aboutsummaryrefslogtreecommitdiff
path: root/man
diff options
context:
space:
mode:
Diffstat (limited to 'man')
-rw-r--r--man/mkinerrplot.Rd2
-rw-r--r--man/mkinfit.Rd40
-rw-r--r--man/plot.mkinfit.Rd2
3 files changed, 44 insertions, 0 deletions
diff --git a/man/mkinerrplot.Rd b/man/mkinerrplot.Rd
index 4cbb5eb7..3b557b0a 100644
--- a/man/mkinerrplot.Rd
+++ b/man/mkinerrplot.Rd
@@ -68,8 +68,10 @@
\code{\link{mkinplot}}, for a way to plot the data and the fitted lines of the
mkinfit object. }
\examples{
+\dontrun{
model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
fit <- mkinfit(model, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
mkinerrplot(fit)
}
+}
\keyword{ hplot }
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index 78a53ee0..975eace8 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -31,6 +31,8 @@ mkinfit(mkinmod, observed,
quiet = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
error_model = c("const", "obs", "tc"),
+ error_model_algorithm = c("d_3", "direct", "twostep", "threestep", "fourstep", "IRLS"),
+ reweight.tol = 1e-8, reweight.max.iter = 10,
trace_parms = FALSE, ...)
}
\arguments{
@@ -171,6 +173,44 @@ mkinfit(mkinmod, observed,
errors follow a lognormal distribution for large values, not a normal
distribution as assumed by this method.
}
+ \item{error_model_algorithm}{
+ If the error model is "const", the error model algorithm is ignored,
+ because no special algorithm is needed and unweighted (also known as
+ ordinary) least squares fitting can be applied.
+
+ The default algorithm "d_3" will directly minimize the negative
+ log-likelihood and - independently - also use the three step algorithm
+ described below. The fit with the higher likelihood is returned.
+
+ The algorithm "direct" will directly minimize the negative
+ log-likelihood.
+
+ The algorithm "twostep" will minimize the negative log-likelihood
+ after an initial unweighted leas squares optimisation step.
+
+ The algorithm "threestep" starts with unweighted least squares,
+ then optimizes only the error model using the degradation model
+ parameters found, and then minimizes the negative log-likelihood
+ with free degradation and error model parameters.
+
+ The algorithm "fourstep" starts with unweighted least squares,
+ then optimizes only the error model using the degradation model
+ parameters found, then optimizes the degradation model again
+ with fixed error model parameters, and finally minimizes the negative
+ log-likelihood with free degradation and error model parameters.
+
+ The algorithm "IRLS" starts with unweighted least squares,
+ and then iterates optimization of the error model parameters and subsequent
+ optimization of the degradation model using those error model parameters,
+ until the error model parameters converge.
+ }
+ \item{reweight.tol}{
+ Tolerance for the convergence criterion calculated from the error model
+ parameters in IRLS fits.
+ }
+ \item{reweight.max.iter}{
+ Maximum number of iterations in IRLS fits.
+ }
\item{trace_parms}{
Should a trace of the parameter values be listed?
}
diff --git a/man/plot.mkinfit.Rd b/man/plot.mkinfit.Rd
index 9514c5e5..5e20ad90 100644
--- a/man/plot.mkinfit.Rd
+++ b/man/plot.mkinfit.Rd
@@ -115,6 +115,7 @@ plot_sep(fit, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE, \dots)
\examples{
# One parent compound, one metabolite, both single first order, path from
# parent to sink included
+\dontrun{
SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
m1 = mkinsub("SFO", full = "Metabolite M1" ))
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
@@ -136,6 +137,7 @@ plot_sep(fit, lpos = c("topright", "bottomright"))
plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),
show_errmin = TRUE)
}
+}
\author{
Johannes Ranke
}

Contact - Imprint