In practice, this function undoes the flattening needed to run DEoptim. Hence, it should take the same parameters that optimisation.params.peaks does. Takes a linear vector of parameters, as passed to and returned from an optimisation method, and makes it a list. Transforms degradation parameters back into normal (non-logarithmic) form. Adds fixed arguments back into the list.
relistArguments.peaks(parameters, hypothesis, fixed=NULL,
logDegradation=TRUE, arguments=NULL)Vector of parameters
Hypothesis from which objective function was obtained.
Names of the arguments which were fixed during optimisation.
Whether degradation is logarithmic form.
Initial guess, if any, when starting minimization.
Input parameters as a list.
optimisation.params.peaks, initial.arguments.peaks