

   parent = mkinsub("FOMC", "m1"),
   m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults
 fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
# Use starting parameters from parent only FOMC fit
-fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE)
#> Error in (function (t, parent_0, alpha, beta) {    parent = parent_0/(t/beta + 1)^alpha})(t = c(0, 0.757575757575758, 1, 1.51515151515152, 2.27272727272727, 3, 3.03030303030303, 3.78787878787879, 4.54545454545454, 5.3030303030303, 6.06060606060606, 6.81818181818182, 7, 7.57575757575758, 8.33333333333333, 9.09090909090909, 9.84848484848485, 10.6060606060606, 11.3636363636364, 12.1212121212121, 12.8787878787879, 13.6363636363636, 14, 14.3939393939394, 15.1515151515152, 15.9090909090909, 16.6666666666667, 17.4242424242424, 18.1818181818182, 18.9393939393939, 19.6969696969697, 20.4545454545455, 21, 21.2121212121212, 21.969696969697, 22.7272727272727, 23.4848484848485, 24.2424242424242, 25, 25.7575757575758, 26.5151515151515, 27.2727272727273, 28.030303030303, 28.7878787878788, 29.5454545454545, 30.3030303030303, 31.0606060606061, 31.8181818181818, 32.5757575757576, 33.3333333333333, 34.0909090909091, 34.8484848484849, 35, 35.6060606060606, 36.3636363636364, 37.1212121212121, 37.8787878787879, 38.6363636363636, 39.3939393939394, 40.1515151515151, 40.9090909090909, 41.6666666666667, 42.4242424242424, 43.1818181818182, 43.9393939393939, 44.6969696969697, 45.4545454545455, 46.2121212121212, 46.969696969697, 47.7272727272727, 48.4848484848485, 49.2424242424242, 50, 50.7575757575758, 51.5151515151515, 52.2727272727273, 53.030303030303, 53.7878787878788, 54.5454545454545, 55.3030303030303, 56.0606060606061, 56.8181818181818, 57.5757575757576, 58.3333333333333, 59.0909090909091, 59.8484848484849, 60.6060606060606, 61.3636363636364, 62.1212121212121, 62.8787878787879, 63.6363636363636, 64.3939393939394, 65.1515151515152, 65.9090909090909, 66.6666666666667, 67.4242424242424, 68.1818181818182, 68.9393939393939, 69.6969696969697, 70.4545454545455, 71.2121212121212, 71.969696969697, 72.7272727272727, 73.4848484848485, 74.2424242424242, 75), parent.0 = c(parent = 100.75), alpha = 1,     beta = 10): unbenutztes Argument (parent.0 = 100.75)
#> Timing stopped at: 0 0 0.001
fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE,
-  parms.ini = fit.FOMC$bparms.ode)
#> Warning: Observations with value of zero were removed from the data
#> Error in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE, parms.ini = fit.FOMC$bparms.ode): Objekt 'fit.FOMC' nicht gefunden

+fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE)
+fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE,
+  parms.ini = fit.FOMC$bparms.ode)
#> Warning: Observations with value of zero were removed from the data

 # Use stepwise fitting, using optimised parameters from parent only fit, SFORB
 SFORB_SFO <- mkinmod(
   parent = list(type = "SFORB", to = "m1", sink = TRUE),
   m1 = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults
 fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve",
                                  quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
# Use starting parameters from parent only SFORB fit (not really needed in this case)
-fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE)
#> Error in (function (t, parent_0, k_12, k_21, k_1output) {    sqrt_exp = sqrt(1/4 * (k_12 + k_21 + k_1output)^2 + k_12 *         k_21 - (k_12 + k_1output) * k_21)    b1 = 0.5 * (k_12 + k_21 + k_1output) + sqrt_exp    b2 = 0.5 * (k_12 + k_21 + k_1output) - sqrt_exp    parent = parent_0 * (((k_12 + k_21 - b1)/(b2 - b1)) * exp(-b1 *         t) + ((k_12 + k_21 - b2)/(b1 - b2)) * exp(-b2 * t))})(t = c(0, 0.757575757575758, 1, 1.51515151515152, 2.27272727272727, 3, 3.03030303030303, 3.78787878787879, 4.54545454545454, 5.3030303030303, 6.06060606060606, 6.81818181818182, 7, 7.57575757575758, 8.33333333333333, 9.09090909090909, 9.84848484848485, 10.6060606060606, 11.3636363636364, 12.1212121212121, 12.8787878787879, 13.6363636363636, 14, 14.3939393939394, 15.1515151515152, 15.9090909090909, 16.6666666666667, 17.4242424242424, 18.1818181818182, 18.9393939393939, 19.6969696969697, 20.4545454545455, 21, 21.2121212121212, 21.969696969697, 22.7272727272727, 23.4848484848485, 24.2424242424242, 25, 25.7575757575758, 26.5151515151515, 27.2727272727273, 28.030303030303, 28.7878787878788, 29.5454545454545, 30.3030303030303, 31.0606060606061, 31.8181818181818, 32.5757575757576, 33.3333333333333, 34.0909090909091, 34.8484848484849, 35, 35.6060606060606, 36.3636363636364, 37.1212121212121, 37.8787878787879, 38.6363636363636, 39.3939393939394, 40.1515151515151, 40.9090909090909, 41.6666666666667, 42.4242424242424, 43.1818181818182, 43.9393939393939, 44.6969696969697, 45.4545454545455, 46.2121212121212, 46.969696969697, 47.7272727272727, 48.4848484848485, 49.2424242424242, 50, 50.7575757575758, 51.5151515151515, 52.2727272727273, 53.030303030303, 53.7878787878788, 54.5454545454545, 55.3030303030303, 56.0606060606061, 56.8181818181818, 57.5757575757576, 58.3333333333333, 59.0909090909091, 59.8484848484849, 60.6060606060606, 61.3636363636364, 62.1212121212121, 62.8787878787879, 63.6363636363636, 64.3939393939394, 65.1515151515152, 65.9090909090909, 66.6666666666667, 67.4242424242424, 68.1818181818182, 68.9393939393939, 69.6969696969697, 70.4545454545455, 71.2121212121212, 71.969696969697, 72.7272727272727, 73.4848484848485, 74.2424242424242, 75), parent.0 = c(parent_free = 100.75), k_1output = 0.1,     k_12 = 0.1, k_21 = 0.02): unbenutztes Argument (parent.0 = 100.75)
#> Timing stopped at: 0.001 0 0.001
fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Error in mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode,     quiet = TRUE): Objekt 'fit.SFORB' nicht gefunden
# }
+fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE)
+fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Initial parameter(s) k_parent_free_sink not used in the model
# }
 
 # \dontrun{
 # Weighted fits, including IRLS
 SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"),
-                      m1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.noweight)
#> mkin version used for fitting:    0.9.49.11 
+                      m1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.noweight)
#> mkin version used for fitting:    0.9.50 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Thu May  7 08:59:03 2020 
-#> Date of summary: Thu May  7 08:59:03 2020 
+#> Date of fit:     Mon May 11 05:14:31 2020 
+#> Date of summary: Mon May 11 05:14:31 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
 #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #> 
-#> Model predictions using solution type deSolve 
+#> Model predictions using solution type analytical 
 #> 
-#> Fitted using 422 model solutions performed in 1.106 s
+#> Fitted using 421 model solutions performed in 0.124 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -566,6 +659,11 @@ estimators.

 #>      value  type
 #> m1_0     0 state
 #> 
+#> Results:
+#> 
+#>        AIC      BIC    logLik
+#>   204.4486 212.6365 -97.22429
+#> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>                Estimate Std. Error    Lower    Upper
 #> parent_0       99.60000    1.57000 96.40000 102.8000
@@ -576,11 +674,11 @@ estimators.

 #> 
 #> Parameter correlation:
 #>                  parent_0 log_k_parent   log_k_m1 f_parent_ilr_1      sigma
-#> parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -2.443e-07
-#> log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.181e-07
-#> log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.369e-07
-#> f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00 -2.287e-08
-#> sigma          -2.443e-07    3.181e-07 -1.369e-07     -2.287e-08  1.000e+00
+#> parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -3.214e-07
+#> log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.168e-07
+#> log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.410e-07
+#> f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00  5.093e-10
+#> sigma          -3.214e-07    3.168e-07 -1.410e-07      5.093e-10  1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
@@ -648,18 +746,18 @@ estimators.

 #>   100       m1    31.04  31.98163 -9.416e-01
 #>   100       m1    33.13  31.98163  1.148e+00
 #>   120       m1    25.15  28.78984 -3.640e+00
-#>   120       m1    33.31  28.78984  4.520e+00
f.obs <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.obs)
#> mkin version used for fitting:    0.9.49.11 
+#>   120       m1    33.31  28.78984  4.520e+00
f.obs <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.obs)
#> mkin version used for fitting:    0.9.50 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Thu May  7 08:59:06 2020 
-#> Date of summary: Thu May  7 08:59:06 2020 
+#> Date of fit:     Mon May 11 05:14:32 2020 
+#> Date of summary: Mon May 11 05:14:32 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
 #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #> 
-#> Model predictions using solution type deSolve 
+#> Model predictions using solution type analytical 
 #> 
-#> Fitted using 979 model solutions performed in 2.604 s
+#> Fitted using 978 model solutions performed in 0.336 s
 #> 
 #> Error model: Variance unique to each observed variable 
 #> 
@@ -688,6 +786,11 @@ estimators.

 #>      value  type
 #> m1_0     0 state
 #> 
+#> Results:
+#> 
+#>        AIC      BIC    logLik
+#>   205.8727 215.6982 -96.93634
+#> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>                Estimate Std. Error    Lower    Upper
 #> parent_0       99.65000    1.70200 96.19000 103.1000
@@ -780,23 +883,23 @@ estimators.

 #>   100       m1    31.04  31.98773 -9.477e-01
 #>   100       m1    33.13  31.98773  1.142e+00
 #>   120       m1    25.15  28.80429 -3.654e+00
-#>   120       m1    33.31  28.80429  4.506e+00
f.tc <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.tc)
#> mkin version used for fitting:    0.9.49.11 
+#>   120       m1    33.31  28.80429  4.506e+00
f.tc <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.tc)
#> mkin version used for fitting:    0.9.50 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Thu May  7 08:59:16 2020 
-#> Date of summary: Thu May  7 08:59:16 2020 
+#> Date of fit:     Mon May 11 05:14:32 2020 
+#> Date of summary: Mon May 11 05:14:32 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
 #> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
 #> 
-#> Model predictions using solution type deSolve 
+#> Model predictions using solution type analytical 
 #> 
-#> Fitted using 2552 model solutions performed in 10.544 s
+#> Fitted using 1875 model solutions performed in 0.642 s
 #> 
 #> Error model: Two-component variance function 
 #> 
 #> Error model algorithm: d_3 
-#> Three-step fitting yielded a higher likelihood than direct fitting 
+#> Direct fitting and three-step fitting yield approximately the same likelihood 
 #> 
 #> Starting values for parameters to be optimised:
 #>                   value   type
@@ -820,6 +923,11 @@ estimators.

 #>      value  type
 #> m1_0     0 state
 #> 
+#> Results:
+#> 
+#>        AIC      BIC    logLik
+#>   141.9656 151.7911 -64.98278
+#> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>                 Estimate Std. Error     Lower     Upper
 #> parent_0       100.70000   2.621000 95.400000 106.10000
@@ -868,14 +976,14 @@ estimators.

 #> 
 #> Data:
 #>  time variable observed predicted   residual
-#>     0   parent    99.46 100.73434  -1.274339
-#>     0   parent   102.04 100.73434   1.305661
+#>     0   parent    99.46 100.73434  -1.274340
+#>     0   parent   102.04 100.73434   1.305660
 #>     1   parent    93.50  91.09751   2.402486
 #>     1   parent    92.50  91.09751   1.402486
 #>     3   parent    63.23  74.50141 -11.271410
 #>     3   parent    68.99  74.50141  -5.511410
-#>     7   parent    52.32  49.82880   2.491201
-#>     7   parent    55.13  49.82880   5.301201
+#>     7   parent    52.32  49.82880   2.491200
+#>     7   parent    55.13  49.82880   5.301200
 #>    14   parent    27.27  24.64809   2.621908
 #>    14   parent    26.64  24.64809   1.991908
 #>    21   parent    11.50  12.19232  -0.692315
@@ -902,8 +1010,8 @@ estimators.

 #>    50       m1    40.01  41.34199  -1.331985
 #>    75       m1    40.09  36.61471   3.475295
 #>    75       m1    33.85  36.61471  -2.764705
-#>   100       m1    31.04  32.20082  -1.160824
-#>   100       m1    33.13  32.20082   0.929176
+#>   100       m1    31.04  32.20082  -1.160823
+#>   100       m1    33.13  32.20082   0.929177
 #>   120       m1    25.15  29.04130  -3.891304
 #>   120       m1    33.31  29.04130   4.268696
# }
 
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 2f2e89d9..81b62ae1 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -237,7 +237,7 @@ in the FOCUS and NAFTA guidance documents are used.

     
Examples

     

 # Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)
-SFO <- mkinmod(parent = list(type = "SFO"))
+SFO <- mkinmod(parent = mkinsub("SFO"))
 
 # One parent compound, one metabolite, both single first order
 SFO_SFO <- mkinmod(
@@ -252,7 +252,7 @@ in the FOCUS and NAFTA guidance documents are used.

 SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
   m1 = mkinsub("SFO"), verbose = TRUE)
#> Compilation argument:
-#>  /usr/lib/R/bin/R CMD SHLIB fileb6a4eaab60.c 2> fileb6a4eaab60.c.err.txt 
+#>  /usr/lib/R/bin/R CMD SHLIB file1a1abf38df.c 2> file1a1abf38df.c.err.txt 
 #> Program source:
 #>   1: #include <R.h>
 #>   2: 
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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diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index 33dc52b7..2ede2dec 100644
--- a/docs/reference/plot.mkinfit.html
+++ b/docs/reference/plot.mkinfit.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
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-
+
+
 
 
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+
 
 
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+
+
 
 
 
@@ -39,7 +43,6 @@
 
-
 
 
 
@@ -57,7 +60,7 @@ observed data together with the solution of the fitted model." />
 
   
 
-  
+  
     

       

       
 
@@ -115,7 +118,12 @@ observed data together with the solution of the fitted model." />
 
       
       
       
     

@@ -130,7 +138,7 @@ observed data together with the solution of the fitted model." />
   

     

     
Plot the observed data and the fitted model of an mkinfit object

-    
+    Source: R/plot.mkinfit.R
     
     

 
@@ -141,7 +149,7 @@ observed data together with the solution of the fitted model.

     

 
     
# S3 method for mkinfit
-plot(
+plot(
   x,
   fit = x,
   obs_vars = names(fit$mkinmod$map),
@@ -303,7 +311,7 @@ chi2 error percentage.

     
     
       ...
-      
Further arguments passed to plot.

+      
Further arguments passed to plot.

     
     
       standardized
@@ -327,29 +335,22 @@ latex is being used for the formatting of the chi2 error level, if
 # parent to sink included
 # \dontrun{
 SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
-                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
#> Warning: Observations with value of zero were removed from the data
plot(fit)
plot_res(fit)
plot_res(fit, standardized = FALSE)
plot_err(fit)

+                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
#> Warning: Observations with value of zero were removed from the data
plot(fit)
plot_res(fit)
plot_res(fit, standardized = FALSE)
plot_err(fit)

 # Show the observed variables separately, with residuals
-plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
+plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
      show_errmin = TRUE)

 # The same can be obtained with less typing, using the convenience function plot_sep
 plot_sep(fit, lpos = c("topright", "bottomright"))

 # Show the observed variables separately, with the error model
-plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),
+plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),
      show_errmin = TRUE)
# }
- @@ -360,7 +361,7 @@ latex is being used for the formatting of the chi2 error level, if
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

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