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-rw-r--r--man/mkinpredict.Rd12
-rw-r--r--man/mmkin.Rd7
2 files changed, 8 insertions, 11 deletions
diff --git a/man/mkinpredict.Rd b/man/mkinpredict.Rd
index d93c0753..ad749fa4 100644
--- a/man/mkinpredict.Rd
+++ b/man/mkinpredict.Rd
@@ -21,7 +21,6 @@ mkinpredict(x, odeparms, odeini, outtimes, ...)
maxsteps = 20000L,
map_output = TRUE,
na_stop = TRUE,
- call_lsoda = NULL,
...
)
@@ -69,7 +68,8 @@ parent compound.}
\link{mkinmod} model is used, even if is present.}
\item{method.ode}{The solution method passed via \link{mkinpredict} to \link{ode}] in
-case the solution type is "deSolve" and we are not using compiled code.}
+case the solution type is "deSolve" and we are not using compiled code.
+When using compiled code, only lsoda is supported.}
\item{atol}{Absolute error tolerance, passed to the ode solver.}
@@ -83,8 +83,6 @@ FALSE). Setting this to FALSE has no effect for analytical solutions,
as these always return mapped output.}
\item{na_stop}{Should it be an error if \link{ode} returns NaN values}
-
-\item{call_lsoda}{The address of the compiled function "call_lsoda"}
}
\value{
A matrix with the numeric solution in wide format
@@ -111,11 +109,11 @@ mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
solution_type = "analytical")[21,]
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
- method = "lsoda")[21,]
+ method = "lsoda", use_compiled = FALSE)[21,]
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
- method = "ode45")[21,]
+ method = "ode45", use_compiled = FALSE)[21,]
mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
- method = "rk4")[21,]
+ method = "rk4", use_compiled = FALSE)[21,]
# rk4 is not as precise here
# The number of output times used to make a lot of difference until the
diff --git a/man/mmkin.Rd b/man/mmkin.Rd
index 309761dd..438a237b 100644
--- a/man/mmkin.Rd
+++ b/man/mmkin.Rd
@@ -83,16 +83,15 @@ plot_sep(fits.0[[1, 1]])
# allow to plot the observed variables separately
plot(fits.0[1, 1])
-# On Windows, we can use multiple cores by making a cluster using the parallel
-# package, which gets loaded with mkin, and passing it to mmkin, e.g.
-cl <- makePSOCKcluster(12)
+# On Windows, we can use multiple cores by making a cluster first
+cl <- parallel::makePSOCKcluster(12)
f <- mmkin(c("SFO", "FOMC", "DFOP"),
list(A = FOCUS_2006_A, B = FOCUS_2006_B, C = FOCUS_2006_C, D = FOCUS_2006_D),
cluster = cl, quiet = TRUE)
print(f)
# We get false convergence for the FOMC fit to FOCUS_2006_A because this
# dataset is really SFO, and the FOMC fit is overparameterised
-stopCluster(cl)
+parallel::stopCluster(cl)
}
}

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