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. --- R/add_err.R | 2 + R/mkin_wide_to_long.R | 1 + R/mkinfit.R | 8 +- R/mkinpredict.R | 48 +-- R/plot.mkinfit.R | 1 + _pkgdown.yml | 1 + docs/articles/FOCUS_D.html | 110 ++++--- docs/articles/FOCUS_D_files/figure-html/plot-1.png | Bin 101585 -> 101350 bytes .../FOCUS_D_files/figure-html/plot_2-1.png | Bin 14220 -> 15733 bytes docs/articles/mkin.html | 113 ++++--- .../mkin_files/figure-html/unnamed-chunk-2-1.png | Bin 113818 -> 116140 bytes docs/articles/web_only/benchmarks.html | 248 +++++++--------- docs/news/index.html | 1 - docs/pkgdown.yml | 2 +- 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 ++-- man/mkinpredict.Rd | 13 +- test.log | 20 +- tests/testthat/FOCUS_2006_D.csf | 2 +- tests/testthat/test_mkinpredict_SFO_SFO.R | 26 +- tests/testthat/test_plots_summary_twa.R | 2 +- vignettes/FOCUS_D.html | 114 ++++--- vignettes/mkin.html | 112 +++---- vignettes/mkin_benchmarks.rda | Bin 871 -> 882 bytes vignettes/web_only/benchmarks.html | 22 +- 36 files changed, 799 insertions(+), 715 deletions(-) diff --git a/R/add_err.R b/R/add_err.R index a523e9c2..9235223f 100644 --- a/R/add_err.R +++ b/R/add_err.R @@ -76,6 +76,8 @@ add_err <- function(prediction, sdfunc, secondary = c("M1", "M2"), { if (!is.na(seed)) set.seed(seed) + prediction <- as.data.frame(prediction) + # The output of mkinpredict is in wide format d_long = mkin_wide_to_long(prediction, time = "time") diff --git a/R/mkin_wide_to_long.R b/R/mkin_wide_to_long.R index bef0e408..971f5273 100644 --- a/R/mkin_wide_to_long.R +++ b/R/mkin_wide_to_long.R @@ -21,6 +21,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' @export mkin_wide_to_long <- function(wide_data, time = "t") { + wide_data <- as.data.frame(wide_data) colnames <- names(wide_data) if (!(time %in% colnames)) stop("The data in wide format have to contain a variable named ", time, ".") vars <- subset(colnames, colnames != time) diff --git a/R/mkinfit.R b/R/mkinfit.R index e1089673..f6691b1b 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -592,12 +592,8 @@ mkinfit <- function(mkinmod, observed, method.ode = method.ode, atol = atol, rtol = rtol, ...) - out_long <- mkin_wide_to_long(out, time = "time") - - out_merged <- merge(observed[c("name", "time")], out_long) - out_merged$name <- ordered(out_merged$name, levels = obs_vars) - out_merged <- out_merged[order(out_merged$name, out_merged$time), ] - observed$predicted <- out_merged$value + observed_index <- cbind(as.character(observed$time), as.character(observed$name)) + observed$predicted <- out[observed_index] } # Define standard deviation for each observation diff --git a/R/mkinpredict.R b/R/mkinpredict.R index 75582fac..90cd45fb 100644 --- a/R/mkinpredict.R +++ b/R/mkinpredict.R @@ -32,12 +32,13 @@ #' is 1e-10, much lower than in \code{\link{lsoda}}. #' @param 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. #' @param \dots Further arguments passed to the ode solver in case such a #' solver is used. #' @import deSolve #' @importFrom inline getDynLib -#' @return A data frame with the solution in wide format +#' @return A matrix with the numeric solution in wide format #' @author Johannes Ranke #' @examples #' @@ -70,7 +71,7 @@ #' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), #' seq(0, 20, by = 0.01))[2001,] #' -#' # Check compiled model versions - they are faster than the eigenvalue based solutions! +#' # Comparison of the performance of solution types #' SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"), #' m1 = list(type = "SFO"), use_of_ff = "max") #' if(require(rbenchmark)) { @@ -92,15 +93,11 @@ #' c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), #' solution_type = "analytical", 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,] #' #' \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 = "analytical") +#' f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve") #' head(mkinpredict(f)) #' } #' @@ -127,29 +124,34 @@ mkinpredict.mkinmod <- function(x, map_output = TRUE, ...) { - # Get the names of the state variables in the model + # Names of state variables and observed variables mod_vars <- names(x$diffs) + obs_vars <- names(x$spec) # Order the inital values for state variables if they are named if (!is.null(names(odeini))) { odeini <- odeini[mod_vars] } + out_obs <- matrix(NA, nrow = length(outtimes), ncol = 1 + length(obs_vars), + dimnames = list(as.character(outtimes), c("time", obs_vars))) + out_obs[, "time"] <- outtimes + if (solution_type == "analytical") { # This is clumsy, as we wanted fast analytical predictions for mkinfit - out <- data.frame(time = outtimes) - obs_vars <- names(x$spec) pseudo_observed <- data.frame(name = rep(obs_vars, each = length(outtimes)), time = rep(outtimes, length(obs_vars))) pseudo_observed$predicted <- x$deg_func(pseudo_observed, odeini, odeparms) for (obs_var in obs_vars) { - out[obs_var] <- pseudo_observed[pseudo_observed$name == obs_var, "predicted"] + out_obs[, obs_var] <- pseudo_observed[pseudo_observed$name == obs_var, "predicted"] } + # We don't have solutions for unobserved state variables, the output of + # analytical solutions is always mapped to observed variables + return(out_obs) } if (solution_type == "eigen") { - evalparse <- function(string) { eval(parse(text=string), as.list(c(odeparms, odeini))) } @@ -163,8 +165,8 @@ mkinpredict.mkinmod <- function(x, } o <- matrix(mapply(f.out, outtimes), nrow = length(mod_vars), ncol = length(outtimes)) - out <- data.frame(outtimes, t(o)) - names(out) <- c("time", mod_vars) + out <- cbind(outtimes, t(o)) + colnames(out) <- c("time", mod_vars) } if (solution_type == "deSolve") { @@ -208,21 +210,23 @@ mkinpredict.mkinmod <- function(x, if (sum(is.na(out)) > 0) { stop("Differential equations were not integrated for all output times because\n", "NaN values occurred in output from ode()") - } + } } + if (map_output) { # Output transformation for models with unobserved compartments like SFORB - out_mapped <- data.frame(time = out[,"time"]) + # if not already mapped in analytical solution for (var in names(x$map)) { - if((length(x$map[[var]]) == 1) || solution_type == "analytical") { - out_mapped[var] <- out[, var] + if((length(x$map[[var]]) == 1)) { + out_obs[, var] <- out[, var] } else { - out_mapped[var] <- rowSums(out[, x$map[[var]]]) + out_obs[, var] <- out[, x$map[[var]][1]] + out[, x$map[[var]][2]] } } - return(out_mapped) + return(out_obs) } else { - return(as.data.frame(out)) + dimnames(out) <- list(time = as.character(outtimes), c("time", mod_vars)) + return(out) } } diff --git a/R/plot.mkinfit.R b/R/plot.mkinfit.R index b2cb4890..48df9483 100644 --- a/R/plot.mkinfit.R +++ b/R/plot.mkinfit.R @@ -148,6 +148,7 @@ plot.mkinfit <- function(x, fit = x, solution_type = solution_type, atol = fit$atol, rtol = fit$rtol, use_compiled = FALSE) } + out <- as.data.frame(out) names(col_obs) <- names(pch_obs) <- names(lty_obs) <- obs_vars diff --git a/_pkgdown.yml b/_pkgdown.yml index 0623c6ed..2e6770d3 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -78,6 +78,7 @@ reference: - mkinresplot - mkinparplot - mkinerrplot + - create_deg_func - title: Analytical solutions desc: Parent only model solutions contents: diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index e9c6b005..0a9ddf4a 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -31,7 +31,7 @@ mkin - 0.9.49.11 + 0.9.50 @@ -97,7 +97,7 @@

Example evaluation of FOCUS Example Dataset D

Johannes Ranke

-

2020-05-07

+

2020-05-11

Source: vignettes/FOCUS_D.Rmd @@ -159,10 +159,10 @@ 
SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
## Successfully compiled differential equation model from auto-generated C code.
print(SFO_SFO$diffs)
-
##                                                       parent 
-## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
-##                                                           m1 
-##             "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"
+
##                                                    parent 
+##                          "d_parent = - k_parent * parent" 
+##                                                        m1 
+## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"

We do the fitting without progress report (quiet = TRUE).

fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value
@@ -175,85 +175,83 @@
 

 
A comprehensive report of the results is obtained using the summary method for mkinfit objects.

 
summary(fit)

-
## mkin version used for fitting:    0.9.49.11 
+
## mkin version used for fitting:    0.9.50 
 ## R version used for fitting:       4.0.0 
-## Date of fit:     Thu May  7 08:59:27 2020 
-## Date of summary: Thu May  7 08:59:27 2020 
+## Date of fit:     Mon May 11 05:14:41 2020 
+## Date of summary: Mon May 11 05:14:41 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
-## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+## 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 389 model solutions performed in 1.031 s
+## Fitted using 421 model solutions performed in 0.167 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## Starting values for parameters to be optimised:
-##                  value   type
-## parent_0      100.7500  state
-## k_parent_sink   0.1000 deparm
-## k_parent_m1     0.1001 deparm
-## k_m1_sink       0.1002 deparm
+##                   value   type
+## parent_0       100.7500  state
+## k_parent         0.1000 deparm
+## k_m1             0.1001 deparm
+## f_parent_to_m1   0.5000 deparm
 ## 
 ## Starting values for the transformed parameters actually optimised:
-##                        value lower upper
-## parent_0          100.750000  -Inf   Inf
-## log_k_parent_sink  -2.302585  -Inf   Inf
-## log_k_parent_m1    -2.301586  -Inf   Inf
-## log_k_m1_sink      -2.300587  -Inf   Inf
+##                     value lower upper
+## parent_0       100.750000  -Inf   Inf
+## log_k_parent    -2.302585  -Inf   Inf
+## log_k_m1        -2.301586  -Inf   Inf
+## f_parent_ilr_1   0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ##      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            99.600    1.57000 96.400 102.800
-## log_k_parent_sink   -3.038    0.07626 -3.193  -2.883
-## log_k_parent_m1     -2.980    0.04033 -3.062  -2.898
-## log_k_m1_sink       -5.248    0.13320 -5.518  -4.977
-## sigma                3.126    0.35850  2.396   3.855
+##                Estimate Std. Error    Lower    Upper
+## parent_0       99.60000    1.57000 96.40000 102.8000
+## log_k_parent   -2.31600    0.04087 -2.39900  -2.2330
+## log_k_m1       -5.24800    0.13320 -5.51800  -4.9770
+## f_parent_ilr_1  0.04096    0.06312 -0.08746   0.1694
+## sigma           3.12600    0.35850  2.39600   3.8550
 ## 
 ## Parameter correlation:
-##                     parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
-## parent_0           1.000e+00         6.067e-01      -6.372e-02    -1.688e-01
-## log_k_parent_sink  6.067e-01         1.000e+00      -8.550e-02    -6.252e-01
-## log_k_parent_m1   -6.372e-02        -8.550e-02       1.000e+00     4.731e-01
-## log_k_m1_sink     -1.688e-01        -6.252e-01       4.731e-01     1.000e+00
-## sigma              5.287e-10         3.306e-09       4.421e-08    -3.319e-10
-##                        sigma
-## parent_0           5.287e-10
-## log_k_parent_sink  3.306e-09
-## log_k_parent_m1    4.421e-08
-## log_k_m1_sink     -3.319e-10
-## sigma              1.000e+00
+##                  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 -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.
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
-##                Estimate t value    Pr(>t)     Lower     Upper
-## parent_0      99.600000  63.430 2.298e-36 96.400000 1.028e+02
-## k_parent_sink  0.047920  13.110 6.126e-15  0.041030 5.596e-02
-## k_parent_m1    0.050780  24.800 3.269e-23  0.046780 5.512e-02
-## k_m1_sink      0.005261   7.510 6.165e-09  0.004012 6.898e-03
-## sigma          3.126000   8.718 2.235e-10  2.396000 3.855e+00
+##                 Estimate t value    Pr(>t)     Lower     Upper
+## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
+## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
+## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
+## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
+## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
 ## 
 ## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   6.398       4 15
-## parent     6.827       3  6
-## m1         4.490       1  9
+## parent     6.459       2  7
+## m1         4.690       2  8
 ## 
 ## Resulting formation fractions:
 ##                 ff
-## parent_sink 0.4855
 ## parent_m1   0.5145
-## m1_sink     1.0000
+## parent_sink 0.4855
 ## 
 ## Estimated disappearance times:
 ##           DT50   DT90
@@ -266,10 +264,10 @@
 ##     0   parent   102.04  99.59848  2.442e+00
 ##     1   parent    93.50  90.23787  3.262e+00
 ##     1   parent    92.50  90.23787  2.262e+00
-##     3   parent    63.23  74.07320 -1.084e+01
-##     3   parent    68.99  74.07320 -5.083e+00
-##     7   parent    52.32  49.91207  2.408e+00
-##     7   parent    55.13  49.91207  5.218e+00
+##     3   parent    63.23  74.07319 -1.084e+01
+##     3   parent    68.99  74.07319 -5.083e+00
+##     7   parent    52.32  49.91206  2.408e+00
+##     7   parent    55.13  49.91206  5.218e+00
 ##    14   parent    27.27  25.01257  2.257e+00
 ##    14   parent    26.64  25.01257  1.627e+00
 ##    21   parent    11.50  12.53462 -1.035e+00
@@ -279,7 +277,7 @@
 ##    50   parent     0.69   0.71624 -2.624e-02
 ##    50   parent     0.63   0.71624 -8.624e-02
 ##    75   parent     0.05   0.06074 -1.074e-02
-##    75   parent     0.06   0.06074 -7.382e-04
+##    75   parent     0.06   0.06074 -7.381e-04
 ##     1       m1     4.84   4.80296  3.704e-02
 ##     1       m1     5.64   4.80296  8.370e-01
 ##     3       m1    12.91  13.02400 -1.140e-01
diff --git a/docs/articles/FOCUS_D_files/figure-html/plot-1.png b/docs/articles/FOCUS_D_files/figure-html/plot-1.png
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diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 42eb6c0a..b828b7dc 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -6,19 +6,19 @@
 
 
 Introduction to mkin • mkin
-
-
-
-
+
+
+
+
+
 
-
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+
 
 
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+
     

       

 
   

@@ -87,12 +94,12 @@
       

   

     

-      
Introduction to mkin

+      
Introduction to mkin

                         
Johannes Ranke

             
-            
2019-11-01

-      
+            
2020-05-11

       
+      Source: vignettes/mkin.Rmd
       
 
     

@@ -104,34 +111,34 @@
 

 Abstract

 
In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The R add-on package mkin implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.

-
+
library("mkin", quietly = TRUE)
+# Define the kinetic model
+m_SFO_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+                         M1 = mkinsub("SFO", "M2"),
+                         M2 = mkinsub("SFO"),
+                         use_of_ff = "max", quiet = TRUE)
+
+
+# Produce model predictions using some arbitrary parameters
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+d_SFO_SFO_SFO <- mkinpredict(m_SFO_SFO_SFO,
+  c(k_parent = 0.03,
+    f_parent_to_M1 = 0.5, k_M1 = log(2)/100,
+    f_M1_to_M2 = 0.9, k_M2 = log(2)/50),
+  c(parent = 100, M1 = 0, M2 = 0),
+  sampling_times)
+
+# Generate a dataset by adding normally distributed errors with
+# standard deviation 3, for two replicates at each sampling time
+d_SFO_SFO_SFO_err <- add_err(d_SFO_SFO_SFO, reps = 2,
+                             sdfunc = function(x) 3,
+                             n = 1, seed = 123456789 )
+
+# Fit the model to the dataset
+f_SFO_SFO_SFO <- mkinfit(m_SFO_SFO_SFO, d_SFO_SFO_SFO_err[[1]], quiet = TRUE)
+
+# Plot the results separately for parent and metabolites
+plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", "bottomright"))

 

 

 

@@ -222,29 +229,11 @@
 

   

 
-  
 
@@ -255,7 +244,7 @@
 
 
 

-  
Site built with pkgdown 1.4.1.

+  
Site built with pkgdown 1.5.1.

 

 
       
diff --git a/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/mkin_files/figure-html/unnamed-chunk-2-1.png
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diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index 4fb76fa2..ad7cf62c 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -6,19 +6,19 @@
 
 
 Benchmark timings for mkin on various systems • mkin
-
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+
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+
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+
 
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+
 
 
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@@ -87,12 +94,12 @@
       

   

     

-      
Benchmark timings for mkin on various systems

+      
Benchmark timings for mkin on various systems

                         
Johannes Ranke

             
-            
2019-11-01

-      
+            
2020-05-11

       
+      Source: vignettes/web_only/benchmarks.Rmd
       
 
     

@@ -103,192 +110,143 @@
 

 Systems

 
Each system is characterized by its CPU type, the operating system type and the mkin version.

-
cpu_model <- benchmarkme::get_cpu()$model_name
-operating_system <- Sys.info()[["sysname"]]
-mkin_version <- as.character(packageVersion("mkin"))
-system_string <- paste0(operating_system, ", ", cpu_model, ", mkin version ", mkin_version)
-load("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
-mkin_benchmarks[system_string, c("CPU", "OS", "mkin")] <- c(cpu_model, operating_system, mkin_version)
-
-if (mkin_version > "0.9.48.1") {
-  mmkin_bench <- function(models, datasets, error_model = "const") mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE)
-} else {
-  mmkin_bench <- function(models, datasets, error_model = NULL) mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE)
-}

-
# Parent only
-t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D)))[["elapsed"]]
-t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_2006_C, FOCUS_2006_D), error_model = "tc"))[["elapsed"]]

+
cpu_model <- benchmarkme::get_cpu()$model_name
+operating_system <- Sys.info()[["sysname"]]
+mkin_version <- as.character(packageVersion("mkin"))
+system_string <- paste0(operating_system, ", ", cpu_model, ", mkin version ", mkin_version)
+load("~/git/mkin/vignettes/web_only/mkin_benchmarks.rda")
+mkin_benchmarks[system_string, c("CPU", "OS", "mkin")] <- c(cpu_model, operating_system, mkin_version)
+
+if (mkin_version > "0.9.48.1") {
+  mmkin_bench <- function(models, datasets, error_model = "const") mmkin(models, datasets, error_model = error_model, cores = 1, quiet = TRUE)
+} else {
+  mmkin_bench <- function(models, datasets, error_model = NULL) mmkin(models, datasets, reweight.method = error_model, cores = 1, quiet = TRUE)
+}

+
FOCUS_C <- FOCUS_2006_C
+FOCUS_D <- subset(FOCUS_2006_D, value != 0)
+# Parent only
+t1 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_C, FOCUS_D)))[["elapsed"]]
+t2 <- system.time(mmkin_bench(c("SFO", "FOMC", "DFOP", "HS"), list(FOCUS_C, FOCUS_D), error_model = "tc"))[["elapsed"]]

 
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
+## iteration limit reached without convergence (10)
+
+## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): Optimisation did not converge:
 ## iteration limit reached without convergence (10)

-
+
# One metabolite
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))

 
## Successfully compiled differential equation model from auto-generated C code.

-
+
FOMC_SFO <- mkinmod(
+  parent = mkinsub("FOMC", "m1"),
+  m1 = mkinsub("SFO"))

 
## Successfully compiled differential equation model from auto-generated C code.

-
+
DFOP_SFO <- mkinmod(
+  parent = mkinsub("FOMC", "m1"),
+  m1 = mkinsub("SFO"))

 
## Successfully compiled differential equation model from auto-generated C code.

-
t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D)))[["elapsed"]]

-
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data

-
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+
t3 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D)))[["elapsed"]]
+t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D), error_model = "tc"))[["elapsed"]]
+t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_D), error_model = "obs"))[["elapsed"]]
+
+# Two metabolites, synthetic data
+m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
+                           M1 = mkinsub("SFO", "M2"),
+                           M2 = mkinsub("SFO"),
+                           use_of_ff = "max", quiet = TRUE)
+
+m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
+                           M1 = mkinsub("SFO"),
+                           M2 = mkinsub("SFO"),
+                           use_of_ff = "max", quiet = TRUE)
+
+SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data

-
t4 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(subset(FOCUS_2006_D, value != 0)), error_model = "tc"))[["elapsed"]]
-t5 <- system.time(mmkin_bench(list(SFO_SFO, FOMC_SFO, DFOP_SFO), list(FOCUS_2006_D), error_model = "obs"))[["elapsed"]]

-
## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data
+t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
+t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
 
-## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...):
-## Observations with value of zero were removed from the data

-
# Two metabolites, synthetic data
-m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
-                           M1 = mkinsub("SFO", "M2"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
-                           M1 = mkinsub("SFO"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-SFO_lin_a <- synthetic_data_for_UBA_2014[[1]]$data
-
-DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
-
-t6 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a)))["elapsed"]
-t7 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c)))["elapsed"]
-
-t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
-t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
-
-t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
-t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
-
-mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
-mkin_benchmarks

-
##                                                                                                       CPU
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 AMD Ryzen 7 1700 Eight-Core Processor
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 AMD Ryzen 7 1700 Eight-Core Processor
-##                                                                        OS
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 Linux
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 Linux
-##                                                                         mkin
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 0.9.48.1
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 0.9.49.1
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 0.9.49.2
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 0.9.49.3
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 0.9.49.4
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 0.9.49.6
-##                                                                        t1
+t8 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "tc"))["elapsed"]
+t9 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "tc"))["elapsed"]
+
+t10 <- system.time(mmkin_bench(list(m_synth_SFO_lin), list(SFO_lin_a), error_model = "obs"))["elapsed"]
+t11 <- system.time(mmkin_bench(list(m_synth_DFOP_par), list(DFOP_par_c), error_model = "obs"))["elapsed"]
+
+mkin_benchmarks[system_string, paste0("t", 1:11)] <- c(t1, t2, t3, t4, t5, t6, t7, t8, t9, t10, t11)
+mkin_benchmarks[, -c(1:3)]

+
##                                                                        t1
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.610
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 8.184
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 5.901
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.683
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 36.164
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    3.862
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.510
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.369
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 30.849
-##                                                                         t5
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1  9.495
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  6.395
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.675
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.386
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  6.002
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.545
-##                                                                        t6
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 2.563
-##                                                                        t7
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 4.252
-##                                                                        t8
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.02
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 7.865
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    6.104
+##                                                                        t5    t6
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 9.495 2.623
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 6.395 2.542
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 5.675 2.723
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.386 2.643
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 6.002 2.635
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.718 0.752
+##                                                                        t7    t8
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587 7.525
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128 4.632
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478 4.862
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374  7.02
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259 4.737
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   1.214 1.276
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 16.195
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.858
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 7.729
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50   2.032
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.6 10.787

-
save(mkin_benchmarks, file = "~/git/mkin/vignettes/mkin_benchmarks.rda")

+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.50    2.973

+
save(mkin_benchmarks, file = "~/git/mkin/vignettes/mkin_benchmarks.rda")

 

   

 
-  
 
@@ -299,7 +257,7 @@
 
 
 

-  
Site built with pkgdown 1.4.1.

+  
Site built with pkgdown 1.5.1.

 

 
       
diff --git a/docs/news/index.html b/docs/news/index.html
index bc69fd2d..1e13041d 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -145,7 +145,6 @@
 
    
 
  • Support SFORB with formation fractions
    • 
 
  • ‘mkinmod’: Make ‘use_of_ff’ = “max” the default
    • 
-
  • Implement analytical solutions for some coupled models, e.g. SFO-SFO, DFOP-SFO, SFORB-SFO
    • 
 

 
     

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 32bcaa05..b3cd3d30 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
   NAFTA_examples: web_only/NAFTA_examples.html
   benchmarks: web_only/benchmarks.html
   compiled_models: web_only/compiled_models.html
-last_built: 2020-05-07T20:11Z
+last_built: 2020-05-11T03:18Z
 urls:
   reference: https://pkgdown.jrwb.de/mkin/reference
   article: https://pkgdown.jrwb.de/mkin/articles
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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index 20f30221..1109c8df 100644
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index 0b3769ce..230c90e9 100644
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diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png
index ad2ffa8c..b23427b5 100644
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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 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -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.

 

 
       
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd
index 1ba5b5f8..2c9192b7 100644
--- a/man/mkinpredict.Rd
+++ b/man/mkinpredict.Rd
@@ -85,13 +85,14 @@ is 1e-10, much lower than in \code{\link{lsoda}}.}
 
 \item{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.}
 
 \item{\dots}{Further arguments passed to the ode solver in case such a
 solver is used.}
 }
 \value{
-A data frame with the solution in wide format
+A matrix with the numeric solution in wide format
 }
 \description{
 This function produces a time series for all the observed variables in a
@@ -129,7 +130,7 @@ mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
 mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
       seq(0, 20, by = 0.01))[2001,]
 
-# Check compiled model versions - they are faster than the eigenvalue based solutions!
+# Comparison of the performance of solution types
 SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
                   m1 = list(type = "SFO"), use_of_ff = "max")
 if(require(rbenchmark)) {
@@ -151,15 +152,11 @@ if(require(rbenchmark)) {
       c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
       solution_type = "analytical", 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,]
 
 \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 = "analytical")
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
   head(mkinpredict(f))
 }
 
diff --git a/test.log b/test.log
index 2a116058..7822efdb 100644
--- a/test.log
+++ b/test.log
@@ -2,9 +2,9 @@ Loading mkin
 Testing mkin
 ✔ |  OK F W S | Context
 ✔ |   2       | Export dataset for reading into CAKE
-✔ |  13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [2.5 s]
+✔ |  13       | Results for FOCUS D established in expertise for UBA (Ranke 2014) [1.2 s]
 ✔ |   4       | Calculation of FOCUS chi2 error levels [0.4 s]
-✔ |   7       | Fitting the SFORB model [9.0 s]
+✔ |   7       | Fitting the SFORB model [3.5 s]
 ✔ |   1       | Analytical solutions for coupled models [0.2 s]
 ✔ |   5       | Calculation of Akaike weights
 ✔ |  10       | Confidence intervals and p-values [1.0 s]
@@ -14,21 +14,21 @@ Testing mkin
 ✔ |   1       | Test dataset class mkinds used in gmkin
 ✔ |  12       | Special cases of mkinfit calls [0.6 s]
 ✔ |   8       | mkinmod model generation and printing [0.2 s]
-✔ |   3       | Model predictions with mkinpredict [0.4 s]
-✔ |  16       | Evaluations according to 2015 NAFTA guidance [1.3 s]
-✔ |   9       | Nonlinear mixed-effects models [12.1 s]
+✔ |   3       | Model predictions with mkinpredict [0.3 s]
+✔ |  16       | Evaluations according to 2015 NAFTA guidance [1.4 s]
+✔ |   9       | Nonlinear mixed-effects models [7.5 s]
 ✔ |   4       | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s]
 ✔ |   3       | Summary
-✔ |  14       | Plotting [3.6 s]
+✔ |  14       | Plotting [1.7 s]
 ✔ |   4       | AIC calculation
 ✔ |   4       | Residuals extracted from mkinfit models
-✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [4.2 s]
+✔ |   2       | Complex test case from Schaefer et al. (2007) Piacenza paper [1.4 s]
 ✔ |   1       | Summaries of old mkinfit objects
-✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [6.3 s]
-✔ |   9       | Hypothesis tests [17.7 s]
+✔ |   4       | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s]
+✔ |   9       | Hypothesis tests [6.5 s]
 
 ══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 66.3 s
+Duration: 35.1 s
 
 OK:       157
 Failed:   0
diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf
index d41f0a9f..c0c5c10c 100644
--- a/tests/testthat/FOCUS_2006_D.csf
+++ b/tests/testthat/FOCUS_2006_D.csf
@@ -5,7 +5,7 @@ Description:
 MeasurementUnits: % AR
 TimeUnits: days
 Comments: Created using mkin::CAKE_export
-Date: 2020-05-10
+Date: 2020-05-11
 Optimiser: IRLS
 
 [Data]
diff --git a/tests/testthat/test_mkinpredict_SFO_SFO.R b/tests/testthat/test_mkinpredict_SFO_SFO.R
index 347aa50b..d0dc7cdb 100644
--- a/tests/testthat/test_mkinpredict_SFO_SFO.R
+++ b/tests/testthat/test_mkinpredict_SFO_SFO.R
@@ -5,26 +5,28 @@ test_that("Variants of model predictions for SFO_SFO model give equivalent resul
   # Do not use time 0, as eigenvalue based solution does not give 0 at time 0 for metabolites
   # and relative tolerance is thus not met
   tol = 0.01
-  SFO_SFO.1 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "min", quiet = TRUE)
-  SFO_SFO.2 <- mkinmod(parent = list(type = "SFO", to = "m1"),
-         m1 = list(type = "SFO"), use_of_ff = "max", quiet = TRUE)
+  SFO_SFO.1 <- mkinmod(parent = mkinsub("SFO", to = "m1"),
+         m1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
+  SFO_SFO.2 <- mkinmod(parent = mkinsub("SFO", to = "m1"),
+         m1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
 
   ot = seq(0, 100, by = 1)
-  r.1.e <- subset(mkinpredict(SFO_SFO.1,
+  r.1.e <- subset(as.data.frame(mkinpredict(SFO_SFO.1,
              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
-             c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+             c(parent = 100, m1 = 0), ot, solution_type = "eigen")),
                  time %in% c(1, 10, 50, 100))
-  r.1.d <- subset(mkinpredict(SFO_SFO.1,
+  r.1.d <- subset(as.data.frame(mkinpredict(SFO_SFO.1,
              c(k_parent_m1 = 0.1, k_parent_sink = 0.1, k_m1_sink = 0.1),
-             c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+             c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),
                  time %in% c(1, 10, 50, 100))
 
-  r.2.e <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
-      c(parent = 100, m1 = 0), ot, solution_type = "eigen"),
+  r.2.e <- subset(as.data.frame(mkinpredict(SFO_SFO.2,
+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+      c(parent = 100, m1 = 0), ot, solution_type = "eigen")),
                   time %in% c(1, 10, 50, 100))
-  r.2.d <- subset(mkinpredict(SFO_SFO.2, c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
-      c(parent = 100, m1 = 0), ot, solution_type = "deSolve"),
+  r.2.d <- subset(as.data.frame(mkinpredict(SFO_SFO.2,
+      c(k_parent = 0.2, f_parent_to_m1 = 0.5, k_m1 = 0.1),
+      c(parent = 100, m1 = 0), ot, solution_type = "deSolve")),
                   time %in% c(1, 10, 50, 100))
 
   # Compare eigen and deSolve for minimum use of formation fractions
diff --git a/tests/testthat/test_plots_summary_twa.R b/tests/testthat/test_plots_summary_twa.R
index 13d1dd0f..bc3d29a8 100644
--- a/tests/testthat/test_plots_summary_twa.R
+++ b/tests/testthat/test_plots_summary_twa.R
@@ -13,7 +13,7 @@ test_that("Time weighted average concentrations are correct", {
                 odeparms = bpar[2:length(bpar)],
                 odeini = c(parent = bpar[[1]]),
                 outtimes = outtimes_10)
-    twa_num <- mean(pred_10$parent)
+    twa_num <- mean(pred_10[, "parent"])
     names(twa_num) <- 10
     twa_ana <- max_twa_parent(fit, 10)
 
diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html
index ff9d4596..38c597b0 100644
--- a/vignettes/FOCUS_D.html
+++ b/vignettes/FOCUS_D.html
@@ -11,7 +11,7 @@
 
 
 
-
+
 
 Example evaluation of FOCUS Example Dataset D
 
@@ -365,7 +365,7 @@ summary {
 
 
Example evaluation of FOCUS Example Dataset D

 
Johannes Ranke

-
2020-05-06

+
2020-05-11

 
 
 
@@ -423,101 +423,99 @@ print(FOCUS_2006_D)

 
SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))

 
## Successfully compiled differential equation model from auto-generated C code.

 
print(SFO_SFO$diffs)

-
##                                                       parent 
-## "d_parent = - k_parent_sink * parent - k_parent_m1 * parent" 
-##                                                           m1 
-##             "d_m1 = + k_parent_m1 * parent - k_m1_sink * m1"

+
##                                                    parent 
+##                          "d_parent = - k_parent * parent" 
+##                                                        m1 
+## "d_m1 = + f_parent_to_m1 * k_parent * parent - k_m1 * m1"

 
We do the fitting without progress report (quiet = TRUE).

 
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)

 
## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value
 ## of zero were removed from the data

 
A plot of the fit including a residual plot for both observed variables is obtained using the plot_sep method for mkinfit objects, which shows separate graphs for all compounds and their residuals.

 
plot_sep(fit, lpos = c("topright", "bottomright"))

-

+

 
Confidence intervals for the parameter estimates are obtained using the mkinparplot function.

 
mkinparplot(fit)

-

+

 
A comprehensive report of the results is obtained using the summary method for mkinfit objects.

 
summary(fit)

-
## mkin version used for fitting:    0.9.49.11 
+
## mkin version used for fitting:    0.9.50 
 ## R version used for fitting:       4.0.0 
-## Date of fit:     Wed May  6 19:54:54 2020 
-## Date of summary: Wed May  6 19:54:54 2020 
+## Date of fit:     Mon May 11 04:41:12 2020 
+## Date of summary: Mon May 11 04:41:12 2020 
 ## 
 ## Equations:
-## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
-## d_m1/dt = + k_parent_m1 * parent - k_m1_sink * m1
+## 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 389 model solutions performed in 1.041 s
+## Fitted using 421 model solutions performed in 0.149 s
 ## 
 ## Error model: Constant variance 
 ## 
 ## Error model algorithm: OLS 
 ## 
 ## Starting values for parameters to be optimised:
-##                  value   type
-## parent_0      100.7500  state
-## k_parent_sink   0.1000 deparm
-## k_parent_m1     0.1001 deparm
-## k_m1_sink       0.1002 deparm
+##                   value   type
+## parent_0       100.7500  state
+## k_parent         0.1000 deparm
+## k_m1             0.1001 deparm
+## f_parent_to_m1   0.5000 deparm
 ## 
 ## Starting values for the transformed parameters actually optimised:
-##                        value lower upper
-## parent_0          100.750000  -Inf   Inf
-## log_k_parent_sink  -2.302585  -Inf   Inf
-## log_k_parent_m1    -2.301586  -Inf   Inf
-## log_k_m1_sink      -2.300587  -Inf   Inf
+##                     value lower upper
+## parent_0       100.750000  -Inf   Inf
+## log_k_parent    -2.302585  -Inf   Inf
+## log_k_m1        -2.301586  -Inf   Inf
+## f_parent_ilr_1   0.000000  -Inf   Inf
 ## 
 ## Fixed parameter values:
 ##      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            99.600    1.57000 96.400 102.800
-## log_k_parent_sink   -3.038    0.07626 -3.193  -2.883
-## log_k_parent_m1     -2.980    0.04033 -3.062  -2.898
-## log_k_m1_sink       -5.248    0.13320 -5.518  -4.977
-## sigma                3.126    0.35850  2.396   3.855
+##                Estimate Std. Error    Lower    Upper
+## parent_0       99.60000    1.57000 96.40000 102.8000
+## log_k_parent   -2.31600    0.04087 -2.39900  -2.2330
+## log_k_m1       -5.24800    0.13320 -5.51800  -4.9770
+## f_parent_ilr_1  0.04096    0.06312 -0.08746   0.1694
+## sigma           3.12600    0.35850  2.39600   3.8550
 ## 
 ## Parameter correlation:
-##                     parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink
-## parent_0           1.000e+00         6.067e-01      -6.372e-02    -1.688e-01
-## log_k_parent_sink  6.067e-01         1.000e+00      -8.550e-02    -6.252e-01
-## log_k_parent_m1   -6.372e-02        -8.550e-02       1.000e+00     4.731e-01
-## log_k_m1_sink     -1.688e-01        -6.252e-01       4.731e-01     1.000e+00
-## sigma              5.287e-10         3.306e-09       4.421e-08    -3.319e-10
-##                        sigma
-## parent_0           5.287e-10
-## log_k_parent_sink  3.306e-09
-## log_k_parent_m1    4.421e-08
-## log_k_m1_sink     -3.319e-10
-## sigma              1.000e+00
+##                  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 -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.
 ## t-test (unrealistically) based on the assumption of normal distribution
 ## for estimators of untransformed parameters.
-##                Estimate t value    Pr(>t)     Lower     Upper
-## parent_0      99.600000  63.430 2.298e-36 96.400000 1.028e+02
-## k_parent_sink  0.047920  13.110 6.126e-15  0.041030 5.596e-02
-## k_parent_m1    0.050780  24.800 3.269e-23  0.046780 5.512e-02
-## k_m1_sink      0.005261   7.510 6.165e-09  0.004012 6.898e-03
-## sigma          3.126000   8.718 2.235e-10  2.396000 3.855e+00
+##                 Estimate t value    Pr(>t)     Lower     Upper
+## parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
+## k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
+## k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
+## f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
+## sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
 ## 
 ## FOCUS Chi2 error levels in percent:
 ##          err.min n.optim df
 ## All data   6.398       4 15
-## parent     6.827       3  6
-## m1         4.490       1  9
+## parent     6.459       2  7
+## m1         4.690       2  8
 ## 
 ## Resulting formation fractions:
 ##                 ff
-## parent_sink 0.4855
 ## parent_m1   0.5145
-## m1_sink     1.0000
+## parent_sink 0.4855
 ## 
 ## Estimated disappearance times:
 ##           DT50   DT90
@@ -530,10 +528,10 @@ print(FOCUS_2006_D)

 ##     0   parent   102.04  99.59848  2.442e+00
 ##     1   parent    93.50  90.23787  3.262e+00
 ##     1   parent    92.50  90.23787  2.262e+00
-##     3   parent    63.23  74.07320 -1.084e+01
-##     3   parent    68.99  74.07320 -5.083e+00
-##     7   parent    52.32  49.91207  2.408e+00
-##     7   parent    55.13  49.91207  5.218e+00
+##     3   parent    63.23  74.07319 -1.084e+01
+##     3   parent    68.99  74.07319 -5.083e+00
+##     7   parent    52.32  49.91206  2.408e+00
+##     7   parent    55.13  49.91206  5.218e+00
 ##    14   parent    27.27  25.01257  2.257e+00
 ##    14   parent    26.64  25.01257  1.627e+00
 ##    21   parent    11.50  12.53462 -1.035e+00
@@ -543,7 +541,7 @@ print(FOCUS_2006_D)

 ##    50   parent     0.69   0.71624 -2.624e-02
 ##    50   parent     0.63   0.71624 -8.624e-02
 ##    75   parent     0.05   0.06074 -1.074e-02
-##    75   parent     0.06   0.06074 -7.382e-04
+##    75   parent     0.06   0.06074 -7.381e-04
 ##     1       m1     4.84   4.80296  3.704e-02
 ##     1       m1     5.64   4.80296  8.370e-01
 ##     3       m1    12.91  13.02400 -1.140e-01
diff --git a/vignettes/mkin.html b/vignettes/mkin.html
index 8786aa05..2ba360f4 100644
--- a/vignettes/mkin.html
+++ b/vignettes/mkin.html
@@ -1,17 +1,17 @@
 
 
-
+
 
 
 
 
-
 
+
 
 
 
 
-
+
 
 Introduction to mkin
 
@@ -1288,7 +1288,7 @@ window.initializeCodeFolding = function(show) {
 
     // create a collapsable div to wrap the code in
     var div = $('
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-    if (show)
+    if (show || $(this)[0].classList.contains('fold-show'))
       div.addClass('in');
     var id = 'rcode-643E0F36' + currentIndex++;
     div.attr('id', id);
@@ -1404,7 +1404,6 @@ code {
 }
 img {
   max-width:100%;
-  height: auto;
 }
 .tabbed-pane {
   padding-top: 12px;
@@ -1466,6 +1465,7 @@ summary {
   border: none;
   display: inline-block;
   border-radius: 4px;
+  background-color: transparent;
 }
 
 .tabset-dropdown > .nav-tabs.nav-tabs-open > li {
@@ -1478,56 +1478,12 @@ summary {
 }
 
 
-
-
 
 
-
-
-
-
-