diff options
Diffstat (limited to 'R/mkinfit.R')
| -rw-r--r-- | R/mkinfit.R | 49 |
1 files changed, 27 insertions, 22 deletions
diff --git a/R/mkinfit.R b/R/mkinfit.R index 17fd59d0..a3e39053 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -1,7 +1,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' Fit a kinetic model to data with one or more state variables -#' +#' #' This function maximises the likelihood of the observed data using the Port #' algorithm \code{\link{nlminb}}, and the specified initial or fixed #' parameters and starting values. In each step of the optimsation, the @@ -9,11 +9,11 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' parameters of the selected error model are fitted simultaneously with the #' degradation model parameters, as both of them are arguments of the #' likelihood function. -#' +#' #' Per default, parameters in the kinetic models are internally transformed in #' order to better satisfy the assumption of a normal distribution of their #' estimators. -#' +#' #' @param mkinmod A list of class \code{\link{mkinmod}}, containing the kinetic #' model to be fitted to the data, or one of the shorthand names ("SFO", #' "FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a @@ -33,7 +33,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' 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 @@ -105,10 +105,10 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' argument. The default value is 100. #' @param error_model If the error model is "const", a constant standard #' deviation is assumed. -#' +#' #' If the error model is "obs", each observed variable is assumed to have its #' own variance. -#' +#' #' If the error model is "tc" (two-component error model), a two component #' error model similar to the one described by Rocke and Lorenzato (1995) is #' used for setting up the likelihood function. Note that this model @@ -119,27 +119,27 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' the error model. If the error model is "const", unweighted nonlinear #' least squares fitting ("OLS") is selected. If the error model is "obs", or #' "tc", the "d_3" algorithm is selected. -#' +#' #' The 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 least 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" (Iteratively Reweighted Least Squares) starts with #' unweighted least squares, and then iterates optimization of the error #' model parameters and subsequent optimization of the degradation model @@ -161,20 +161,20 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' @author Johannes Ranke #' @seealso Plotting methods \code{\link{plot.mkinfit}} and #' \code{\link{mkinparplot}}. -#' +#' #' Comparisons of models fitted to the same data can be made using #' \code{\link{AIC}} by virtue of the method \code{\link{logLik.mkinfit}}. -#' +#' #' Fitting of several models to several datasets in a single call to #' \code{\link{mmkin}}. #' @source Rocke, David M. und Lorenzato, Stefan (1995) A two-component model #' for measurement error in analytical chemistry. Technometrics 37(2), 176-184. #' @examples -#' +#' #' # Use shorthand notation for parent only degradation #' fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) #' summary(fit) -#' +#' #' # One parent compound, one metabolite, both single first order. #' # Use mkinsub for convenience in model formulation. Pathway to sink included per default. #' SFO_SFO <- mkinmod( @@ -192,7 +192,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' coef(fit.deSolve) #' endpoints(fit.deSolve) #' } -#' +#' #' # Use stepwise fitting, using optimised parameters from parent only fit, FOMC #' \dontrun{ #' FOMC_SFO <- mkinmod( @@ -204,7 +204,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE) #' fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE, #' parms.ini = fit.FOMC$bparms.ode) -#' +#' #' # Use stepwise fitting, using optimised parameters from parent only fit, SFORB #' SFORB_SFO <- mkinmod( #' parent = list(type = "SFORB", to = "m1", sink = TRUE), @@ -217,7 +217,7 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE) #' fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE) #' } -#' +#' #' \dontrun{ #' # Weighted fits, including IRLS #' SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), @@ -229,8 +229,8 @@ if(getRversion() >= '2.15.1') utils::globalVariables(c("name", "time", "value")) #' f.tc <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "tc", quiet = TRUE) #' summary(f.tc) #' } -#' -#' +#' +#' #' @export mkinfit <- function(mkinmod, observed, parms.ini = "auto", @@ -795,6 +795,8 @@ mkinfit <- function(mkinmod, observed, fit$hessian <- try(numDeriv::hessian(cost_function, c(degparms, errparms), OLS = FALSE, update_data = FALSE), silent = TRUE) + dimnames(fit$hessian) <- list(names(c(degparms, errparms)), + names(c(degparms, errparms))) # Backtransform parameters bparms.optim = backtransform_odeparms(fit$par, mkinmod, @@ -805,6 +807,9 @@ mkinfit <- function(mkinmod, observed, fit$hessian_notrans <- try(numDeriv::hessian(cost_function, c(bparms.all, errparms), OLS = FALSE, trans = FALSE, update_data = FALSE), silent = TRUE) + + dimnames(fit$hessian_notrans) <- list(names(c(bparms.all, errparms)), + names(c(bparms.all, errparms))) }) fit$error_model_algorithm <- error_model_algorithm @@ -839,7 +844,7 @@ mkinfit <- function(mkinmod, observed, # Log-likelihood with possibility to fix degparms or errparms fit$ll <- function(P, fixed_degparms = FALSE, fixed_errparms = FALSE) { - - cost_function(P, fixed_degparms = fixed_degparms, + - cost_function(P, trans = FALSE, fixed_degparms = fixed_degparms, fixed_errparms = fixed_errparms, OLS = FALSE, update_data = FALSE) } |