From 9275bcb39b5ee25753ef489d334b4906401970b3 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 14 Nov 2022 21:47:45 +0100 Subject: Update online docs --- docs/dev/reference/DFOP.solution-1.png | Bin 31078 -> 31230 bytes docs/dev/reference/DFOP.solution.html | 237 ++++------ docs/dev/reference/FOMC.solution-1.png | Bin 28900 -> 29016 bytes docs/dev/reference/FOMC.solution.html | 241 ++++------ docs/dev/reference/HS.solution-1.png | Bin 29354 -> 29376 bytes docs/dev/reference/HS.solution.html | 233 ++++------ docs/dev/reference/IORE.solution-1.png | Bin 30028 -> 30069 bytes docs/dev/reference/IORE.solution.html | 258 +++++------ docs/dev/reference/Rplot002.png | Bin 17010 -> 16953 bytes docs/dev/reference/Rplot003.png | Bin 49705 -> 49894 bytes docs/dev/reference/Rplot004.png | Bin 58906 -> 59077 bytes docs/dev/reference/SFO.solution-1.png | Bin 29201 -> 29375 bytes docs/dev/reference/SFO.solution.html | 217 ++++----- docs/dev/reference/SFORB.solution-1.png | Bin 31408 -> 31566 bytes docs/dev/reference/SFORB.solution.html | 237 ++++------ docs/dev/reference/endpoints.html | 28 +- docs/dev/reference/logistic.solution-1.png | Bin 80293 -> 80362 bytes docs/dev/reference/logistic.solution-2.png | Bin 42118 -> 42071 bytes docs/dev/reference/logistic.solution.html | 323 ++++++-------- docs/dev/reference/mhmkin.html | 24 +- docs/dev/reference/mkinpredict.html | 678 ++++++++++++++--------------- docs/dev/reference/saem-1.png | Bin 46419 -> 53991 bytes docs/dev/reference/saem-2.png | Bin 49282 -> 49254 bytes docs/dev/reference/saem-3.png | Bin 128227 -> 127024 bytes docs/dev/reference/saem-4.png | Bin 171244 -> 173266 bytes docs/dev/reference/saem.html | 516 +++++++++++----------- docs/dev/reference/summary.mkinfit.html | 9 +- 27 files changed, 1310 insertions(+), 1691 deletions(-) (limited to 'docs/dev/reference') diff --git a/docs/dev/reference/DFOP.solution-1.png b/docs/dev/reference/DFOP.solution-1.png index 616e19d5..5ebba336 100644 Binary files a/docs/dev/reference/DFOP.solution-1.png and b/docs/dev/reference/DFOP.solution-1.png differ diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index 3ee660f2..f41d8e9b 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -1,68 +1,13 @@ - - - - - - - -Double First-Order in Parallel kinetics — DFOP.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Double First-Order in Parallel kinetics — DFOP.solution • mkin - + + - - - -
-
- -
- -
+
@@ -149,89 +91,92 @@ two exponential decline functions." /> two exponential decline functions.

-
DFOP.solution(t, parent_0, k1, k2, g)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

k1

First kinetic constant.

k2

Second kinetic constant.

g

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 +

+
DFOP.solution(t, parent_0, k1, k2, g)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ + +
k1
+

First kinetic constant.

+ + +
k2
+

Second kinetic constant.

+ + +
g
+

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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

-

See also

- - - -

Examples

-
- plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100)) -
-
+http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

+
+
+

See also

+ +
+ +
+

Examples

+

+  plot(function(x) DFOP.solution(x, 100, 5, 0.5, 0.3), 0, 4, ylim = c(0,100))
+
+
+
+
+
- - - + + diff --git a/docs/dev/reference/FOMC.solution-1.png b/docs/dev/reference/FOMC.solution-1.png index 1d32355a..9d222d42 100644 Binary files a/docs/dev/reference/FOMC.solution-1.png and b/docs/dev/reference/FOMC.solution-1.png differ diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index 1a5124e0..f64e759c 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -1,68 +1,13 @@ - - - - - - - -First-Order Multi-Compartment kinetics — FOMC.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -First-Order Multi-Compartment kinetics — FOMC.solution • mkin - - + + - - -
-
- -
- -
+
@@ -149,98 +91,103 @@ a decreasing rate constant." /> a decreasing rate constant.

-
FOMC.solution(t, parent_0, alpha, beta)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

alpha

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

beta

Location parameter.

- -

Value

- -

The value of the response variable at time t.

-

Details

+
+
FOMC.solution(t, parent_0, alpha, beta)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ + +
alpha
+

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

+ + +
beta
+

Location parameter.

+
+
+

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

- +
+
+

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 +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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

+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 Technology 24, 1032-1038

-

See also

- - - -

Examples

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

See also

+ +
+ +
+

Examples

+

+  plot(function(x) FOMC.solution(x, 100, 10, 2), 0, 2, ylim = c(0, 100))
+
+
+
+
+
- - - + + diff --git a/docs/dev/reference/HS.solution-1.png b/docs/dev/reference/HS.solution-1.png index 32d04b2d..dd7a76c8 100644 Binary files a/docs/dev/reference/HS.solution-1.png and b/docs/dev/reference/HS.solution-1.png differ diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 1b79e8b6..5fcef9c0 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -1,68 +1,13 @@ - - - - - - - -Hockey-Stick kinetics — HS.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Hockey-Stick kinetics — HS.solution • mkin - + + - - - -
-
- -
- -
+
@@ -149,90 +91,93 @@ between them." /> between them.

-
HS.solution(t, parent_0, k1, k2, tb)
- -

Arguments

- - - - - - - - - - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

k1

First kinetic constant.

k2

Second kinetic constant.

tb

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

+
+
HS.solution(t, parent_0, k1, k2, tb)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ -

Value

+
k1
+

First kinetic constant.

-

The value of the response variable at time t.

-

References

-

FOCUS (2006) “Guidance Document on Estimating Persistence +

k2
+

Second kinetic constant.

+ + +
tb
+

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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

-

See also

- - - -

Examples

-
- plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100)) -
-
+http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

+
+
+

See also

+ +
+ +
+

Examples

+

+  plot(function(x) HS.solution(x, 100, 2, 0.3, 0.5), 0, 2, ylim=c(0,100))
+
+
+
+
+
- - - + + diff --git a/docs/dev/reference/IORE.solution-1.png b/docs/dev/reference/IORE.solution-1.png index 42643a58..9b6ab58f 100644 Binary files a/docs/dev/reference/IORE.solution-1.png and b/docs/dev/reference/IORE.solution-1.png differ diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index bc17319e..90eccde9 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -1,68 +1,13 @@ - - - - - - - -Indeterminate order rate equation kinetics — IORE.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Indeterminate order rate equation kinetics — IORE.solution • mkin - + + - - - -
-
- -
- -
+
@@ -149,100 +91,106 @@ a concentration dependent rate constant." /> a concentration dependent rate constant.

-
IORE.solution(t, parent_0, k__iore, N)
- -

Arguments

- - - - - - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

k__iore

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

N

Exponent describing the nonlinearity of the rate equation

- -

Value

- -

The value of the response variable at time t.

-

Note

+
+
IORE.solution(t, parent_0, k__iore, N)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ + +
k__iore
+

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

+ +
N
+

Exponent describing the nonlinearity of the rate equation

+ +
+
+

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 +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

- +
+
+

References

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

-

See also

- - - -

Examples

-
- 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) - - print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par, - row.names = paste("model par", 1:4))) -
#> fit.fomc.par fit.iore.par fit.iore.deS.par -#> model par 1 85.87489063 85.874890 85.874890 -#> model par 2 0.05192238 -4.826631 -4.826631 -#> model par 3 0.65096665 1.949403 1.949403 -#> model par 4 1.85744396 1.857444 1.857444
print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes, - iore.deS = endpoints(fit.iore)$distimes)) -
#> DT50 DT90 DT50back -#> fomc 1.785233 15.1479 4.559973 -#> iore 1.785233 15.1479 4.559973 -#> iore.deS 1.785233 15.1479 4.559973
# } - -
+
+
+

See also

+ +
+ +
+

Examples

+

+  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)
+
+    print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,
+                     row.names = paste("model par", 1:4)))
+#>             fit.fomc.par fit.iore.par fit.iore.deS.par
+#> model par 1  85.87489063    85.874890        85.874890
+#> model par 2   0.05192238    -4.826631        -4.826631
+#> model par 3   0.65096665     1.949403         1.949403
+#> model par 4   1.85744396     1.857444         1.857444
+    print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,
+                iore.deS = endpoints(fit.iore)$distimes))
+#>              DT50    DT90 DT50back
+#> fomc     1.785233 15.1479 4.559973
+#> iore     1.785233 15.1479 4.559973
+#> iore.deS 1.785233 15.1479 4.559973
+  # }
+
+
+
+
- - - + + diff --git a/docs/dev/reference/Rplot002.png b/docs/dev/reference/Rplot002.png index f06a860e..27feab09 100644 Binary files a/docs/dev/reference/Rplot002.png and b/docs/dev/reference/Rplot002.png differ diff --git a/docs/dev/reference/Rplot003.png b/docs/dev/reference/Rplot003.png index 1af5d4b4..774715e0 100644 Binary files a/docs/dev/reference/Rplot003.png and b/docs/dev/reference/Rplot003.png differ diff --git a/docs/dev/reference/Rplot004.png b/docs/dev/reference/Rplot004.png index 12d337a4..37e0e95e 100644 Binary files a/docs/dev/reference/Rplot004.png and b/docs/dev/reference/Rplot004.png differ diff --git a/docs/dev/reference/SFO.solution-1.png b/docs/dev/reference/SFO.solution-1.png index 56d27ef6..a00499cb 100644 Binary files a/docs/dev/reference/SFO.solution-1.png and b/docs/dev/reference/SFO.solution-1.png differ diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index 43c434c6..970a62c5 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -1,67 +1,12 @@ - - - - - - - -Single First-Order kinetics — SFO.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Single First-Order kinetics — SFO.solution • mkin + + - - - - -
-
- -
- -
+
@@ -147,80 +89,83 @@

Function describing exponential decline from a defined starting value.

-
SFO.solution(t, parent_0, k)
- -

Arguments

- - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

k

Kinetic rate constant.

- -

Value

- -

The value of the response variable at time t.

-

References

- -

FOCUS (2006) “Guidance Document on Estimating Persistence +

+
SFO.solution(t, parent_0, k)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ + +
k
+

Kinetic rate 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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

-

See also

- - - -

Examples

-
- plot(function(x) SFO.solution(x, 100, 3), 0, 2) -
-
+http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

+
+
+

See also

+ +
+ +
+

Examples

+

+  plot(function(x) SFO.solution(x, 100, 3), 0, 2)
+
+
+
+
+
- - - + + diff --git a/docs/dev/reference/SFORB.solution-1.png b/docs/dev/reference/SFORB.solution-1.png index 4eeb0d41..7bea3b78 100644 Binary files a/docs/dev/reference/SFORB.solution-1.png and b/docs/dev/reference/SFORB.solution-1.png differ diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index 807fbe5c..e3e43557 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -1,71 +1,16 @@ - - - - - - - -Single First-Order Reversible Binding kinetics — SFORB.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Single First-Order Reversible Binding kinetics — SFORB.solution • mkin - - - - - - - - - - - + + - - -
-
- -
- -
+
@@ -155,90 +97,93 @@ 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

- - - - - - - - - - - - - - - - - - - - - - -
t

Time.

parent_0

Starting value for the response variable at time zero.

k_12

Kinetic constant describing transfer from free to bound.

k_21

Kinetic constant describing transfer from bound to free.

k_1output

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.

-

References

+
+
SFORB.solution(t, parent_0, k_12, k_21, k_1output)
+
+ +
+

Arguments

+
t
+

Time.

+ + +
parent_0
+

Starting value for the response variable at time zero.

+ + +
k_12
+

Kinetic constant describing transfer from free to bound.

+ -

FOCUS (2006) “Guidance Document on Estimating Persistence +

k_21
+

Kinetic constant describing transfer from bound to free.

+ + +
k_1output
+

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.

+
+
+

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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

-

See also

- - - -

Examples

-
- plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2) -
-
+http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

+
+
+

See also

+ +
+ +
+

Examples

+

+  plot(function(x) SFORB.solution(x, 100, 0.5, 2, 3), 0, 2)
+
+
+
+
+
- - - + + diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index ed72ec47..e8198521 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -23,7 +23,7 @@ advantage that the SFORB model can also be used for metabolites."> mkin - 1.1.2 + 1.2.0 @@ -50,11 +50,14 @@ advantage that the SFORB model can also be used for metabolites.">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
  • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
  • @@ -104,21 +107,20 @@ advantage that the SFORB model can also be used for metabolites.

    Arguments

    fit
    -

    An object of class mkinfit, nlme.mmkin or saem.mmkin, -or another object that has list components -mkinmod containing an mkinmod degradation model, and two numeric vectors, -bparms.optim and bparms.fixed, that contain parameter values -for that model.

    +

    An object of class mkinfit, nlme.mmkin or saem.mmkin, or +another object that has list components mkinmod containing an mkinmod +degradation model, and two numeric vectors, bparms.optim and bparms.fixed, +that contain parameter values for that model.

    Value

    -

    A list with a matrix of dissipation times named distimes, -and, if applicable, a vector of formation fractions named ff -and, if the SFORB model was in use, a vector of eigenvalues -of these SFORB models, equivalent to DFOP rate constants

    +

    A list with a matrix of dissipation times named distimes, and, if +applicable, a vector of formation fractions named ff and, if the SFORB model +was in use, a vector of eigenvalues of these SFORB models, equivalent to +DFOP rate constants

    Details

    @@ -158,8 +160,8 @@ HS and DFOP, as well as from Eigenvalues b1 and b2 of any SFORB models

    #> 1 #> #> $SFORB -#> parent_b1 parent_b2 -#> 0.4595574 0.0178488 +#> parent_b1 parent_b2 parent_g +#> 0.4595574 0.0178488 0.8539454 #> #> $distimes #> DT50 DT90 DT50back DT50_parent_b1 DT50_parent_b2 diff --git a/docs/dev/reference/logistic.solution-1.png b/docs/dev/reference/logistic.solution-1.png index 84d8e722..73dad0a4 100644 Binary files a/docs/dev/reference/logistic.solution-1.png and b/docs/dev/reference/logistic.solution-1.png differ diff --git a/docs/dev/reference/logistic.solution-2.png b/docs/dev/reference/logistic.solution-2.png index 73e6436d..8d2514a3 100644 Binary files a/docs/dev/reference/logistic.solution-2.png and b/docs/dev/reference/logistic.solution-2.png differ diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index ab68c99e..1d1880fd 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -1,68 +1,13 @@ - - - - - - - -Logistic kinetics — logistic.solution • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Logistic kinetics — logistic.solution • mkin - + + - - - -
    -
    - -
    - -
    +
    @@ -149,136 +91,143 @@ 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)
    +
    +
    logistic.solution(t, parent_0, kmax, k0, r)
    +
    -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    t

    Time.

    parent_0

    Starting value for the response variable at time zero.

    kmax

    Maximum rate constant.

    k0

    Minimum rate constant effective at time zero.

    r

    Growth rate of the increase in the rate constant.

    +
    +

    Arguments

    +
    t
    +

    Time.

    -

    Value

    -

    The value of the response variable at time t.

    -

    Note

    +
    parent_0
    +

    Starting value for the response variable at time zero.

    -

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

    -

    References

    -

    FOCUS (2006) “Guidance Document on Estimating Persistence +

    kmax
    +

    Maximum rate constant.

    + + +
    k0
    +

    Minimum rate constant effective at time zero.

    + + +
    r
    +

    Growth rate of the increase in the rate constant.

    + +
    +
    +

    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 (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, +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 (2014) “Generic guidance for Estimating Persistence +http://esdac.jrc.ec.europa.eu/projects/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, +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

    -

    See also

    - -

    Other parent solutions: -DFOP.solution(), -FOMC.solution(), -HS.solution(), -IORE.solution(), -SFO.solution(), -SFORB.solution()

    - -

    Examples

    -
    - # 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), - from = 0, to = 100, add = TRUE, lty = 2, col = 2) -
    plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8), - from = 0, to = 100, add = TRUE, lty = 3, col = 3) -
    plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2), - from = 0, to = 100, add = TRUE, lty = 4, col = 4) -
    plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2), - from = 0, to = 100, add = TRUE, lty = 5, col = 5) -
    legend("topright", inset = 0.05, - legend = paste0("k0 = ", c(0.0001, 0.0001, 0.0001, 0.001, 0.08), - ", r = ", c(0.2, 0.4, 0.8, 0.2, 0.2)), - lty = 1:5, col = 1:5) -
    - # Fit with synthetic data - logistic <- mkinmod(parent = mkinsub("logistic")) - - sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) - parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2) - d_logistic <- mkinpredict(logistic, - parms_logistic, c(parent = 100), - sampling_times) - d_2_1 <- add_err(d_logistic, - sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07), - n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]] +http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

    +
    +
    +

    See also

    +

    Other parent solutions: +DFOP.solution(), +FOMC.solution(), +HS.solution(), +IORE.solution(), +SFO.solution(), +SFORB.solution()

    +
    - m <- mkinfit("logistic", d_2_1, quiet = TRUE) - plot_sep(m) -
    summary(m)$bpar -
    #> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 -#> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 -#> r 2.263946e-01 0.1718110662 1.317695 1.061043e-01 4.335843e-02 -#> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 -#> Upper -#> parent_0 109.9341588 -#> kmax 0.1041853 -#> k0 0.4448749 -#> r 1.1821120 -#> sigma 7.3256566
    endpoints(m)$distimes -
    #> DT50 DT90 DT50_k0 DT50_kmax -#> parent 36.86533 62.41511 4297.853 10.83349
    -
    +
    +

    Examples

    +
    
    +  # 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),
    +       from = 0, to = 100, add = TRUE, lty = 2, col = 2)
    +  plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),
    +       from = 0, to = 100, add = TRUE, lty = 3, col = 3)
    +  plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),
    +       from = 0, to = 100, add = TRUE, lty = 4, col = 4)
    +  plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),
    +       from = 0, to = 100, add = TRUE, lty = 5, col = 5)
    +  legend("topright", inset = 0.05,
    +         legend = paste0("k0 = ", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),
    +                         ", r = ", c(0.2, 0.4, 0.8, 0.2, 0.2)),
    +         lty = 1:5, col = 1:5)
    +
    +
    +  # Fit with synthetic data
    +  logistic <- mkinmod(parent = mkinsub("logistic"))
    +
    +  sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
    +  parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)
    +  d_logistic <- mkinpredict(logistic,
    +    parms_logistic, c(parent = 100),
    +    sampling_times)
    +  d_2_1 <- add_err(d_logistic,
    +    sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
    +    n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]
    +
    +  m <- mkinfit("logistic", d_2_1, quiet = TRUE)
    +  plot_sep(m)
    +
    +  summary(m)$bpar
    +#>              Estimate   se_notrans   t value       Pr(>t)        Lower
    +#> parent_0 1.057896e+02 1.9023449590 55.610120 3.768360e-16 1.016451e+02
    +#> kmax     6.398190e-02 0.0143201029  4.467978 3.841828e-04 3.929235e-02
    +#> k0       1.612775e-04 0.0005866813  0.274898 3.940351e-01 5.846688e-08
    +#> r        2.263946e-01 0.1718110662  1.317695 1.061043e-01 4.335843e-02
    +#> sigma    5.332935e+00 0.9145907310  5.830952 4.036926e-05 3.340213e+00
    +#>                Upper
    +#> parent_0 109.9341588
    +#> kmax       0.1041853
    +#> k0         0.4448749
    +#> r          1.1821120
    +#> sigma      7.3256566
    +  endpoints(m)$distimes
    +#>            DT50     DT90  DT50_k0 DT50_kmax
    +#> parent 36.86533 62.41511 4297.853  10.83349
    +
    +
    +
    +
    -
    - +
    - - + + diff --git a/docs/dev/reference/mhmkin.html b/docs/dev/reference/mhmkin.html index cbfeae60..e87e20a1 100644 --- a/docs/dev/reference/mhmkin.html +++ b/docs/dev/reference/mhmkin.html @@ -99,7 +99,7 @@ mixed-effects model fitting functions.

    -
    mhmkin(objects, backend = "saemix", algorithm = "saem", ...)
    +    
    mhmkin(objects, ...)
     
     # S3 method for mmkin
     mhmkin(objects, ...)
    @@ -108,6 +108,9 @@ mhmkin(objects, ...)
     mhmkin(
       objects,
       backend = "saemix",
    +  algorithm = "saem",
    +  no_random_effect = NULL,
    +  auto_ranef_threshold = 3,
       ...,
       cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(),
       cluster = NULL
    @@ -129,6 +132,11 @@ Alternatively, a single mmkin object containing fits of
     degradation models to the same data

    +
    ...
    +

    Further arguments that will be passed to the nonlinear mixed-effects +model fitting function.

    + +
    backend

    The backend to be used for fitting. Currently, only saemix is supported

    @@ -138,9 +146,17 @@ supported

    The algorithm to be used for fitting (currently not used)

    -
    ...
    -

    Further arguments that will be passed to the nonlinear mixed-effects -model fitting function.

    +
    no_random_effect
    +

    Default is NULL and will be passed to saem. If +you specify "auto", random effects are only included if the number +of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. +Beware that while this may make for convenient model reduction or even +numerical stability of the algorithm, it will likely lead to +underparameterised models.

    + + +
    auto_ranef_threshold
    +

    See 'no_random_effect.

    cores
    diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 1ebaecb5..14f2b75b 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -1,69 +1,14 @@ - - - - - - - -Produce predictions from a kinetic model using specific parameters — mkinpredict • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Produce predictions from a kinetic model using specific parameters — mkinpredict • mkin - - - - - - - - - - - + + - - -
    -
    - -
    - -
    +

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

    -
    mkinpredict(x, odeparms, odeini, outtimes, ...)
    -
    -# 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,
    -  na_stop = 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

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    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.

    odeini

    A numeric vector containing the initial values of the state +

    +
    mkinpredict(x, odeparms, odeini, outtimes, ...)
    +
    +# 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,
    +  maxsteps = 20000,
    +  map_output = TRUE,
    +  na_stop = 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

    +
    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.

    + + +
    odeini
    +

    A numeric vector 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.

    ...

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

    solution_type

    The method that should be used for producing 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.

    + + +
    ...
    +

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

    + + +
    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.

    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.

    rtol

    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 +some models e.g. using FOMC for the parent compound.

    + + +
    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.

    + + +
    rtol
    +

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

    + + +
    maxsteps
    +

    Maximum number of steps, passed to ode.

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

    na_stop

    Should it be an error if deSolve::ode returns NaN values

    +as these always return mapped output.

    + -

    Value

    +
    na_stop
    +

    Should it be an error if ode returns NaN values

    -

    A matrix with the numeric solution in wide format

    -

    Author

    +
    +
    +

    Value

    + +

    A matrix with the numeric solution in wide format

    +
    +
    +

    Author

    Johannes Ranke

    +
    -

    Examples

    -
    -SFO <- mkinmod(degradinol = mkinsub("SFO")) -# Compare solution types -mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20, - solution_type = "analytical") -
    #> time degradinol -#> 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 -#> 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 -#> 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 -#> 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 -#> 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 -#> 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 = "max") -
    #> Temporary DLL for differentials generated and loaded
    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 = 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 = 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 -#> 2 deSolve_compiled 1.0 0.005 -#> 4 analytical 1.0 0.005 -#> 1 eigen 4.4 0.022 -#> 3 deSolve 46.8 0.234
    -# \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 = "deSolve") - head(mkinpredict(f)) -
    #> time parent m1 -#> 0 0.0 82.49216 0.000000 -#> 0.1 0.1 80.00562 1.236394 -#> 0.2 0.2 77.59404 2.423201 -#> 0.3 0.3 75.25514 3.562040 -#> 0.4 0.4 72.98675 4.654478 -#> 0.5 0.5 70.78673 5.702033
    # } - -
    +
    +

    Examples

    +
    
    +SFO <- mkinmod(degradinol = mkinsub("SFO"))
    +# Compare solution types
    +mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
    +      solution_type = "analytical")
    +#>    time  degradinol
    +#> 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
    +#> 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
    +#> 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
    +#> 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 
    +#> 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 
    +#> 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 = "max")
    +#> Temporary DLL for differentials generated and loaded
    +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 = 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 = 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,])
    +}
    +#> Loading required package: rbenchmark
    +#>               test relative elapsed
    +#> 2 deSolve_compiled     1.00   0.004
    +#> 4       analytical     5.25   0.021
    +#> 1            eigen     6.00   0.024
    +#> 3          deSolve    52.50   0.210
    +
    +# \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 = "deSolve")
    +  head(mkinpredict(f))
    +#> DLSODA-  At current T (=R1), MXSTEP (=I1) steps   
    +#>       taken on this call before reaching TOUT     
    +#> In above message, I1 = 1
    +#>  
    +#> In above message, R1 = 9.99904e-07
    +#>  
    +#> Warning: an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxsteps
    +#> Warning: Returning early. Results are accurate, as far as they go
    +#> Error in out[available, var]: (subscript) logical subscript too long
    +# }
    +
    +
    +
    +
    -
    - +
    - - + + diff --git a/docs/dev/reference/saem-1.png b/docs/dev/reference/saem-1.png index 08939d4f..9e310252 100644 Binary files a/docs/dev/reference/saem-1.png and b/docs/dev/reference/saem-1.png differ diff --git a/docs/dev/reference/saem-2.png b/docs/dev/reference/saem-2.png index b737db03..de1bcf57 100644 Binary files a/docs/dev/reference/saem-2.png and b/docs/dev/reference/saem-2.png differ diff --git a/docs/dev/reference/saem-3.png b/docs/dev/reference/saem-3.png index 08e0f544..de569ce0 100644 Binary files a/docs/dev/reference/saem-3.png and b/docs/dev/reference/saem-3.png differ diff --git a/docs/dev/reference/saem-4.png b/docs/dev/reference/saem-4.png index bb775c25..0f2ee3e7 100644 Binary files a/docs/dev/reference/saem-4.png and b/docs/dev/reference/saem-4.png differ diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index 8ea0ef6e..968dc68c 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -106,13 +106,14 @@ Expectation Maximisation algorithm (SAEM).

    conf.level = 0.6, solution_type = "auto", covariance.model = "auto", + omega.init = "auto", covariates = NULL, covariate_models = NULL, no_random_effect = NULL, + error.init = c(3, 0.1), nbiter.saemix = c(300, 100), control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix, save = FALSE, save.graphs = FALSE), - fail_with_errors = TRUE, verbose = FALSE, quiet = FALSE, ... @@ -129,8 +130,10 @@ Expectation Maximisation algorithm (SAEM).

    degparms_start = numeric(), covariance.model = "auto", no_random_effect = NULL, + omega.init = "auto", covariates = NULL, covariate_models = NULL, + error.init = numeric(), test_log_parms = FALSE, conf.level = 0.6, verbose = FALSE, @@ -189,11 +192,21 @@ automatic choice is not desired

    covariance.model
    -

    Will be passed to saemix::SaemixModel(). Per +

    Will be passed to saemix::saemixModel(). Per default, uncorrelated random effects are specified for all degradation parameters.

    +
    omega.init
    +

    Will be passed to saemix::saemixModel(). If using +mkin transformations and the default covariance model with optionally +excluded random effects, the variances of the degradation parameters +are estimated using mean_degparms, with testing of untransformed +log parameters for significant difference from zero. If not using +mkin transformations or a custom covariance model, the default +initialisation of saemix::saemixModel is used for omega.init.

    + +
    covariates

    A data frame with covariate data for use in 'covariate_models', with dataset names as row names.

    @@ -211,6 +224,10 @@ which there should be no variability over the groups. Only used if the covariance model is not explicitly specified.

    +
    error.init
    +

    Will be passed to saemix::saemixModel().

    + +
    nbiter.saemix

    Convenience option to increase the number of iterations

    @@ -220,11 +237,6 @@ iterations

    Passed to saemix::saemix.

    -
    fail_with_errors
    -

    Should a failure to compute standard errors -from the inverse of the Fisher Information Matrix be a failure?

    - -
    verbose

    Should we print information about created objects of type saemix::SaemixModel and saemix::SaemixData?

    @@ -293,15 +305,15 @@ using mmkin.

    #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> #> npar AIC BIC Lik -#> f_saem_sfo 5 624.26 622.31 -307.13 -#> f_saem_fomc 7 467.87 465.13 -226.93 -#> f_saem_dfop 9 493.98 490.47 -237.99 +#> f_saem_sfo 5 624.33 622.38 -307.17 +#> f_saem_fomc 7 467.85 465.11 -226.92 +#> f_saem_dfop 9 493.76 490.24 -237.88 anova(f_saem_sfo, f_saem_dfop, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> #> npar AIC BIC Lik Chisq Df Pr(>Chisq) -#> f_saem_sfo 5 624.26 622.31 -307.13 -#> f_saem_dfop 9 493.98 490.47 -237.99 138.28 4 < 2.2e-16 *** +#> f_saem_sfo 5 624.33 622.38 -307.17 +#> f_saem_dfop 9 493.76 490.24 -237.88 138.57 4 < 2.2e-16 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 illparms(f_saem_dfop) @@ -311,16 +323,16 @@ using mmkin.

    #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> #> npar AIC BIC Lik Chisq Df Pr(>Chisq) -#> f_saem_dfop_red 8 490.64 487.52 -237.32 -#> f_saem_dfop 9 493.98 490.47 -237.99 0 1 1 +#> f_saem_dfop_red 8 488.68 485.55 -236.34 +#> f_saem_dfop 9 493.76 490.24 -237.88 0 1 1 anova(f_saem_sfo, f_saem_fomc, f_saem_dfop) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> #> npar AIC BIC Lik -#> f_saem_sfo 5 624.26 622.31 -307.13 -#> f_saem_fomc 7 467.87 465.13 -226.93 -#> f_saem_dfop 9 493.98 490.47 -237.99 +#> f_saem_sfo 5 624.33 622.38 -307.17 +#> f_saem_fomc 7 467.85 465.11 -226.92 +#> f_saem_dfop 9 493.76 490.24 -237.88 # The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix) @@ -335,9 +347,9 @@ using mmkin.

    compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so) #> Likelihoods calculated by importance sampling #> AIC BIC -#> 1 624.2598 622.3070 -#> 2 467.8664 465.1324 -#> 3 493.9811 490.4660 +#> 1 624.3316 622.3788 +#> 2 467.8472 465.1132 +#> 3 493.7592 490.2441 plot(f_saem_fomc$so, plot.type = "convergence") plot(f_saem_fomc$so, plot.type = "individual.fit") @@ -354,9 +366,9 @@ using mmkin.

    anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE) #> Data: 90 observations of 1 variable(s) grouped in 5 datasets #> -#> npar AIC BIC Lik Chisq Df Pr(>Chisq) -#> f_saem_fomc 7 467.87 465.13 -226.93 -#> f_saem_fomc_tc 8 469.80 466.68 -226.90 0.0645 1 0.7995 +#> npar AIC BIC Lik Chisq Df Pr(>Chisq) +#> f_saem_fomc 7 467.85 465.11 -226.92 +#> f_saem_fomc_tc 8 469.83 466.71 -226.92 0.015 1 0.9027 sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO")) @@ -393,32 +405,32 @@ using mmkin.

    #> 170 observations of 2 variable(s) grouped in 5 datasets #> #> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 842 836.9 -408 +#> AIC BIC logLik +#> 839.2 834.1 -406.6 #> #> Fitted parameters: -#> estimate lower upper -#> parent_0 93.7701 91.1458 96.3945 -#> log_k_A1 -5.8116 -7.5998 -4.0234 -#> f_parent_qlogis -0.9608 -1.3654 -0.5562 -#> log_k1 -2.5841 -3.6876 -1.4805 -#> log_k2 -3.5228 -5.3254 -1.7203 -#> g_qlogis -0.1027 -0.8719 0.6665 -#> a.1 1.8856 1.6676 2.1037 -#> SD.parent_0 2.7682 0.7668 4.7695 -#> SD.log_k_A1 1.7447 0.4047 3.0848 -#> SD.f_parent_qlogis 0.4525 0.1620 0.7431 -#> SD.log_k1 1.2423 0.4560 2.0285 -#> SD.log_k2 2.0390 0.7601 3.3180 -#> SD.g_qlogis 0.4439 -0.3069 1.1947 +#> estimate lower upper +#> parent_0 93.70402 91.04104 96.3670 +#> log_k_A1 -5.83760 -7.66452 -4.0107 +#> f_parent_qlogis -0.95718 -1.35955 -0.5548 +#> log_k1 -2.35514 -3.39402 -1.3163 +#> log_k2 -3.79634 -5.64009 -1.9526 +#> g_qlogis -0.02108 -0.66463 0.6225 +#> a.1 1.88191 1.66491 2.0989 +#> SD.parent_0 2.81628 0.78922 4.8433 +#> SD.log_k_A1 1.78751 0.42105 3.1540 +#> SD.f_parent_qlogis 0.45016 0.16116 0.7391 +#> SD.log_k1 1.06923 0.31676 1.8217 +#> SD.log_k2 2.03768 0.70938 3.3660 +#> SD.g_qlogis 0.44024 -0.09262 0.9731 plot(f_saem_dfop_sfo) summary(f_saem_dfop_sfo, data = TRUE) #> saemix version used for fitting: 3.2 #> mkin version used for pre-fitting: 1.2.0 #> R version used for fitting: 4.2.2 -#> Date of fit: Tue Nov 1 14:12:07 2022 -#> Date of summary: Tue Nov 1 14:12:07 2022 +#> Date of fit: Mon Nov 14 21:06:30 2022 +#> Date of summary: Mon Nov 14 21:06:30 2022 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -433,7 +445,7 @@ using mmkin.

    #> #> Model predictions using solution type analytical #> -#> Fitted in 8.45 s +#> Fitted in 9.625 s #> Using 300, 100 iterations and 10 chains #> #> Variance model: Constant variance @@ -450,237 +462,237 @@ using mmkin.

    #> Results: #> #> Likelihood computed by importance sampling -#> AIC BIC logLik -#> 842 836.9 -408 +#> AIC BIC logLik +#> 839.2 834.1 -406.6 #> #> Optimised parameters: -#> est. lower upper -#> parent_0 93.7701 91.1458 96.3945 -#> log_k_A1 -5.8116 -7.5998 -4.0234 -#> f_parent_qlogis -0.9608 -1.3654 -0.5562 -#> log_k1 -2.5841 -3.6876 -1.4805 -#> log_k2 -3.5228 -5.3254 -1.7203 -#> g_qlogis -0.1027 -0.8719 0.6665 -#> a.1 1.8856 1.6676 2.1037 -#> SD.parent_0 2.7682 0.7668 4.7695 -#> SD.log_k_A1 1.7447 0.4047 3.0848 -#> SD.f_parent_qlogis 0.4525 0.1620 0.7431 -#> SD.log_k1 1.2423 0.4560 2.0285 -#> SD.log_k2 2.0390 0.7601 3.3180 -#> SD.g_qlogis 0.4439 -0.3069 1.1947 +#> est. lower upper +#> parent_0 93.70402 91.04104 96.3670 +#> log_k_A1 -5.83760 -7.66452 -4.0107 +#> f_parent_qlogis -0.95718 -1.35955 -0.5548 +#> log_k1 -2.35514 -3.39402 -1.3163 +#> log_k2 -3.79634 -5.64009 -1.9526 +#> g_qlogis -0.02108 -0.66463 0.6225 +#> a.1 1.88191 1.66491 2.0989 +#> SD.parent_0 2.81628 0.78922 4.8433 +#> SD.log_k_A1 1.78751 0.42105 3.1540 +#> SD.f_parent_qlogis 0.45016 0.16116 0.7391 +#> SD.log_k1 1.06923 0.31676 1.8217 +#> SD.log_k2 2.03768 0.70938 3.3660 +#> SD.g_qlogis 0.44024 -0.09262 0.9731 #> #> Correlation: #> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 -#> log_k_A1 -0.0160 -#> f_parent_qlogis -0.0263 0.0612 -#> log_k1 0.0100 -0.0014 -0.0033 -#> log_k2 0.0131 0.0050 -0.0011 0.0071 -#> g_qlogis -0.0419 -0.0199 0.0026 -0.0765 -0.0707 +#> log_k_A1 -0.0147 +#> f_parent_qlogis -0.0269 0.0573 +#> log_k1 0.0263 -0.0011 -0.0040 +#> log_k2 0.0020 0.0065 -0.0002 -0.0776 +#> g_qlogis -0.0248 -0.0180 -0.0004 -0.0903 -0.0603 #> #> Random effects: -#> est. lower upper -#> SD.parent_0 2.7682 0.7668 4.7695 -#> SD.log_k_A1 1.7447 0.4047 3.0848 -#> SD.f_parent_qlogis 0.4525 0.1620 0.7431 -#> SD.log_k1 1.2423 0.4560 2.0285 -#> SD.log_k2 2.0390 0.7601 3.3180 -#> SD.g_qlogis 0.4439 -0.3069 1.1947 +#> est. lower upper +#> SD.parent_0 2.8163 0.78922 4.8433 +#> SD.log_k_A1 1.7875 0.42105 3.1540 +#> SD.f_parent_qlogis 0.4502 0.16116 0.7391 +#> SD.log_k1 1.0692 0.31676 1.8217 +#> SD.log_k2 2.0377 0.70938 3.3660 +#> SD.g_qlogis 0.4402 -0.09262 0.9731 #> #> Variance model: #> est. lower upper -#> a.1 1.886 1.668 2.104 +#> a.1 1.882 1.665 2.099 #> #> Backtransformed parameters: #> est. lower upper -#> parent_0 93.770115 9.115e+01 96.39447 -#> k_A1 0.002993 5.005e-04 0.01789 -#> f_parent_to_A1 0.276720 2.034e-01 0.36443 -#> k1 0.075467 2.503e-02 0.22753 -#> k2 0.029516 4.867e-03 0.17902 -#> g 0.474353 2.949e-01 0.66073 +#> parent_0 93.704015 9.104e+01 96.36699 +#> k_A1 0.002916 4.692e-04 0.01812 +#> f_parent_to_A1 0.277443 2.043e-01 0.36475 +#> k1 0.094880 3.357e-02 0.26813 +#> k2 0.022453 3.553e-03 0.14191 +#> g 0.494731 3.397e-01 0.65078 #> #> Resulting formation fractions: #> ff -#> parent_A1 0.2767 -#> parent_sink 0.7233 +#> parent_A1 0.2774 +#> parent_sink 0.7226 #> #> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 14.56 58.26 17.54 9.185 23.48 -#> A1 231.62 769.41 NA NA NA +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 14.0 72.38 21.79 7.306 30.87 +#> A1 237.7 789.68 NA NA NA #> #> Data: #> ds name time observed predicted residual std standardized -#> Dataset 6 parent 0 97.2 95.78623 1.41377 1.886 0.749758 -#> Dataset 6 parent 0 96.4 95.78623 0.61377 1.886 0.325498 -#> Dataset 6 parent 3 71.1 71.34666 -0.24666 1.886 -0.130812 -#> Dataset 6 parent 3 69.2 71.34666 -2.14666 1.886 -1.138429 -#> Dataset 6 parent 6 58.1 56.49768 1.60232 1.886 0.849749 -#> Dataset 6 parent 6 56.6 56.49768 0.10232 1.886 0.054262 -#> Dataset 6 parent 10 44.4 44.53511 -0.13511 1.886 -0.071650 -#> Dataset 6 parent 10 43.4 44.53511 -1.13511 1.886 -0.601974 -#> Dataset 6 parent 20 33.3 29.77451 3.52549 1.886 1.869656 -#> Dataset 6 parent 20 29.2 29.77451 -0.57451 1.886 -0.304675 -#> Dataset 6 parent 34 17.6 19.32540 -1.72540 1.886 -0.915023 -#> Dataset 6 parent 34 18.0 19.32540 -1.32540 1.886 -0.702894 -#> Dataset 6 parent 55 10.5 10.42781 0.07219 1.886 0.038282 -#> Dataset 6 parent 55 9.3 10.42781 -1.12781 1.886 -0.598107 -#> Dataset 6 parent 90 4.5 3.74190 0.75810 1.886 0.402037 -#> Dataset 6 parent 90 4.7 3.74190 0.95810 1.886 0.508102 -#> Dataset 6 parent 112 3.0 1.96485 1.03515 1.886 0.548966 -#> Dataset 6 parent 112 3.4 1.96485 1.43515 1.886 0.761096 -#> Dataset 6 parent 132 2.3 1.09395 1.20605 1.886 0.639596 -#> Dataset 6 parent 132 2.7 1.09395 1.60605 1.886 0.851726 -#> Dataset 6 A1 3 4.3 4.72702 -0.42702 1.886 -0.226458 -#> Dataset 6 A1 3 4.6 4.72702 -0.12702 1.886 -0.067361 -#> Dataset 6 A1 6 7.0 7.51314 -0.51314 1.886 -0.272128 -#> Dataset 6 A1 6 7.2 7.51314 -0.31314 1.886 -0.166063 -#> Dataset 6 A1 10 8.2 9.63719 -1.43719 1.886 -0.762179 -#> Dataset 6 A1 10 8.0 9.63719 -1.63719 1.886 -0.868244 -#> Dataset 6 A1 20 11.0 11.84931 -0.84931 1.886 -0.450409 -#> Dataset 6 A1 20 13.7 11.84931 1.85069 1.886 0.981468 -#> Dataset 6 A1 34 11.5 12.82336 -1.32336 1.886 -0.701808 -#> Dataset 6 A1 34 12.7 12.82336 -0.12336 1.886 -0.065418 -#> Dataset 6 A1 55 14.9 12.89456 2.00544 1.886 1.063533 -#> Dataset 6 A1 55 14.5 12.89456 1.60544 1.886 0.851403 -#> Dataset 6 A1 90 12.1 11.55919 0.54081 1.886 0.286806 -#> Dataset 6 A1 90 12.3 11.55919 0.74081 1.886 0.392871 -#> Dataset 6 A1 112 9.9 10.42334 -0.52334 1.886 -0.277539 -#> Dataset 6 A1 112 10.2 10.42334 -0.22334 1.886 -0.118442 -#> Dataset 6 A1 132 8.8 9.37987 -0.57987 1.886 -0.307519 -#> Dataset 6 A1 132 7.8 9.37987 -1.57987 1.886 -0.837844 -#> Dataset 7 parent 0 93.6 90.95702 2.64298 1.886 1.401639 -#> Dataset 7 parent 0 92.3 90.95702 1.34298 1.886 0.712217 -#> Dataset 7 parent 3 87.0 84.77506 2.22494 1.886 1.179942 -#> Dataset 7 parent 3 82.2 84.77506 -2.57506 1.886 -1.365616 -#> Dataset 7 parent 7 74.0 77.60962 -3.60962 1.886 -1.914268 -#> Dataset 7 parent 7 73.9 77.60962 -3.70962 1.886 -1.967301 -#> Dataset 7 parent 14 64.2 67.50646 -3.30646 1.886 -1.753499 -#> Dataset 7 parent 14 69.5 67.50646 1.99354 1.886 1.057221 -#> Dataset 7 parent 30 54.0 52.48909 1.51091 1.886 0.801271 -#> Dataset 7 parent 30 54.6 52.48909 2.11091 1.886 1.119465 -#> Dataset 7 parent 60 41.1 39.54372 1.55628 1.886 0.825335 -#> Dataset 7 parent 60 38.4 39.54372 -1.14372 1.886 -0.606542 -#> Dataset 7 parent 90 32.5 33.87968 -1.37968 1.886 -0.731676 -#> Dataset 7 parent 90 35.5 33.87968 1.62032 1.886 0.859298 -#> Dataset 7 parent 120 28.1 30.41071 -2.31071 1.886 -1.225427 -#> Dataset 7 parent 120 29.0 30.41071 -1.41071 1.886 -0.748135 -#> Dataset 7 parent 180 26.5 25.36386 1.13614 1.886 0.602524 -#> Dataset 7 parent 180 27.6 25.36386 2.23614 1.886 1.185881 -#> Dataset 7 A1 3 3.9 2.74863 1.15137 1.886 0.610600 -#> Dataset 7 A1 3 3.1 2.74863 0.35137 1.886 0.186341 -#> Dataset 7 A1 7 6.9 5.92686 0.97314 1.886 0.516081 -#> Dataset 7 A1 7 6.6 5.92686 0.67314 1.886 0.356983 -#> Dataset 7 A1 14 10.4 10.38800 0.01200 1.886 0.006362 -#> Dataset 7 A1 14 8.3 10.38800 -2.08800 1.886 -1.107320 -#> Dataset 7 A1 30 14.4 16.93529 -2.53529 1.886 -1.344524 -#> Dataset 7 A1 30 13.7 16.93529 -3.23529 1.886 -1.715751 -#> Dataset 7 A1 60 22.1 22.33044 -0.23044 1.886 -0.122209 -#> Dataset 7 A1 60 22.3 22.33044 -0.03044 1.886 -0.016144 -#> Dataset 7 A1 90 27.5 24.42300 3.07700 1.886 1.631809 -#> Dataset 7 A1 90 25.4 24.42300 0.97700 1.886 0.518127 -#> Dataset 7 A1 120 28.0 25.51140 2.48860 1.886 1.319768 -#> Dataset 7 A1 120 26.6 25.51140 1.08860 1.886 0.577313 -#> Dataset 7 A1 180 25.8 26.80282 -1.00282 1.886 -0.531818 -#> Dataset 7 A1 180 25.3 26.80282 -1.50282 1.886 -0.796981 -#> Dataset 8 parent 0 91.9 91.08733 0.81267 1.886 0.430980 -#> Dataset 8 parent 0 90.8 91.08733 -0.28733 1.886 -0.152377 -#> Dataset 8 parent 1 64.9 67.55332 -2.65332 1.886 -1.407123 -#> Dataset 8 parent 1 66.2 67.55332 -1.35332 1.886 -0.717701 -#> Dataset 8 parent 3 43.5 41.65811 1.84189 1.886 0.976800 -#> Dataset 8 parent 3 44.1 41.65811 2.44189 1.886 1.294994 -#> Dataset 8 parent 8 18.3 19.65773 -1.35773 1.886 -0.720038 -#> Dataset 8 parent 8 18.1 19.65773 -1.55773 1.886 -0.826103 -#> Dataset 8 parent 14 10.2 10.65118 -0.45118 1.886 -0.239269 -#> Dataset 8 parent 14 10.8 10.65118 0.14882 1.886 0.078925 -#> Dataset 8 parent 27 4.9 3.11694 1.78306 1.886 0.945601 -#> Dataset 8 parent 27 3.3 3.11694 0.18306 1.886 0.097082 -#> Dataset 8 parent 48 1.6 0.43165 1.16835 1.886 0.619603 -#> Dataset 8 parent 48 1.5 0.43165 1.06835 1.886 0.566570 -#> Dataset 8 parent 70 1.1 0.05441 1.04559 1.886 0.554503 -#> Dataset 8 parent 70 0.9 0.05441 0.84559 1.886 0.448438 -#> Dataset 8 A1 1 9.6 7.66431 1.93569 1.886 1.026546 -#> Dataset 8 A1 1 7.7 7.66431 0.03569 1.886 0.018930 -#> Dataset 8 A1 3 15.0 15.57948 -0.57948 1.886 -0.307311 -#> Dataset 8 A1 3 15.1 15.57948 -0.47948 1.886 -0.254279 -#> Dataset 8 A1 8 21.2 20.38988 0.81012 1.886 0.429625 -#> Dataset 8 A1 8 21.1 20.38988 0.71012 1.886 0.376593 -#> Dataset 8 A1 14 19.7 20.16439 -0.46439 1.886 -0.246276 -#> Dataset 8 A1 14 18.9 20.16439 -1.26439 1.886 -0.670535 -#> Dataset 8 A1 27 17.5 16.40918 1.09082 1.886 0.578489 -#> Dataset 8 A1 27 15.9 16.40918 -0.50918 1.886 -0.270030 -#> Dataset 8 A1 48 9.5 10.12011 -0.62011 1.886 -0.328861 -#> Dataset 8 A1 48 9.8 10.12011 -0.32011 1.886 -0.169764 -#> Dataset 8 A1 70 6.2 5.79080 0.40920 1.886 0.217011 -#> Dataset 8 A1 70 6.1 5.79080 0.30920 1.886 0.163979 -#> Dataset 9 parent 0 99.8 97.38786 2.41214 1.886 1.279218 -#> Dataset 9 parent 0 98.3 97.38786 0.91214 1.886 0.483731 -#> Dataset 9 parent 1 77.1 79.25431 -2.15431 1.886 -1.142481 -#> Dataset 9 parent 1 77.2 79.25431 -2.05431 1.886 -1.089449 -#> Dataset 9 parent 3 59.0 55.69866 3.30134 1.886 1.750781 -#> Dataset 9 parent 3 58.1 55.69866 2.40134 1.886 1.273489 -#> Dataset 9 parent 8 27.4 31.64893 -4.24893 1.886 -2.253314 -#> Dataset 9 parent 8 29.2 31.64893 -2.44893 1.886 -1.298729 -#> Dataset 9 parent 14 19.1 22.57316 -3.47316 1.886 -1.841901 -#> Dataset 9 parent 14 29.6 22.57316 7.02684 1.886 3.726507 -#> Dataset 9 parent 27 10.1 14.11345 -4.01345 1.886 -2.128430 -#> Dataset 9 parent 27 18.2 14.11345 4.08655 1.886 2.167199 -#> Dataset 9 parent 48 4.5 6.95586 -2.45586 1.886 -1.302400 -#> Dataset 9 parent 48 9.1 6.95586 2.14414 1.886 1.137093 -#> Dataset 9 parent 70 2.3 3.31753 -1.01753 1.886 -0.539619 -#> Dataset 9 parent 70 2.9 3.31753 -0.41753 1.886 -0.221424 -#> Dataset 9 parent 91 2.0 1.63642 0.36358 1.886 0.192816 -#> Dataset 9 parent 91 1.8 1.63642 0.16358 1.886 0.086751 -#> Dataset 9 parent 120 2.0 0.61667 1.38333 1.886 0.733614 -#> Dataset 9 parent 120 2.2 0.61667 1.58333 1.886 0.839679 -#> Dataset 9 A1 1 4.2 3.67247 0.52753 1.886 0.279763 -#> Dataset 9 A1 1 3.9 3.67247 0.22753 1.886 0.120666 -#> Dataset 9 A1 3 7.4 8.36240 -0.96240 1.886 -0.510385 -#> Dataset 9 A1 3 7.9 8.36240 -0.46240 1.886 -0.245223 -#> Dataset 9 A1 8 14.5 12.80590 1.69410 1.886 0.898422 -#> Dataset 9 A1 8 13.7 12.80590 0.89410 1.886 0.474162 -#> Dataset 9 A1 14 14.2 13.99625 0.20375 1.886 0.108053 -#> Dataset 9 A1 14 12.2 13.99625 -1.79625 1.886 -0.952596 -#> Dataset 9 A1 27 13.7 14.22730 -0.52730 1.886 -0.279641 -#> Dataset 9 A1 27 13.2 14.22730 -1.02730 1.886 -0.544803 -#> Dataset 9 A1 48 13.6 13.33713 0.26287 1.886 0.139406 -#> Dataset 9 A1 48 15.4 13.33713 2.06287 1.886 1.093991 -#> Dataset 9 A1 70 10.4 11.84008 -1.44008 1.886 -0.763708 -#> Dataset 9 A1 70 11.6 11.84008 -0.24008 1.886 -0.127318 -#> Dataset 9 A1 91 10.0 10.30732 -0.30732 1.886 -0.162980 -#> Dataset 9 A1 91 9.5 10.30732 -0.80732 1.886 -0.428142 -#> Dataset 9 A1 120 9.1 8.33981 0.76019 1.886 0.403149 -#> Dataset 9 A1 120 9.0 8.33981 0.66019 1.886 0.350117 -#> Dataset 10 parent 0 96.1 93.70349 2.39651 1.886 1.270926 -#> Dataset 10 parent 0 94.3 93.70349 0.59651 1.886 0.316342 -#> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429 -#> Dataset 10 parent 8 73.9 77.86253 -3.96253 1.886 -2.101429 -#> Dataset 10 parent 14 69.4 70.18665 -0.78665 1.886 -0.417182 -#> Dataset 10 parent 14 73.1 70.18665 2.91335 1.886 1.545019 -#> Dataset 10 parent 21 65.6 64.03245 1.56755 1.886 0.831308 -#> Dataset 10 parent 21 65.3 64.03245 1.26755 1.886 0.672210 -#> Dataset 10 parent 41 55.9 54.71491 1.18509 1.886 0.628480 -#> Dataset 10 parent 41 54.4 54.71491 -0.31491 1.886 -0.167007 -#> Dataset 10 parent 63 47.0 49.63436 -2.63436 1.886 -1.397065 -#> Dataset 10 parent 63 49.3 49.63436 -0.33436 1.886 -0.177319 -#> Dataset 10 parent 91 44.7 45.08853 -0.38853 1.886 -0.206049 -#> Dataset 10 parent 91 46.7 45.08853 1.61147 1.886 0.854600 -#> Dataset 10 parent 120 42.1 41.07653 1.02347 1.886 0.542772 -#> Dataset 10 parent 120 41.3 41.07653 0.22347 1.886 0.118513 -#> Dataset 10 A1 8 3.3 4.08295 -0.78295 1.886 -0.415218 -#> Dataset 10 A1 8 3.4 4.08295 -0.68295 1.886 -0.362186 -#> Dataset 10 A1 14 3.9 6.04367 -2.14367 1.886 -1.136841 -#> Dataset 10 A1 14 2.9 6.04367 -3.14367 1.886 -1.667165 -#> Dataset 10 A1 21 6.4 7.59693 -1.19693 1.886 -0.634761 -#> Dataset 10 A1 21 7.2 7.59693 -0.39693 1.886 -0.210502 -#> Dataset 10 A1 41 9.1 9.86436 -0.76436 1.886 -0.405361 -#> Dataset 10 A1 41 8.5 9.86436 -1.36436 1.886 -0.723555 -#> Dataset 10 A1 63 11.7 10.99397 0.70603 1.886 0.374425 -#> Dataset 10 A1 63 12.0 10.99397 1.00603 1.886 0.533522 -#> Dataset 10 A1 91 13.3 11.91274 1.38726 1.886 0.735696 -#> Dataset 10 A1 91 13.2 11.91274 1.28726 1.886 0.682663 -#> Dataset 10 A1 120 14.3 12.66519 1.63481 1.886 0.866981 -#> Dataset 10 A1 120 12.1 12.66519 -0.56519 1.886 -0.299733 +#> Dataset 6 parent 0 97.2 95.70025 1.49975 1.882 0.79693 +#> Dataset 6 parent 0 96.4 95.70025 0.69975 1.882 0.37183 +#> Dataset 6 parent 3 71.1 71.44670 -0.34670 1.882 -0.18423 +#> Dataset 6 parent 3 69.2 71.44670 -2.24670 1.882 -1.19384 +#> Dataset 6 parent 6 58.1 56.59283 1.50717 1.882 0.80087 +#> Dataset 6 parent 6 56.6 56.59283 0.00717 1.882 0.00381 +#> Dataset 6 parent 10 44.4 44.56648 -0.16648 1.882 -0.08847 +#> Dataset 6 parent 10 43.4 44.56648 -1.16648 1.882 -0.61984 +#> Dataset 6 parent 20 33.3 29.76020 3.53980 1.882 1.88096 +#> Dataset 6 parent 20 29.2 29.76020 -0.56020 1.882 -0.29767 +#> Dataset 6 parent 34 17.6 19.39208 -1.79208 1.882 -0.95226 +#> Dataset 6 parent 34 18.0 19.39208 -1.39208 1.882 -0.73971 +#> Dataset 6 parent 55 10.5 10.55761 -0.05761 1.882 -0.03061 +#> Dataset 6 parent 55 9.3 10.55761 -1.25761 1.882 -0.66826 +#> Dataset 6 parent 90 4.5 3.84742 0.65258 1.882 0.34676 +#> Dataset 6 parent 90 4.7 3.84742 0.85258 1.882 0.45304 +#> Dataset 6 parent 112 3.0 2.03997 0.96003 1.882 0.51013 +#> Dataset 6 parent 112 3.4 2.03997 1.36003 1.882 0.72268 +#> Dataset 6 parent 132 2.3 1.14585 1.15415 1.882 0.61328 +#> Dataset 6 parent 132 2.7 1.14585 1.55415 1.882 0.82583 +#> Dataset 6 A1 3 4.3 4.86054 -0.56054 1.882 -0.29786 +#> Dataset 6 A1 3 4.6 4.86054 -0.26054 1.882 -0.13844 +#> Dataset 6 A1 6 7.0 7.74179 -0.74179 1.882 -0.39417 +#> Dataset 6 A1 6 7.2 7.74179 -0.54179 1.882 -0.28789 +#> Dataset 6 A1 10 8.2 9.94048 -1.74048 1.882 -0.92485 +#> Dataset 6 A1 10 8.0 9.94048 -1.94048 1.882 -1.03112 +#> Dataset 6 A1 20 11.0 12.19109 -1.19109 1.882 -0.63291 +#> Dataset 6 A1 20 13.7 12.19109 1.50891 1.882 0.80180 +#> Dataset 6 A1 34 11.5 13.10706 -1.60706 1.882 -0.85395 +#> Dataset 6 A1 34 12.7 13.10706 -0.40706 1.882 -0.21630 +#> Dataset 6 A1 55 14.9 13.06131 1.83869 1.882 0.97703 +#> Dataset 6 A1 55 14.5 13.06131 1.43869 1.882 0.76448 +#> Dataset 6 A1 90 12.1 11.54495 0.55505 1.882 0.29494 +#> Dataset 6 A1 90 12.3 11.54495 0.75505 1.882 0.40122 +#> Dataset 6 A1 112 9.9 10.31533 -0.41533 1.882 -0.22070 +#> Dataset 6 A1 112 10.2 10.31533 -0.11533 1.882 -0.06128 +#> Dataset 6 A1 132 8.8 9.20222 -0.40222 1.882 -0.21373 +#> Dataset 6 A1 132 7.8 9.20222 -1.40222 1.882 -0.74510 +#> Dataset 7 parent 0 93.6 90.82357 2.77643 1.882 1.47532 +#> Dataset 7 parent 0 92.3 90.82357 1.47643 1.882 0.78453 +#> Dataset 7 parent 3 87.0 84.73448 2.26552 1.882 1.20384 +#> Dataset 7 parent 3 82.2 84.73448 -2.53448 1.882 -1.34675 +#> Dataset 7 parent 7 74.0 77.65013 -3.65013 1.882 -1.93958 +#> Dataset 7 parent 7 73.9 77.65013 -3.75013 1.882 -1.99272 +#> Dataset 7 parent 14 64.2 67.60639 -3.40639 1.882 -1.81007 +#> Dataset 7 parent 14 69.5 67.60639 1.89361 1.882 1.00621 +#> Dataset 7 parent 30 54.0 52.53663 1.46337 1.882 0.77760 +#> Dataset 7 parent 30 54.6 52.53663 2.06337 1.882 1.09642 +#> Dataset 7 parent 60 41.1 39.42728 1.67272 1.882 0.88884 +#> Dataset 7 parent 60 38.4 39.42728 -1.02728 1.882 -0.54587 +#> Dataset 7 parent 90 32.5 33.76360 -1.26360 1.882 -0.67144 +#> Dataset 7 parent 90 35.5 33.76360 1.73640 1.882 0.92268 +#> Dataset 7 parent 120 28.1 30.39975 -2.29975 1.882 -1.22203 +#> Dataset 7 parent 120 29.0 30.39975 -1.39975 1.882 -0.74379 +#> Dataset 7 parent 180 26.5 25.62379 0.87621 1.882 0.46559 +#> Dataset 7 parent 180 27.6 25.62379 1.97621 1.882 1.05010 +#> Dataset 7 A1 3 3.9 2.70005 1.19995 1.882 0.63762 +#> Dataset 7 A1 3 3.1 2.70005 0.39995 1.882 0.21252 +#> Dataset 7 A1 7 6.9 5.83475 1.06525 1.882 0.56605 +#> Dataset 7 A1 7 6.6 5.83475 0.76525 1.882 0.40663 +#> Dataset 7 A1 14 10.4 10.26142 0.13858 1.882 0.07364 +#> Dataset 7 A1 14 8.3 10.26142 -1.96142 1.882 -1.04225 +#> Dataset 7 A1 30 14.4 16.82999 -2.42999 1.882 -1.29123 +#> Dataset 7 A1 30 13.7 16.82999 -3.12999 1.882 -1.66319 +#> Dataset 7 A1 60 22.1 22.32486 -0.22486 1.882 -0.11949 +#> Dataset 7 A1 60 22.3 22.32486 -0.02486 1.882 -0.01321 +#> Dataset 7 A1 90 27.5 24.45927 3.04073 1.882 1.61576 +#> Dataset 7 A1 90 25.4 24.45927 0.94073 1.882 0.49988 +#> Dataset 7 A1 120 28.0 25.54862 2.45138 1.882 1.30260 +#> Dataset 7 A1 120 26.6 25.54862 1.05138 1.882 0.55868 +#> Dataset 7 A1 180 25.8 26.82277 -1.02277 1.882 -0.54347 +#> Dataset 7 A1 180 25.3 26.82277 -1.52277 1.882 -0.80916 +#> Dataset 8 parent 0 91.9 91.16791 0.73209 1.882 0.38901 +#> Dataset 8 parent 0 90.8 91.16791 -0.36791 1.882 -0.19550 +#> Dataset 8 parent 1 64.9 67.58358 -2.68358 1.882 -1.42598 +#> Dataset 8 parent 1 66.2 67.58358 -1.38358 1.882 -0.73520 +#> Dataset 8 parent 3 43.5 41.62086 1.87914 1.882 0.99853 +#> Dataset 8 parent 3 44.1 41.62086 2.47914 1.882 1.31735 +#> Dataset 8 parent 8 18.3 19.60116 -1.30116 1.882 -0.69140 +#> Dataset 8 parent 8 18.1 19.60116 -1.50116 1.882 -0.79768 +#> Dataset 8 parent 14 10.2 10.63101 -0.43101 1.882 -0.22903 +#> Dataset 8 parent 14 10.8 10.63101 0.16899 1.882 0.08980 +#> Dataset 8 parent 27 4.9 3.12435 1.77565 1.882 0.94354 +#> Dataset 8 parent 27 3.3 3.12435 0.17565 1.882 0.09334 +#> Dataset 8 parent 48 1.6 0.43578 1.16422 1.882 0.61864 +#> Dataset 8 parent 48 1.5 0.43578 1.06422 1.882 0.56550 +#> Dataset 8 parent 70 1.1 0.05534 1.04466 1.882 0.55510 +#> Dataset 8 parent 70 0.9 0.05534 0.84466 1.882 0.44883 +#> Dataset 8 A1 1 9.6 7.63450 1.96550 1.882 1.04442 +#> Dataset 8 A1 1 7.7 7.63450 0.06550 1.882 0.03481 +#> Dataset 8 A1 3 15.0 15.52593 -0.52593 1.882 -0.27947 +#> Dataset 8 A1 3 15.1 15.52593 -0.42593 1.882 -0.22633 +#> Dataset 8 A1 8 21.2 20.32192 0.87808 1.882 0.46659 +#> Dataset 8 A1 8 21.1 20.32192 0.77808 1.882 0.41345 +#> Dataset 8 A1 14 19.7 20.09721 -0.39721 1.882 -0.21107 +#> Dataset 8 A1 14 18.9 20.09721 -1.19721 1.882 -0.63617 +#> Dataset 8 A1 27 17.5 16.37477 1.12523 1.882 0.59792 +#> Dataset 8 A1 27 15.9 16.37477 -0.47477 1.882 -0.25228 +#> Dataset 8 A1 48 9.5 10.13141 -0.63141 1.882 -0.33551 +#> Dataset 8 A1 48 9.8 10.13141 -0.33141 1.882 -0.17610 +#> Dataset 8 A1 70 6.2 5.81827 0.38173 1.882 0.20284 +#> Dataset 8 A1 70 6.1 5.81827 0.28173 1.882 0.14970 +#> Dataset 9 parent 0 99.8 97.48728 2.31272 1.882 1.22892 +#> Dataset 9 parent 0 98.3 97.48728 0.81272 1.882 0.43186 +#> Dataset 9 parent 1 77.1 79.29476 -2.19476 1.882 -1.16624 +#> Dataset 9 parent 1 77.2 79.29476 -2.09476 1.882 -1.11310 +#> Dataset 9 parent 3 59.0 55.67060 3.32940 1.882 1.76915 +#> Dataset 9 parent 3 58.1 55.67060 2.42940 1.882 1.29092 +#> Dataset 9 parent 8 27.4 31.57871 -4.17871 1.882 -2.22046 +#> Dataset 9 parent 8 29.2 31.57871 -2.37871 1.882 -1.26398 +#> Dataset 9 parent 14 19.1 22.51546 -3.41546 1.882 -1.81489 +#> Dataset 9 parent 14 29.6 22.51546 7.08454 1.882 3.76454 +#> Dataset 9 parent 27 10.1 14.09074 -3.99074 1.882 -2.12057 +#> Dataset 9 parent 27 18.2 14.09074 4.10926 1.882 2.18355 +#> Dataset 9 parent 48 4.5 6.95747 -2.45747 1.882 -1.30584 +#> Dataset 9 parent 48 9.1 6.95747 2.14253 1.882 1.13848 +#> Dataset 9 parent 70 2.3 3.32472 -1.02472 1.882 -0.54451 +#> Dataset 9 parent 70 2.9 3.32472 -0.42472 1.882 -0.22569 +#> Dataset 9 parent 91 2.0 1.64300 0.35700 1.882 0.18970 +#> Dataset 9 parent 91 1.8 1.64300 0.15700 1.882 0.08343 +#> Dataset 9 parent 120 2.0 0.62073 1.37927 1.882 0.73291 +#> Dataset 9 parent 120 2.2 0.62073 1.57927 1.882 0.83918 +#> Dataset 9 A1 1 4.2 3.64568 0.55432 1.882 0.29455 +#> Dataset 9 A1 1 3.9 3.64568 0.25432 1.882 0.13514 +#> Dataset 9 A1 3 7.4 8.30173 -0.90173 1.882 -0.47916 +#> Dataset 9 A1 3 7.9 8.30173 -0.40173 1.882 -0.21347 +#> Dataset 9 A1 8 14.5 12.71589 1.78411 1.882 0.94803 +#> Dataset 9 A1 8 13.7 12.71589 0.98411 1.882 0.52293 +#> Dataset 9 A1 14 14.2 13.90452 0.29548 1.882 0.15701 +#> Dataset 9 A1 14 12.2 13.90452 -1.70452 1.882 -0.90574 +#> Dataset 9 A1 27 13.7 14.15523 -0.45523 1.882 -0.24190 +#> Dataset 9 A1 27 13.2 14.15523 -0.95523 1.882 -0.50759 +#> Dataset 9 A1 48 13.6 13.31038 0.28962 1.882 0.15389 +#> Dataset 9 A1 48 15.4 13.31038 2.08962 1.882 1.11037 +#> Dataset 9 A1 70 10.4 11.85965 -1.45965 1.882 -0.77562 +#> Dataset 9 A1 70 11.6 11.85965 -0.25965 1.882 -0.13797 +#> Dataset 9 A1 91 10.0 10.36294 -0.36294 1.882 -0.19286 +#> Dataset 9 A1 91 9.5 10.36294 -0.86294 1.882 -0.45855 +#> Dataset 9 A1 120 9.1 8.43003 0.66997 1.882 0.35601 +#> Dataset 9 A1 120 9.0 8.43003 0.56997 1.882 0.30287 +#> Dataset 10 parent 0 96.1 93.95603 2.14397 1.882 1.13925 +#> Dataset 10 parent 0 94.3 93.95603 0.34397 1.882 0.18278 +#> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237 +#> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237 +#> Dataset 10 parent 14 69.4 70.04570 -0.64570 1.882 -0.34311 +#> Dataset 10 parent 14 73.1 70.04570 3.05430 1.882 1.62298 +#> Dataset 10 parent 21 65.6 64.01710 1.58290 1.882 0.84111 +#> Dataset 10 parent 21 65.3 64.01710 1.28290 1.882 0.68170 +#> Dataset 10 parent 41 55.9 54.98434 0.91566 1.882 0.48656 +#> Dataset 10 parent 41 54.4 54.98434 -0.58434 1.882 -0.31050 +#> Dataset 10 parent 63 47.0 49.87137 -2.87137 1.882 -1.52577 +#> Dataset 10 parent 63 49.3 49.87137 -0.57137 1.882 -0.30361 +#> Dataset 10 parent 91 44.7 45.06727 -0.36727 1.882 -0.19516 +#> Dataset 10 parent 91 46.7 45.06727 1.63273 1.882 0.86759 +#> Dataset 10 parent 120 42.1 40.76402 1.33598 1.882 0.70991 +#> Dataset 10 parent 120 41.3 40.76402 0.53598 1.882 0.28481 +#> Dataset 10 A1 8 3.3 4.14599 -0.84599 1.882 -0.44954 +#> Dataset 10 A1 8 3.4 4.14599 -0.74599 1.882 -0.39640 +#> Dataset 10 A1 14 3.9 6.08478 -2.18478 1.882 -1.16093 +#> Dataset 10 A1 14 2.9 6.08478 -3.18478 1.882 -1.69231 +#> Dataset 10 A1 21 6.4 7.59411 -1.19411 1.882 -0.63452 +#> Dataset 10 A1 21 7.2 7.59411 -0.39411 1.882 -0.20942 +#> Dataset 10 A1 41 9.1 9.78292 -0.68292 1.882 -0.36289 +#> Dataset 10 A1 41 8.5 9.78292 -1.28292 1.882 -0.68171 +#> Dataset 10 A1 63 11.7 10.93274 0.76726 1.882 0.40770 +#> Dataset 10 A1 63 12.0 10.93274 1.06726 1.882 0.56711 +#> Dataset 10 A1 91 13.3 11.93986 1.36014 1.882 0.72274 +#> Dataset 10 A1 91 13.2 11.93986 1.26014 1.882 0.66961 +#> Dataset 10 A1 120 14.3 12.79238 1.50762 1.882 0.80111 +#> Dataset 10 A1 120 12.1 12.79238 -0.69238 1.882 -0.36791 # The following takes about 6 minutes #f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve", diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html index 993544c7..3994a424 100644 --- a/docs/dev/reference/summary.mkinfit.html +++ b/docs/dev/reference/summary.mkinfit.html @@ -108,7 +108,7 @@ values.

    Arguments

    object
    -

    an object of class mkinfit.

    +

    an object of class mkinfit.

    data
    @@ -189,7 +189,8 @@ model.

    The DT50 and DT90 values for each observed variable.

    SFORB
    -

    If applicable, eigenvalues of SFORB components of the model.

    +

    If applicable, eigenvalues and fractional eigenvector component +g of SFORB systems in the model.

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

    @@ -212,8 +213,8 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, summary(mkinfit("SFO", FOCUS_2006_A, quiet = TRUE)) #> mkin version used for fitting: 1.2.0 #> R version used for fitting: 4.2.2 -#> Date of fit: Tue Nov 1 14:12:09 2022 -#> Date of summary: Tue Nov 1 14:12:09 2022 +#> Date of fit: Mon Nov 14 21:06:31 2022 +#> Date of summary: Mon Nov 14 21:06:31 2022 #> #> Equations: #> d_parent/dt = - k_parent * parent -- cgit v1.2.1