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Diffstat (limited to 'man')
| -rw-r--r-- | man/mkinfit.Rd | 42 |
1 files changed, 42 insertions, 0 deletions
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd index 319b1b3..445bce2 100644 --- a/man/mkinfit.Rd +++ b/man/mkinfit.Rd @@ -15,6 +15,8 @@ mkinfit(mkinmod, observed, state.ini = c(100, rep(0, length(mkinmod$diffs) - 1)), fixed_parms = NULL, fixed_initials = names(mkinmod$diffs)[-1], solution_type = "auto", + method.modFit = "Marq", + control.modFit = list(), plot = FALSE, quiet = FALSE, err = NULL, weight = "none", scaleVar = FALSE, atol = 1e-8, rtol = 1e-10, n.outtimes = 100, @@ -38,6 +40,13 @@ mkinfit(mkinmod, observed, A named vector of initial values for the parameters, including parameters to be optimised and potentially also fixed parameters as indicated by \code{fixed_parms}. If set to "auto", initial values for rate constants are set to default values. + Using parameter names that are not in the model gives an error. + + It is possible to only specify a subset of the parameters that the model + needs. You can use the parameter lists "bparms.ode" from a previously + fitted model, which contains the differential equation parameters from this + model. This works nicely if the models are nested. An example is given + below. } \item{state.ini}{ A named vector of initial values for the state variables of the model. In case the @@ -65,6 +74,15 @@ mkinfit(mkinmod, observed, using eigenvalues and eigenvectors, and finally "deSolve" for the remaining models (time dependence of degradation rates and metabolites). } + \item{method.modFit}{ + The optimisation method passed to \code{\link{modFit}}. The default "Marq" is the Levenberg Marquardt + algorithm \code{\link{nls.lm}} from the package \code{minpack.lm}. Often other methods need + more iterations to find the same result. When using "Pseudo", "upper" and "lower" need to be + specified for the transformed parameters. + } + \item{control.modFit}{ + Additional arguments passed to the optimisation method used by \code{\link{modFit}}. + } \item{plot}{ Should the observed values and the numerical solutions be plotted at each stage of the optimisation? @@ -120,6 +138,30 @@ SFO_SFO <- mkinmod( fit <- mkinfit(SFO_SFO, FOCUS_2006_D) str(fit) summary(fit) + +# Use stepwise fitting, using optimised parameters from parent only fit, FOMC +\dontrun{ +FOMC <- mkinmod(parent = list(type = "FOMC")) +FOMC_SFO <- mkinmod( + parent = list(type = "FOMC", to = "m1", sink = TRUE), + m1 = list(type = "SFO")) +# Fit the model to the FOCUS example dataset D using defaults +fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D) +# Use starting parameters from parent only FOMC fit (not really needed in this case) +fit.FOMC = mkinfit(FOMC, FOCUS_2006_D) +fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, parms.ini = fit.FOMC$bparms.ode, plot=TRUE) +} + +# Use stepwise fitting, using optimised parameters from parent only fit, SFORB +SFORB <- mkinmod(parent = list(type = "SFORB")) +SFORB_SFO <- mkinmod( + parent = list(type = "SFORB", to = "m1", sink = TRUE), + m1 = list(type = "SFO")) +# Fit the model to the FOCUS example dataset D using defaults +fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D) +# Use starting parameters from parent only SFORB fit (not really needed in this case) +fit.SFORB = mkinfit(SFORB, FOCUS_2006_D) +fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, plot=TRUE) } \keyword{ models } \keyword{ optimize } |