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-rw-r--r--man/mkinds.Rd2
-rw-r--r--man/mkinfit.Rd8
-rw-r--r--man/mkinpredict.Rd108
3 files changed, 63 insertions, 55 deletions
diff --git a/man/mkinds.Rd b/man/mkinds.Rd
index 3bbb1c4b..8d1860e8 100644
--- a/man/mkinds.Rd
+++ b/man/mkinds.Rd
@@ -47,6 +47,7 @@ and value in order to be compatible with mkinfit}
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-new"></a>}}
+\if{latex}{\out{\hypertarget{method-new}{}}}
\subsection{Method \code{new()}}{
Create a new mkinds object
\subsection{Usage}{
@@ -69,6 +70,7 @@ Create a new mkinds object
}
\if{html}{\out{<hr>}}
\if{html}{\out{<a id="method-clone"></a>}}
+\if{latex}{\out{\hypertarget{method-clone}{}}}
\subsection{Method \code{clone()}}{
The objects of this class are cloneable with this method.
\subsection{Usage}{
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index 45036361..89d2ddd6 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -28,8 +28,8 @@ mkinfit(
rtol = 1e-10,
n.outtimes = 100,
error_model = c("const", "obs", "tc"),
- error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep", "fourstep",
- "IRLS", "OLS"),
+ error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep",
+ "fourstep", "IRLS", "OLS"),
reweight.tol = 1e-08,
reweight.max.iter = 10,
trace_parms = FALSE,
@@ -238,13 +238,13 @@ SFO_SFO <- mkinmod(
# Fit the model to the FOCUS example dataset D using defaults
print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D,
solution_type = "eigen", quiet = TRUE)))
-coef(fit)
+parms(fit)
endpoints(fit)
\dontrun{
# deSolve is slower when no C compiler (gcc) was available during model generation
print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D,
solution_type = "deSolve")))
-coef(fit.deSolve)
+parms(fit.deSolve)
endpoints(fit.deSolve)
}
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd
index 17d7ef21..366d5b83 100644
--- a/man/mkinpredict.Rd
+++ b/man/mkinpredict.Rd
@@ -100,57 +100,63 @@ kinetic parameters and initial values for the state variables.
}
\examples{
- SFO <- mkinmod(degradinol = mkinsub("SFO"))
- # Compare solution types
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- solution_type = "analytical")
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- solution_type = "deSolve")
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- solution_type = "deSolve", use_compiled = FALSE)
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- solution_type = "eigen")
-
-
- # Compare integration methods to analytical solution
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- solution_type = "analytical")[21,]
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- method = "lsoda")[21,]
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- method = "ode45")[21,]
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
- method = "rk4")[21,]
- # 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_sink = 0.3), c(degradinol = 100),
- seq(0, 20, by = 0.1))[201,]
- mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
- seq(0, 20, by = 0.01))[2001,]
-
- # Check compiled model versions - they are faster than the eigenvalue based solutions!
- SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
- m1 = list(type = "SFO"))
- system.time(
- print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
- c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
- solution_type = "eigen")[201,]))
- system.time(
- print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
- c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
- solution_type = "deSolve")[201,]))
- system.time(
- print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
- c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
- solution_type = "deSolve", use_compiled = FALSE)[201,]))
-
- \dontrun{
- # Predict from a fitted model
- f <- mkinfit(SFO_SFO, FOCUS_2006_C)
- head(mkinpredict(f))
- }
+SFO <- mkinmod(degradinol = mkinsub("SFO"))
+# Compare solution types
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = "analytical")
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = "deSolve")
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = "deSolve", use_compiled = FALSE)
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = "eigen")
+
+# Compare integration methods to analytical solution
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ solution_type = "analytical")[21,]
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = "lsoda")[21,]
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = "ode45")[21,]
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100), 0:20,
+ method = "rk4")[21,]
+# 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_sink = 0.3), c(degradinol = 100),
+ seq(0, 20, by = 0.1))[201,]
+mkinpredict(SFO, c(k_degradinol_sink = 0.3), c(degradinol = 100),
+ seq(0, 20, by = 0.01))[2001,]
+
+# Check compiled model versions - they are faster than the eigenvalue based solutions!
+SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
+ m1 = list(type = "SFO"))
+if(require(rbenchmark)) {
+ benchmark(
+ eigen = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = "eigen")[201,],
+ deSolve_compiled = mkinpredict(SFO_SFO,
+ c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = "deSolve")[201,],
+ deSolve = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+ solution_type = "deSolve", use_compiled = FALSE)[201,],
+ replications = 10)
+}
+
+# Since mkin 0.9.49.11 we also have analytical solutions for some models, including SFO-SFO
+# deSolve = mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
+# c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+# solution_type = "analytical", use_compiled = FALSE)[201,],
+
+\dontrun{
+ # Predict from a fitted model
+ f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
+ head(mkinpredict(f))
+}
}
\author{

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