From 98d684becd9495d370e1bcc8f9c7ca758caa4dd8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2016 22:58:28 +0100 Subject: Static documentation rebuilt by pkgdown::build_site() --- docs/reference/mkinfit.html | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) (limited to 'docs/reference/mkinfit.html') diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index 32083843..891bc18b 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -82,11 +82,11 @@

This function uses the Flexible Modelling Environment package - FME to create a function calculating the model cost, i.e. the + FME to create a function calculating the model cost, i.e. the deviation between the kinetic model and the observed data. This model cost is - then minimised using the Port algorithm nlminb, + then minimised using the Port algorithm nlminb, using the specified initial or fixed parameters and starting values. - Per default, parameters in the kinetic models are internally transformed in order + Per default, parameters in the kinetic models are internally transformed in order to better satisfy the assumption of a normal distribution of their estimators. In each step of the optimsation, the kinetic model is solved using the function mkinpredict. The variance of the residuals for each @@ -200,13 +200,13 @@

use_compiled
- If set to FALSE, no compiled version of the mkinmod + If set to FALSE, no compiled version of the mkinmod model is used, in the calls to mkinpredict even if - a compiled verion is present. + a compiled verion is present.
method.modFit
- The optimisation method passed to modFit. + The optimisation method passed to modFit. In order to optimally deal with problems where local minima occur, the "Port" algorithm is now used per default as it is less prone to get trapped @@ -228,20 +228,20 @@
maxit.modFit
Maximum number of iterations in the optimisation. If not "auto", this will - be passed to the method called by modFit, overriding + be passed to the method called by modFit, overriding what may be specified in the next argument control.modFit.
control.modFit
Additional arguments passed to the optimisation method used by - modFit. + modFit.
transform_rates
Boolean specifying if kinetic rate constants should be transformed in the model specification used in the fitting for better compliance with the - assumption of normal distribution of the estimator. If TRUE, also - alpha and beta parameters of the FOMC model are log-transformed, as well + assumption of normal distribution of the estimator. If TRUE, also + alpha and beta parameters of the FOMC model are log-transformed, as well as k1 and k2 rate constants for the DFOP and HS models and the break point tb of the HS model. If FALSE, zero is used as a lower bound for the rates in the optimisation. @@ -250,7 +250,7 @@
Boolean specifying if formation fractions constants should be transformed in the model specification used in the fitting for better compliance with the - assumption of normal distribution of the estimator. The default (TRUE) is + assumption of normal distribution of the estimator. The default (TRUE) is to do transformations. If TRUE, the g parameter of the DFOP and HS models are also transformed, as they can also be seen as compositional data. The transformation used for these transformations is the @@ -294,7 +294,7 @@
The length of the dataseries that is produced by the model prediction function mkinpredict. This impacts the accuracy of - the numerical solver if that is used (see solution_type argument. + the numerical solver if that is used (see solution_type argument. The default value is 100.
reweight.method
@@ -302,9 +302,9 @@ The method used for iteratively reweighting residuals, also known as iteratively reweighted least squares (IRLS). Default is NULL, the other method implemented is called "obs", meaning that each - observed variable is assumed to have its own variance, this is + observed variable is assumed to have its own variance, this is estimated from the fit and used for weighting the residuals - in each iteration until convergence of this estimate up to + in each iteration until convergence of this estimate up to reweight.tol or up to the maximum number of iterations specified by reweight.max.iter. @@ -323,20 +323,20 @@
…
- Further arguments that will be passed to modFit. + Further arguments that will be passed to modFit.

Value

-

A list with "mkinfit" and "modFit" in the class attribute. +

A list with "mkinfit" and "modFit" in the class attribute. A summary can be obtained by summary.mkinfit.

See also

Plotting methods plot.mkinfit and mkinparplot.

-

Fitting of several models to several datasets in a single call to +

Fitting of several models to several datasets in a single call to mmkin.

Note

@@ -348,7 +348,7 @@

Note

-

When using the "IORE" submodel for metabolites, fitting with +

When using the "IORE" submodel for metabolites, fitting with "transform_rates = TRUE" (the default) often leads to failures of the numerical ODE solver. In this situation it may help to switch off the internal rate transformation.

@@ -359,15 +359,15 @@ fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) summary(fit)
#> mkin version: 0.9.44.9000 #> R version: 3.3.2 -#> Date of fit: Fri Nov 4 17:12:35 2016 -#> Date of summary: Fri Nov 4 17:12:35 2016 +#> Date of fit: Thu Nov 17 22:56:57 2016 +#> Date of summary: Thu Nov 17 22:56:57 2016 #> #> Equations: -#> d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent +#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted with method Port using 64 model solutions performed in 0.147 s +#> Fitted with method Port using 64 model solutions performed in 0.158 s #> #> Weighting: none #> @@ -436,7 +436,7 @@ m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# 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)))
#> user system elapsed -#> 1.208 1.256 0.935
coef(fit)
#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink +#> 1.168 1.260 0.924
coef(fit)
#> parent_0 log_k_parent_sink log_k_parent_m1 log_k_m1_sink #> 99.59848 -3.03822 -2.98030 -5.24750
endpoints(fit)
#> $ff #> parent_sink parent_m1 m1_sink #> 0.485524 0.514476 1.000000 @@ -450,7 +450,7 @@ #> m1 131.760712 437.69961 #>
## Not run: ------------------------------------ # # deSolve is slower when no C compiler (gcc) was available during model generation -# print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D, +# print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D, # solution_type = "deSolve"))) # coef(fit.deSolve) # endpoints(fit.deSolve) @@ -465,7 +465,7 @@ # fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D) # # Use starting parameters from parent only FOMC fit # fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, plot=TRUE) -# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, +# fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, # parms.ini = fit.FOMC$bparms.ode, plot=TRUE) # # # Use stepwise fitting, using optimised parameters from parent only fit, SFORB -- cgit v1.2.1