From af2e1540cdad2fd00bb6216a38a754ff748629ad Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 25 Oct 2019 02:10:08 +0200 Subject: Static documentation rebuilt by pkgdown --- docs/articles/mkin.html | 3 +- .../mkin_files/figure-html/unnamed-chunk-2-1.png | Bin 113885 -> 113818 bytes docs/news/index.html | 1 - docs/reference/AIC.mmkin.html | 34 +- docs/reference/CAKE_export.html | 48 +-- docs/reference/DFOP.solution.html | 39 +- docs/reference/Extract.mmkin.html | 33 +- docs/reference/FOMC.solution.html | 68 ++-- docs/reference/HS.solution.html | 37 +- docs/reference/IORE.solution.html | 51 ++- docs/reference/SFO.solution.html | 27 +- docs/reference/SFORB.solution.html | 50 ++- docs/reference/add_err-2.png | Bin 51024 -> 50582 bytes docs/reference/add_err.html | 65 ++- docs/reference/endpoints.html | 54 +-- docs/reference/ilr.html | 49 +-- docs/reference/index.html | 26 +- docs/reference/logLik.mkinfit.html | 51 ++- docs/reference/logistic.solution-2.png | Bin 29139 -> 29336 bytes docs/reference/logistic.solution.html | 44 +- docs/reference/max_twa_parent.html | 83 ++-- docs/reference/mkin_long_to_wide.html | 40 +- docs/reference/mkin_wide_to_long.html | 36 +- docs/reference/mkinds.html | 38 +- docs/reference/mkinerrmin.html | 57 ++- docs/reference/mkinerrplot.html | 53 ++- docs/reference/mkinfit.html | 441 ++++++++++----------- docs/reference/mkinmod.html | 135 +++---- docs/reference/mkinparplot.html | 32 +- docs/reference/mkinplot.html | 22 +- docs/reference/mkinpredict.html | 107 ++--- docs/reference/mkinresplot.html | 72 ++-- docs/reference/mkinsub.html | 45 +-- docs/reference/mmkin-4.png | Bin 62975 -> 62550 bytes docs/reference/mmkin.html | 73 ++-- docs/reference/nafta.html | 81 ++-- docs/reference/plot.mkinfit-4.png | Bin 43670 -> 59080 bytes docs/reference/plot.mkinfit-5.png | Bin 59080 -> 65273 bytes docs/reference/plot.mkinfit.html | 126 +++--- docs/reference/plot.mmkin-4.png | Bin 38129 -> 37076 bytes docs/reference/plot.mmkin.html | 76 ++-- docs/reference/plot.nafta.html | 36 +- docs/reference/print.mkinds.html | 14 +- docs/reference/print.mkinmod.html | 25 +- docs/reference/sigma_twocomp.html | 34 +- docs/reference/summary.mkinfit.html | 124 +++--- docs/reference/transform_odeparms.html | 119 +++--- docs/sitemap.xml | 3 - 48 files changed, 1275 insertions(+), 1277 deletions(-) (limited to 'docs') diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html index ec138595..9caabec8 100644 --- a/docs/articles/mkin.html +++ b/docs/articles/mkin.html @@ -90,7 +90,7 @@

Introduction to mkin

Johannes Ranke

-

2019-09-19

+

2019-10-25

@@ -223,6 +223,7 @@
- -

Function describing decline from a defined starting value using the sum - of two exponential decline functions.

- +

Function describing decline from a defined starting value using the sum of +two exponential decline functions.

DFOP.solution(t, parent.0, k1, k2, g)
- +

Arguments

@@ -161,36 +161,33 @@ - +
g

Fraction of the starting value declining according to the - first kinetic constant.

Fraction of the starting value declining according to the first +kinetic constant.
- +

Value

The value of the response variable at time t.

-

References

-

FOCUS (2006) “Guidance Document on Estimating Persistence and - Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, +

FOCUS (2006) “Guidance Document on Estimating Persistence + and Degradation Kinetics from Environmental Fate Studies on Pesticides in + EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-

Examples

- 
  plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim=c(0,100))
+ 

+  plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))

+
-

Subsetting method for mmkin objects.

- 
# S3 method for mmkin
 [(x, i, j, ..., drop = FALSE)
- +

Arguments

@@ -160,32 +160,32 @@ - +
drop

If FALSE, the method always returns an mmkin object, otherwise either - a list of mkinfit objects or a single mkinfit object.

If FALSE, the method always returns an mmkin object, otherwise +either a list of mkinfit objects or a single mkinfit object.
- +

Value

An object of class mmkin.

-

Examples

- 
  # Only use one core, to pass R CMD check --as-cran
+    

+  # Only use one core, to pass R CMD check --as-cran
   fits <- mmkin(c("SFO", "FOMC"), list(B = FOCUS_2006_B, C = FOCUS_2006_C),
                 cores = 1, quiet = TRUE)
   fits["FOMC", ]
#>       dataset
 #> model  B       C      
-#>   FOMC List,39 List,39
+#>   FOMC List,40 List,40
 #> attr(,"class")
 #> [1] "mmkin"
  fits[, "B"]
#>       dataset
 #> model  B      
-#>   SFO  List,39
-#>   FOMC List,39
+#>   SFO  List,40
+#>   FOMC List,40
 #> attr(,"class")
 #> [1] "mmkin"
  fits["SFO", "B"]
#>      dataset
 #> model B      
-#>   SFO List,39
+#>   SFO List,40
 #> attr(,"class")
 #> [1] "mmkin"

   head(
@@ -210,15 +210,14 @@
 #> 
 #> $message
 #> [1] "both X-convergence and relative convergence (5)"
-#> 

+#> 

+
- -

Function describing exponential decline from a defined starting value, with - a decreasing rate constant.

-

The form given here differs slightly from the original reference by Gustafson - and Holden (1990). The parameter beta corresponds to 1/beta in the - original equation.

- +

Function describing exponential decline from a defined starting value, with +a decreasing rate constant.

FOMC.solution(t, parent.0, alpha, beta)
- +

Arguments

@@ -159,52 +153,52 @@ The form given here differs slightly from the original reference by Gustafson - +
alpha

Shape parameter determined by coefficient of variation of rate constant - values.

Shape parameter determined by coefficient of variation of rate +constant values.
beta

Location parameter.
- -

Note

-

The solution of the FOMC kinetic model reduces to the - SFO.solution for large values of alpha and - beta with - \(k = \frac{\beta}{\alpha}\).

-

Value

The value of the response variable at time t.

- +

Details

+ +

The form given here differs slightly from the original reference by +Gustafson and Holden (1990). The parameter beta corresponds to 1/beta +in the original equation.

+

Note

+ +

The solution of the FOMC kinetic model reduces to the + SFO.solution for large values of alpha and + beta with \(k = \frac{\beta}{\alpha}\).

References

-

FOCUS (2006) “Guidance Document on Estimating Persistence and - Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, +

FOCUS (2006) “Guidance Document on Estimating Persistence + and Degradation Kinetics from Environmental Fate Studies on Pesticides in + EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-

Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: A - new model based on spatial variability. Environmental Science and +

Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: + A new model based on spatial variability. Environmental Science and Technology 24, 1032-1038

-

Examples

- 
  plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))
+ 

+  plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))

+
-

Function describing two exponential decline functions with a break point - between them.

- +between them.

HS.solution(t, parent.0, k1, k2, tb)
- +

Arguments

@@ -161,37 +161,34 @@ - +
tb

Break point. Before this time, exponential decline according - to k1 is calculated, after this time, exponential decline proceeds - according to k2.

Break point. Before this time, exponential decline according to +k1 is calculated, after this time, exponential decline proceeds +according to k2.
- +

Value

The value of the response variable at time t.

-

References

-

FOCUS (2006) “Guidance Document on Estimating Persistence and - Degradation Kinetics from Environmental Fate Studies on Pesticides in EU - Registration” Report of the FOCUS Work Group on Degradation Kinetics, +

FOCUS (2006) “Guidance Document on Estimating Persistence + and Degradation Kinetics from Environmental Fate Studies on Pesticides in + EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-

Examples

- 
  plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))
+ 

+  plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))

+
- -

Function describing exponential decline from a defined starting value, with - a concentration dependent rate constant.

- +

Function describing exponential decline from a defined starting value, with +a concentration dependent rate constant.

IORE.solution(t, parent.0, k__iore, N)
- +

Arguments

@@ -153,34 +153,32 @@ - +
k__iore

Rate constant. Note that this depends on the concentration units used.

Rate constant. Note that this depends on the concentration +units used.
N

Exponent describing the nonlinearity of the rate equation
- -

Note

-

The solution of the IORE kinetic model reduces to the - SFO.solution if N = 1. - The parameters of the IORE model can be transformed to equivalent parameters - of the FOMC mode - see the NAFTA guidance for details.

-

Value

The value of the response variable at time t.

- +

Note

+ +

The solution of the IORE kinetic model reduces to the + SFO.solution if N = 1. The parameters of the IORE model can + be transformed to equivalent parameters of the FOMC mode - see the NAFTA + guidance for details.

References

-

NAFTA Technical Working Group on Pesticides (not dated) Guidance for - Evaluating and Calculating Degradation Kinetics in Environmental - Media

- +

NAFTA Technical Working Group on Pesticides (not dated) Guidance + for Evaluating and Calculating Degradation Kinetics in Environmental Media

Examples

- 
  plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))
  # \dontrun{
+    

+  plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))
  # \dontrun{
     fit.fomc <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
     fit.iore <- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE)
     fit.iore.deS <- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE)
@@ -195,19 +193,16 @@
 #> fomc     1.785233 15.1479 4.559973
 #> iore     1.785233 15.1479 4.559973
 #> iore.deS 1.785233 15.1479 4.559973
  # }
+
 

   

   
 
     

-    
     
Function describing exponential decline from a defined starting value.

-    
     

 
     
SFO.solution(t, parent.0, k)

-    
+
     
Arguments

     
@@ -154,32 +154,29 @@
       

     
Kinetic constant.

     

     

-    
+
     
Value

 
     
The value of the response variable at time t.

-    
     
References

 
-    
FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+    
FOCUS (2006) “Guidance Document on Estimating Persistence
+  and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+  EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
 
     
Examples

-    
  plot(function(x) SFO.solution(x, 100, 3), 0, 2)

+    

+  plot(function(x) SFO.solution(x, 100, 3), 0, 2)

+

   
   
 
     

-    
     
Function describing the solution of the differential equations describing
-  the kinetic model with first-order terms for a two-way transfer from a free
-  to a bound fraction, and a first-order degradation term for the free
-  fraction.  The initial condition is a defined amount in the free fraction and
-  no substance in the bound fraction.

-    
+the kinetic model with first-order terms for a two-way transfer from a free
+to a bound fraction, and a first-order degradation term for the free
+fraction.  The initial condition is a defined amount in the free fraction
+and no substance in the bound fraction.

     

 
     
SFORB.solution(t, parent.0, k_12, k_21, k_1output)

-    
+
     
Arguments

     
@@ -167,36 +167,34 @@
     
-      
+      

     
     

       k_1output
Kinetic constant describing degradation of the free fraction.
Kinetic constant describing degradation of the free
+fraction.

     

     

-    
+
     
Value

 
-    
The value of the response variable, which is the sum of free and bound 
-  fractions at time t.

-    
+    
The value of the response variable, which is the sum of free and
+  bound fractions at time t.

     
References

 
-    
FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+    
FOCUS (2006) “Guidance Document on Estimating Persistence
+  and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+  EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
 
     
Examples

-    
  plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)

+    

+  plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)

+

   
   
 
     

-    
     
Normally distributed errors are added to data predicted for a specific
-  degradation model using mkinpredict. The variance of the error
-  may depend on the predicted value and is specified as a standard deviation.

-    
+degradation model using mkinpredict. The variance of the error
+may depend on the predicted value and is specified as a standard deviation.

     

 
-    
add_err(prediction, sdfunc, secondary = c("M1", "M2"),
-          n = 1000, LOD = 0.1, reps = 2,
-          digits = 1, seed = NA)

-    
+    
add_err(prediction, sdfunc, secondary = c("M1", "M2"), n = 1000,
+  LOD = 0.1, reps = 2, digits = 1, seed = NA)

+
     
Arguments

     
-      
+      
-      
+      
-      
+      
@@ -167,8 +168,8 @@
     
-      
+      
@@ -180,25 +181,25 @@
     
-      
+      

     
     

       prediction
A prediction from a kinetic model as produced by mkinpredict.
A prediction from a kinetic model as produced by
+mkinpredict.

     

     

       sdfunc
A function taking the predicted value as its only argument and returning
-    a standard deviation that should be used for generating the random error
-    terms for this value.
A function taking the predicted value as its only argument and
+returning a standard deviation that should be used for generating the
+random error terms for this value.

     

     

       secondary
The names of state variables that should have an initial value of zero
The names of state variables that should have an initial
+value of zero

     

     

       n

     

       LOD
The limit of detection (LOD). Values that are below the LOD after adding
-    the random error will be set to NA.
The limit of detection (LOD). Values that are below the LOD after
+adding the random error will be set to NA.

     

     

       reps

     

       seed
The seed used for the generation of random numbers. If NA, the seed
-    is not set.
The seed used for the generation of random numbers. If NA, the
+seed is not set.

     

     

-    
+
     
Value

 
-    
A list of datasets compatible with mmkin, i.e.
-  the components of the list are datasets compatible with
-  mkinfit.

-    
+    
A list of datasets compatible with mmkin, i.e. the
+  components of the list are datasets compatible with mkinfit.

     
References

 
-    
Ranke J and Lehmann R (2015) To t-test or not to t-test, that is the question. XV Symposium on Pesticide Chemistry 2-4 September 2015, Piacenza, Italy
-  http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

-    
+    
Ranke J and Lehmann R (2015) To t-test or not to t-test, that is
+the question. XV Symposium on Pesticide Chemistry 2-4 September 2015,
+Piacenza, Italy
+http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

 
     
Examples

-    
# The kinetic model
+    

+# The kinetic model
 m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
                      M1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

 # Generate a prediction for a specific set of parameters
@@ -235,17 +236,15 @@
 # If we use single brackets, we should give two indices (model and dataset),
 # and plot.mmkin is used
 plot(f_SFO_SFO[1, 3])
# }
+
 

   

   

+
     
Arguments

     
-      
+      

     
     

       x
A numeric vector. Naturally, the forward transformation is only sensible for
-    vectors with all elements being greater than zero.
A numeric vector. Naturally, the forward transformation is only
+sensible for vectors with all elements being greater than zero.

     

     

-    
+
     
Value

 
-    
The result of the forward or backward transformation. The returned components always
-  sum to 1 for the case of the inverse log-ratio transformation.

-    
+    
The result of the forward or backward transformation. The returned
+  components always sum to 1 for the case of the inverse log-ratio
+  transformation.

     
References

 
-    
Peter Filzmoser, Karel Hron (2008) Outlier Detection for Compositional Data Using Robust Methods. Math Geosci 40 233-248

-    
+    
Peter Filzmoser, Karel Hron (2008) Outlier Detection for
+  Compositional Data Using Robust Methods. Math Geosci 40 233-248

     
See also

 
-    
Another implementation can be found in R package robCompositions.

-    
+    
Another implementation can be found in R package
+  robCompositions.

 
     
Examples

-    
# Order matters
+    

+# Order matters
 ilr(c(0.1, 1, 10))
#> [1] -1.628174 -2.820079
ilr(c(10, 1, 0.1))
#> [1] 1.628174 2.820079
# Equal entries give ilr transformations with zeros as elements
 ilr(c(3, 3, 3))
#> [1] 0 0
# Almost equal entries give small numbers
 ilr(c(0.3, 0.4, 0.3))
#> [1] -0.2034219  0.1174457
# Only the ratio between the numbers counts, not their sum
@@ -173,19 +177,16 @@
 sum(invilr(c(-10, 0)))
#> [1] 1
# This is why we do not need all elements of the inverse transformation to go back:
 a <- c(0.1, 0.3, 0.5)
 b <- invilr(a)
-length(b) # Four elements
#> [1] 4
ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)
#> [1] 0.1 0.3 0.5

+length(b) # Four elements
#> [1] 4
ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)
#> [1] 0.1 0.3 0.5

+

   
   
 
     

-    
     
This function simply calculates the product of the likelihood densities
-  calculated using dnorm, i.e. assuming normal distribution,
-  with of the mean predicted by the degradation model, and the
-  standard deviation predicted by the error model.

-
The total number of estimated parameters returned with the value
-  of the likelihood is calculated as the sum of fitted degradation
-  model parameters and the fitted error model parameters.

-    
+calculated using dnorm, i.e. assuming normal distribution,
+with of the mean predicted by the degradation model, and the standard
+deviation predicted by the error model.

     

 
     
# S3 method for mkinfit
 logLik(object, ...)

-    
+
     
Arguments

     
@@ -163,21 +157,25 @@ The total number of estimated parameters returned with the value
       

     
For compatibility with the generic method

     

     

-    
+
     
Value

 
-    
An object of class logLik with the number of
-  estimated parameters (degradation model parameters plus variance
-  model parameters) as attribute.

-    
+    
An object of class logLik with the number of estimated
+  parameters (degradation model parameters plus variance model parameters)
+  as attribute.

+    
Details

+
+    
The total number of estimated parameters returned with the value of the
+likelihood is calculated as the sum of fitted degradation model parameters
+and the fitted error model parameters.

     
See also

 
     
Compare the AIC of columns of mmkin objects using
   AIC.mmkin.

-    
 
     
Examples

-    
  # \dontrun{
+    

+  # \dontrun{
   sfo_sfo <- mkinmod(
     parent = mkinsub("SFO", to = "m1"),
     m1 = mkinsub("SFO")
@@ -186,17 +184,16 @@ The total number of estimated parameters returned with the value
 #> f_nw   5 204.4486
 #> f_obs  6 205.8727
 #> f_tc   6 141.9656
  # }
+
 

   

   
 
     

-    
     
Function describing exponential decline from a defined starting value, with
-  an increasing rate constant, supposedly caused by microbial growth

-    
+an increasing rate constant, supposedly caused by microbial growth

     

 
     
logistic.solution(t, parent.0, kmax, k0, r)

-    
+
     
Arguments

     
@@ -164,28 +164,25 @@
       

     
Growth rate of the increase in the rate constant.

     

     

-    
-    
Note

 
-    
The solution of the logistic model reduces to the
-  SFO.solution if k0 is equal to
-  kmax.

-    
     
Value

 
     
The value of the response variable at time t.

-    
+    
Note

+
+    
The solution of the logistic model reduces to the
+  SFO.solution if k0 is equal to kmax.

     
References

 
-    
FOCUS (2014) “Generic guidance for Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+    
FOCUS (2014) “Generic guidance for Estimating Persistence
+  and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+  EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
   Version 1.1, 18 December 2014
   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
 
     
Examples

-    
  # Reproduce the plot on page 57 of FOCUS (2014)
+    

+  # Reproduce the plot on page 57 of FOCUS (2014)
   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),
        from = 0, to = 100, ylim = c(0, 100),
        xlab = "Time", ylab = "Residue")
  plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),
@@ -221,19 +218,16 @@
 #> k0         0.4448750
 #> r          1.1821121
 #> sigma      7.3256566
  endpoints(m)$distimes
#>            DT50     DT90  DT50_k0 DT50_kmax
-#> parent 36.86533 62.41511 4297.854  10.83349

+#> parent 36.86533 62.41511 4297.854  10.83349

+

   
   

+
     
Arguments

     
@@ -159,14 +166,15 @@ guidance.

-      
+      
+average over the decline curve should be calculated. The default is to use
+a value of 1, which means that a relative maximum time weighted average
+factor (f_twa) is calculated.

@@ -201,36 +209,33 @@ guidance.

     
     
     

       windows
The width of the time windows for which the TWAs should be calculated.
The width of the time windows for which the TWAs should be
+calculated.

     

     

       M0
       
The initial concentration for which the maximum time weighted
-   average over the decline curve should be calculated. The default
-   is to use a value of 1, which means that a relative maximum time
-   weighted average factor (f_twa) is calculated.

     

     

       k
       
Parameter of the HS model.

     

     

-    
+
     
Value

 
-    
For max_twa_parent, a numeric vector, named using the windows argument.
-  For the other functions, a numeric vector of length one (also known as 'a
-  number').

-    
+    
For max_twa_parent, a numeric vector, named using the
+  windows argument.  For the other functions, a numeric vector of
+  length one (also known as 'a number').

     
References

 
-    
FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+    
FOCUS (2006) “Guidance Document on Estimating Persistence
+  and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+  EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
 
     
Examples

-    
  fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+    

+  fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
   max_twa_parent(fit, c(7, 21))
#>        7       21 
-#> 34.71343 18.22124 

+#> 34.71343 18.22124 

+

   
   
 
     

-    
-    
This function takes a dataframe in the long form, i.e. with a row
-  for each observed value,   and converts it into a dataframe with one
-  independent variable and several dependent variables as columns.

-    
+    
This function takes a dataframe in the long form, i.e. with a row for each
+observed value, and converts it into a dataframe with one independent
+variable and several dependent variables as columns.

     

 
     
mkin_long_to_wide(long_data, time = "time", outtime = "time")

-    
+
     
Arguments

     
-      
+      
@@ -160,14 +161,14 @@
       

     
     

       long_data
The dataframe must contain one variable called "time" with the time values specified by the
-  time argument, one column called "name" with the grouping of the observed values, and
-  finally one column of observed values called "value".
The dataframe must contain one variable called "time" with
+the time values specified by the time argument, one column called
+"name" with the grouping of the observed values, and finally one column of
+observed values called "value".

     

     

       time
The name of the time variable in the wide output data.

     

     

-    
+
     
Value

 
     
Dataframe in wide format.

-    
 
     
Examples

-    
mkin_long_to_wide(FOCUS_2006_D)
#>    time parent    m1
+    

+mkin_long_to_wide(FOCUS_2006_D)
#>    time parent    m1
 #> 1     0  99.46  0.00
 #> 2     0 102.04  0.00
 #> 3     1  93.50  4.84
@@ -189,15 +190,14 @@
 #> 19  100     NA 31.04
 #> 20  100     NA 33.13
 #> 21  120     NA 25.15
-#> 22  120     NA 33.31

+#> 22  120     NA 33.31

+

   
   
 
     

-    
-    
This function simply takes a dataframe with one independent variable and several
-  dependent variable and converts it into the long form as required by mkinfit.

-    
+    
This function simply takes a dataframe with one independent variable and
+several dependent variable and converts it into the long form as required by
+mkinfit.

     

 
     
mkin_wide_to_long(wide_data, time = "t")

-    
+
     
Arguments

     
-      
+      

     
     

       wide_data
The dataframe must contain one variable with the time values specified by the
-  time argument and usually more than one column of observed values.
The dataframe must contain one variable with the time
+values specified by the time argument and usually more than one
+column of observed values.

     

     

       time
       
The name of the time variable.

     

     

-    
+
     
Value

 
     
Dataframe in long format as needed for mkinfit.

-    
 
     
Examples

-    
wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5))
+    

+wide <- data.frame(t = c(1,2,3), x = c(1,4,7), y = c(3,4,5))
 mkin_wide_to_long(wide)
#>   name time value
 #> 1    x    1     1
 #> 2    x    2     4
 #> 3    x    3     7
 #> 4    y    1     3
 #> 5    y    2     4
-#> 6    y    3     5

+#> 6    y    3     5

+

   
   
 
     

-    
     
A dataset class for mkin

-    
     

 
     
mkinds

-        
+
+
     
Format

 
     
An R6Class generator object.

-    
     
Fields

 
     
 
-

-
title
A full title for the dataset

 
-
sampling
times The sampling times

+

+
list("title")
A full title for the dataset

 
-
time_unit
The time unit

+
list("sampling")
times The sampling times

 
-
observed
Names of the observed compounds

+
list("time_unit")
The time unit

 
-
unit
The unit of the observations

+
list("observed")
Names of the observed compounds

 
-
replicates
The number of replicates

+
list("unit")
The unit of the observations

 
-
data
A dataframe with at least the columns name, time and value
-in order to be compatible with mkinfit

+
list("replicates")
The number of replicates

 
+
list("data")
A dataframe with at least the columns name, time and
+value in order to be compatible with mkinfit
 
 

-    
+
 
     
Examples

-    
mds <- mkinds$new("FOCUS A", FOCUS_2006_A)

+    

+mds <- mkinds$new("FOCUS A", FOCUS_2006_A)

   
   
 
     

-    
-    
This function finds the smallest relative error still resulting in passing the
-chi-squared test as defined in the FOCUS kinetics report from 2006.

-    
+    
This function finds the smallest relative error still resulting in passing
+the chi-squared test as defined in the FOCUS kinetics report from 2006.

     

 
     
mkinerrmin(fit, alpha = 0.05)

-    
+
     
Arguments

     
@@ -152,35 +152,33 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.

     
       
The confidence level chosen for the chi-squared test.

     

     

-    
+
     
Value

 
     
A dataframe with the following components:

-
err.min
The relative error, expressed as a fraction.

-  
n.optim
The number of optimised parameters attributed to the data series.

-  
df
The number of remaining degrees of freedom for the chi2 error level
-    calculations.  Note that mean values are used for the chi2 statistic and
-    therefore every time point with observed values in the series only counts
-    one time.

-  The dataframe has one row for the total dataset and one further row for
-  each observed state variable in the model.
+
err.min
The
+relative error, expressed as a fraction.
 
n.optim
The number of
+optimised parameters attributed to the data series.
 
df
The number of
+remaining degrees of freedom for the chi2 error level calculations.  Note
+that mean values are used for the chi2 statistic and therefore every time
+point with observed values in the series only counts one time.
 The
+dataframe has one row for the total dataset and one further row for each
+observed state variable in the model.
 
-    
     
Details

 
     
This function is used internally by summary.mkinfit.

-    
     
References

 
-    
FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
-  EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
-  http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
+    
FOCUS (2006) “Guidance Document on Estimating Persistence
+and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
+Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC
+Document Reference Sanco/10058/2005 version 2.0, 434 pp,
+http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

 
     
Examples

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

+SFO_SFO = mkinmod(parent = mkinsub("SFO", to = "m1"),
                   m1 = mkinsub("SFO"),
                   use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

 fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
round(mkinerrmin(fit_FOCUS_D), 4)
#>          err.min n.optim df
@@ -192,19 +190,16 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.

 #> All data  0.1544       4 13
 #> parent    0.1659       2  7
 #> m1        0.1095       2  6
# }
+
 

   

   
   
 
     

-    
     
This function produces a time series for all the observed variables in a
-  kinetic model as specified by mkinmod, using a specific set of
-  kinetic parameters and initial values for the state variables.

-    
+kinetic model as specified by mkinmod, using a specific set of
+kinetic parameters and initial values for the state variables.

     

 
     
mkinpredict(x, odeparms, odeini, outtimes = seq(0, 120, by = 0.1),
-    solution_type = "deSolve", use_compiled = "auto", method.ode = "lsoda",
-    atol = 1e-08, rtol = 1e-10, map_output = TRUE, ...)

-    
+  solution_type = "deSolve", use_compiled = "auto",
+  method.ode = "lsoda", atol = 1e-08, rtol = 1e-10,
+  map_output = TRUE, ...)
+
+# S3 method for mkinmod
+mkinpredict(x, odeparms = c(k_parent_sink = 0.1),
+  odeini = c(parent = 100), outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve", use_compiled = "auto",
+  method.ode = "lsoda", atol = 1e-08, rtol = 1e-10,
+  map_output = TRUE, ...)
+
+# S3 method for mkinfit
+mkinpredict(x, odeparms = x$bparms.ode,
+  odeini = x$bparms.state, outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve", use_compiled = "auto",
+  method.ode = "lsoda", atol = 1e-08, rtol = 1e-10,
+  map_output = TRUE, ...)

+
     
Arguments

     
+fit as fitted by mkinfit. In the latter case, the fitted
+parameters are used for the prediction.

-      
+      
-      
+      
-      
+      
-      
+      
-      
-      
+      
+      
-      
-      
+      
+      
-      
+      
-      
+      
-      
+      
-      
+      

     
     

       x
       
A kinetic model as produced by mkinmod, or a kinetic
-    fit as fitted by mkinfit. In the latter case, the fitted
-    parameters are used for the prediction.

     

     

       odeparms
A numeric vector specifying the parameters used in the kinetic model, which
-    is generally defined as a set of ordinary differential equations.
A numeric vector specifying the parameters used in the
+kinetic model, which is generally defined as a set of ordinary
+differential equations.

     

     

       odeini
A numeric vectory containing the initial values of the state variables of
-    the model. Note that the state variables can differ from the observed
-    variables, for example in the case of the SFORB model.
A numeric vectory containing the initial values of the state
+variables of the model. Note that the state variables can differ from the
+observed variables, for example in the case of the SFORB model.

     

     

       outtimes
A numeric vector specifying the time points for which model predictions
-    should be generated.
A numeric vector specifying the time points for which model
+predictions should be generated.

     

     

       solution_type
The method that should be used for producing the predictions. This should
-    generally be "analytical" if there is only one observed variable, and
-    usually "deSolve" in the case of several observed variables. The third
-    possibility "eigen" is faster but not applicable to some models e.g.
-    using FOMC for the parent compound.
The method that should be used for producing the
+predictions. This should generally be "analytical" if there is only one
+observed variable, and usually "deSolve" in the case of several observed
+variables. The third possibility "eigen" is faster but not applicable to
+some models e.g.  using FOMC for the parent compound.

     

     
method.ode
The solution method passed via mkinpredict to
-    ode in case the solution type is "deSolve". The default
-    "lsoda" is performant, but sometimes fails to converge.
use_compiled
If set to FALSE, no compiled version of the
+mkinmod model is used, even if is present.

     

     
use_compiled
If set to FALSE, no compiled version of the mkinmod
-    model is used, even if is present.
method.ode
The solution method passed via mkinpredict
+to ode in case the solution type is "deSolve". The default
+"lsoda" is performant, but sometimes fails to converge.

     

     

       atol
Absolute error tolerance, passed to ode. Default is 1e-8,
-    lower than in lsoda.
Absolute error tolerance, passed to ode. Default
+is 1e-8, lower than in lsoda.

     

     

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

     

     

       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).
Boolean to specify if the output should list values for
+the observed variables (default) or for all state variables (if set to
+FALSE).

     

     

       ...
Further arguments passed to the ode solver in case such a solver is used.
Further arguments passed to the ode solver in case such a
+solver is used.

     

     

-    
+
     
Value

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

-    
 
     
Examples

-    
  SFO <- mkinmod(degradinol = mkinsub("SFO"))
+    

+  SFO <- mkinmod(degradinol = mkinsub("SFO"))
   # Compare solution types
   mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
         solution_type = "analytical")
#>    time  degradinol
@@ -342,7 +360,7 @@
                 c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
                 solution_type = "deSolve", use_compiled = FALSE)[201,]))
#>     time   parent       m1
 #> 201   20 4.978707 27.46227
#>        User      System verstrichen 
-#>       0.022       0.000       0.021 

+#>       0.021       0.000       0.021 

   # \dontrun{
     # Predict from a fitted model
     f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 552.5739
@@ -380,15 +398,14 @@
 #> 4  0.3 75.25515 3.399419
 #> 5  0.4 72.98675 4.441969
 #> 6  0.5 70.78673 5.441679
  # }
+
 

   

   
 
     

-    
-    
This function plots the residuals for the specified subset of the 
-  observed variables from an mkinfit object. A combined plot of the fitted
-  model and the residuals can be obtained using plot.mkinfit
-  using the argument show_residuals = TRUE.

-    
+    
This function plots the residuals for the specified subset of the observed
+variables from an mkinfit object. A combined plot of the fitted model and
+the residuals can be obtained using plot.mkinfit using the
+argument show_residuals = TRUE.

     

 
-    
mkinresplot(object,
-    obs_vars = names(object$mkinmod$map),
-    xlim = c(0, 1.1 * max(object$data$time)),
-    xlab = "Time", ylab = "Residual",
-    maxabs = "auto", legend = TRUE, lpos = "topright",
-    col_obs = "auto", pch_obs = "auto",
-    frame = TRUE,
-    ...)

-    
+    
mkinresplot(object, obs_vars = names(object$mkinmod$map), xlim = c(0,
+  1.1 * max(object$data$time)), xlab = "Time", ylab = "Residual",
+  maxabs = "auto", legend = TRUE, lpos = "topright",
+  col_obs = "auto", pch_obs = "auto", frame = TRUE, ...)

+
     
Arguments

     
@@ -160,8 +156,9 @@
     
-      
+      
@@ -173,12 +170,13 @@
     
-      
+      
-      
+      
@@ -186,8 +184,8 @@
     
-      
+      
@@ -206,29 +204,27 @@
       

     
     

       obs_vars
A character vector of names of the observed variables for which residuals
-    should be plotted. Defaults to all observed variables in the model
A character vector of names of the observed variables for
+which residuals should be plotted. Defaults to all observed variables in
+the model

     

     

       xlim

     

       ylab
Label for the y axis. Defaults to "Residual [% of applied radioactivity]".
Label for the y axis. Defaults to "Residual [% of applied
+radioactivity]".

     

     

       maxabs
Maximum absolute value of the residuals. This is used for the scaling of
-    the y axis and defaults to "auto".
Maximum absolute value of the residuals. This is used for the
+scaling of the y axis and defaults to "auto".

     

     

       legend

     

       lpos
Where should the legend be placed? Default is "topright". Will be passed on to
-    legend.
Where should the legend be placed? Default is "topright". Will
+be passed on to legend.

     

     

       col_obs
further arguments passed to plot.

     

     

-    
+
     
Value

 
-    
Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.

-    
+    
Nothing is returned by this function, as it is called for its side
+  effect, namely to produce a plot.

     
See also

 
-    
mkinplot, for a way to plot the data and the fitted lines of the
- mkinfit object.

-    
+    
mkinplot, for a way to plot the data and the fitted
+  lines of the mkinfit object.

 
     
Examples

-    
model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
mkinresplot(fit, "m1")

+    

+model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
mkinresplot(fit, "m1")

+

   
   
 
     

-    
     
This is a convenience function to set up the lists used as arguments for
-  mkinmod.

-    
+mkinmod.

     

 
     
mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)

-    
+
     
Arguments

     
-      
+      
-      
+      
-      
+      
-      
+      

     
     

       submodel
Character vector of length one to specify the submodel type. See
-    mkinmod for the list of allowed submodel names.
Character vector of length one to specify the submodel type.
+See mkinmod for the list of allowed submodel names.

     

     

       to
Vector of the names of the state variable to which a transformation
-    shall be included in the model.
Vector of the names of the state variable to which a
+transformation shall be included in the model.

     

     

       sink
Should a pathway to sink be included in the model in addition to the
-    pathways to other state variables?
Should a pathway to sink be included in the model in addition to
+the pathways to other state variables?

     

     

       full_name
An optional name to be used e.g. for plotting fits performed with the model.
-    You can use non-ASCII characters here, but then your R code will not be
-    portable, i.e. may produce unintended plot results on other
-    operating systems or system configurations.
An optional name to be used e.g. for plotting fits
+performed with the model.  You can use non-ASCII characters here, but then
+your R code will not be portable, i.e. may produce unintended plot
+results on other operating systems or system configurations.

     

     

-    
+
     
Value

 
     
A list for use with mkinmod.

-    
 
     
Examples

-    
# One parent compound, one metabolite, both single first order.
+    

+# One parent compound, one metabolite, both single first order.
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1"),
   m1 = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.

@@ -184,15 +184,14 @@
 # Now supplying full names
 SFO_SFO.2 <- mkinmod(
   parent = mkinsub("SFO", "m1", full_name = "Test compound"),
-  m1 = mkinsub("SFO", full_name = "Metabolite M1"))
#> Successfully compiled differential equation model from auto-generated C code.

+  m1 = mkinsub("SFO", full_name = "Metabolite M1"))
#> Successfully compiled differential equation model from auto-generated C code.

+

   
   
   
 
     

-    
Function describing the standard deviation of the measurement error 
-  in dependence of the measured value \(y\):

-
$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$
-    sigma = sqrt(sigma_low^2 + y^2 * rsd_high^2)

-
This is the error model used for example by Werner et al. (1978). The model
-  proposed by Rocke and Lorenzato (1995) can be written in this form as well,
-  but assumes approximate lognormal distribution of errors for high values of y.

+    
Function describing the standard deviation of the measurement error in
+dependence of the measured value \(y\):

     

 
     
sigma_twocomp(y, sigma_low, rsd_high)

@@ -159,18 +149,27 @@ This is the error model used for example by Werner et al. (1978). The model
     
     
       sigma_low
-      
The asymptotic minimum of the standard deviation for low observed values

+      
The asymptotic minimum of the standard deviation for low
+observed values

     
     
       rsd_high
-      
The coefficient describing the increase of the standard deviation with 
-    the magnitude of the observed value

+      
The coefficient describing the increase of the standard
+deviation with the magnitude of the observed value

     
     
 
     
Value

 
     
The standard deviation of the response variable.

+    
Details

+
+    
$$\sigma = \sqrt{ \sigma_{low}^2 + y^2 * {rsd}_{high}^2}$$ sigma =
+sqrt(sigma_low^2 + y^2 * rsd_high^2)

+
This is the error model used for example by Werner et al. (1978). The model
+proposed by Rocke and Lorenzato (1995) can be written in this form as well,
+but assumes approximate lognormal distribution of errors for high values of
+y.

     
References

 
     
Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
@@ -185,6 +184,7 @@ This is the error model used for example by Werner et al. (1978). The model
     

 
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index 73410181..65bc6326 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -8,11 +8,13 @@
 
 Summary method for class "mkinfit" — summary.mkinfit • mkin
 
+
 
 
 
 
 
+
 
 
 
@@ -32,16 +34,18 @@
 
 
 
-
 
-
+
+
 
 
 
 
+
 
 
 
@@ -112,7 +116,6 @@
   News
 
       
-      
       
    
         
       

@@ -134,19 +137,21 @@
     
 
     

-    
-    
Lists model equations, initial parameter values, optimised parameters with some
-  uncertainty statistics, the chi2 error levels calculated according to FOCUS
-  guidance (2006) as defined therein, formation fractions, DT50 values and
-  optionally the data, consisting of observed, predicted and residual values.

-    
+    
Lists model equations, initial parameter values, optimised parameters with
+some uncertainty statistics, the chi2 error levels calculated according to
+FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
+and optionally the data, consisting of observed, predicted and residual
+values.

     

 
     
# S3 method for mkinfit
-summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)
+summary(object, data = TRUE, distimes = TRUE,
+  alpha = 0.05, ...)
+
 # S3 method for summary.mkinfit
-print(x, digits = max(3, getOption("digits") - 3), ...)

-    
+print(x, digits = max(3, getOption("digits") -
+  3), ...)

+
     
Arguments

     
@@ -154,92 +159,95 @@
       
-    
-      
-      
-    
-      
+      
-      
+      
-      
-    
-    
-      
-      
+      
+    
+      
+      
+    
+    
+      
+      
+    

     
object
       
an object of class mkinfit.

     
x
an object of class summary.mkinfit.

     

       data
logical, indicating whether the data should be included in the summary.
logical, indicating whether the data should be included in the
+summary.

     

     

       distimes
logical, indicating whether DT50 and DT90 values should be included.
logical, indicating whether DT50 and DT90 values should be
+included.

     

     

       alpha
error level for confidence interval estimation from t distribution
digits
Number of digits to use for printing
error level for confidence interval estimation from t
+distribution

     

     

       ...
       
optional arguments passed to methods like print.

     
x
an object of class summary.mkinfit.
digits
Number of digits to use for printing

     

-    
+
     
Value

 
     
The summary function returns a list with components, among others

 
version, Rversion
The mkin and R versions used

-  
date.fit, date.summary
The dates where the fit and the summary were produced

+  
date.fit, date.summary
The dates where the fit and the summary were
+    produced

+  
diffs
The differential equations used in the model

   
use_of_ff
Was maximum or minimum use made of formation fractions

-  
residuals, residualVariance, sigma, modVariance, df
As in summary.modFit

-  
cov.unscaled, cov.scaled, info, niter, stopmess, par
As in summary.modFit

-  
bpar
Optimised and backtransformed parameters

-  
diffs 
The differential equations used in the model

-  
data 
The data (see Description above).

-  
start 
The starting values and bounds, if applicable, for optimised parameters.

-  
fixed 
The values of fixed parameters.

-  
errmin 
The chi2 error levels for each observed variable.

-  
bparms.ode 
All backtransformed ODE parameters, for use as starting parameters for
-    related models.

-  
errparms 
Error model parameters.

-  
ff 
The estimated formation fractions derived from the fitted model.

-  
distimes 
The DT50 and DT90 values for each observed variable.

+  
bpar
Optimised and backtransformed
+    parameters

+  
data
The data (see Description above).

+  
start
The starting values and bounds, if applicable, for optimised
+    parameters.

+  
fixed
The values of fixed parameters.

+  
errmin 
The chi2 error levels for
+    each observed variable.

+  
bparms.ode
All backtransformed ODE
+    parameters, for use as starting parameters for related models.

+  
errparms
Error model parameters.

+  
ff
The estimated formation fractions derived from the fitted
+     model.

+  
distimes
The DT50 and DT90 values for each observed variable.

   
SFORB
If applicable, eigenvalues of SFORB components of the model.

   The print method is called for its side effect, i.e. printing the summary.
 
-    
     
References

 
-    
FOCUS (2006) “Guidance Document on Estimating Persistence and
-  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
-  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+    
FOCUS (2006) “Guidance Document on Estimating Persistence
+  and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+  EU Registration” Report of the FOCUS Work Group on Degradation Kinetics,
   EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
   http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

-    
 
     
Examples

-    
  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.49.6 
+    

+  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.49.6 
 #> R version used for fitting:       3.6.1 
-#> Date of fit:     Thu Sep 19 09:52:40 2019 
-#> Date of summary: Thu Sep 19 09:52:40 2019 
+#> Date of fit:     Fri Oct 25 02:09:45 2019 
+#> Date of summary: Fri Oct 25 02:09:45 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent_sink * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 131 model solutions performed in 0.27 s
+#> Fitted using 131 model solutions performed in 0.274 s
 #> 
 #> Error model: Constant variance 
 #> 
 #> Error model algorithm: OLS 
 #> 
 #> Starting values for parameters to be optimised:
-#>                    value   type
-#> parent_0      101.240000  state
-#> k_parent_sink   0.100000 deparm
-#> sigma           5.265546  error
+#>                value   type
+#> parent_0      101.24  state
+#> k_parent_sink   0.10 deparm
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>                        value lower upper
 #> parent_0          101.240000  -Inf   Inf
 #> log_k_parent_sink  -2.302585  -Inf   Inf
-#> sigma               5.265546     0   Inf
 #> 
 #> Fixed parameter values:
 #> None
@@ -287,17 +295,15 @@
 #>    30   parent    29.71    35.738  -6.0283
 #>    62   parent     5.98    10.862  -4.8818
 #>    90   parent     1.54     3.831  -2.2911
-#>   118   parent     0.39     1.351  -0.9613

+#>   118   parent     0.39     1.351  -0.9613

+

   
   
 
     

-    
-    
The transformations are intended to map parameters that should only take
-  on restricted values to the full scale of real numbers. For kinetic rate
-  constants and other paramters that can only take on positive values, a
-  simple log transformation is used. For compositional parameters, such as
-  the formations fractions that should always sum up to 1 and can not be
-  negative, the ilr transformation is used.

-
The transformation of sets of formation fractions is fragile, as it supposes
-  the same ordering of the components in forward and backward transformation.
-  This is no problem for the internal use in mkinfit.

-    
+    
The transformations are intended to map parameters that should only take on
+restricted values to the full scale of real numbers. For kinetic rate
+constants and other paramters that can only take on positive values, a
+simple log transformation is used. For compositional parameters, such as the
+formations fractions that should always sum up to 1 and can not be negative,
+the ilr transformation is used.

     

 
-    
transform_odeparms(parms, mkinmod,
-                   transform_rates = TRUE, transform_fractions = TRUE)
-backtransform_odeparms(transparms, mkinmod,
-                       transform_rates = TRUE, transform_fractions = TRUE)

-    
+    
transform_odeparms(parms, mkinmod, transform_rates = TRUE,
+  transform_fractions = TRUE)
+
+backtransform_odeparms(transparms, mkinmod, transform_rates = TRUE,
+  transform_fractions = TRUE)

+
     
Arguments

     
-      
-    
-    
-      
-      
+      
-      
+      
-      
+      
-      
+      
+    
+    
+      
+      

     
     

       parms
Parameters of kinetic models as used in the differential equations.
transparms
Transformed parameters of kinetic models as used in the fitting procedure.
Parameters of kinetic models as used in the differential
+equations.

     

     

       mkinmod
The kinetic model of class mkinmod, containing the names of
-    the model variables that are needed for grouping the formation fractions
-    before ilr transformation, the parameter names and
-    the information if the pathway to sink is included in the model.
The kinetic model of class mkinmod, containing
+the names of the model variables that are needed for grouping the
+formation fractions before ilr transformation, the parameter
+names and the information if the pathway to sink is included in the model.

     

     

       transform_rates
Boolean specifying if kinetic rate constants should be transformed in the
-    model specification used in the fitting for better compliance with the
-    assumption of normal distribution of the estimator. If TRUE, also
-    alpha and beta parameters of the FOMC model are log-transformed, as well
-    as k1 and k2 rate constants for the DFOP and HS models and the break point tb
-    of the HS model.
Boolean specifying if kinetic rate constants should
+be transformed in the model specification used in the fitting for better
+compliance with the assumption of normal distribution of the estimator. If
+TRUE, also alpha and beta parameters of the FOMC model are
+log-transformed, as well as k1 and k2 rate constants for the DFOP and HS
+models and the break point tb of the HS model.

     

     

       transform_fractions
Boolean specifying if formation fractions constants should be transformed in the
-    model specification used in the fitting for better compliance with the
-    assumption of normal distribution of the estimator. The default (TRUE) is
-    to do transformations. The g parameter of the DFOP and HS models are also
-    transformed, as they can also be seen as compositional data. The
-    transformation used for these transformations is the ilr
-    transformation.
Boolean specifying if formation fractions
+constants should be transformed in the model specification used in the
+fitting for better compliance with the assumption of normal distribution
+of the estimator. The default (TRUE) is to do transformations. The g
+parameter of the DFOP and HS models are also transformed, as they can also
+be seen as compositional data. The transformation used for these
+transformations is the ilr transformation.
transparms
Transformed parameters of kinetic models as used in the
+fitting procedure.

     

     

-    
+
     
Value

 
-    
A vector of transformed or backtransformed parameters with the same names
-  as the original parameters.

+    
A vector of transformed or backtransformed parameters with the same
+  names as the original parameters.

+    
Details

+
+    
The transformation of sets of formation fractions is fragile, as it supposes
+the same ordering of the components in forward and backward transformation.
+This is no problem for the internal use in mkinfit.

+    
Functions

+
     
+
    
+
  • backtransform_odeparms: Backtransform the set of transformed parameters
    • 
+

 
     
Examples

-    
SFO_SFO <- mkinmod(
+    

+SFO_SFO <- mkinmod(
   parent = list(type = "SFO", 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 <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.s <- summary(fit)
@@ -278,15 +286,16 @@ The transformation of sets of formation fractions is fragile, as it supposes
 #> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075
 #> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019
 #> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140
# }
+