Do pre-fit calculations and checks
# S3 method for pk
do_prefit(obj, ...)The same `pk` object, but with a new element `prefit`, containing the results of pre-fit calculations and checks for each model and for the error model.
A `pk` object
Additional arguments. Not in use.
Caroline Ring
This function does the following:
- Based on the error model in `stat_error_model` and the pre-processed data, determines the number of residual standard deviations ("sigmas") hyperparameters to be estimated. - Determines which "sigma" hyperparameter corresponds to each observation in the data. - Calculates lower/upper bounds and starting guesses for each "sigma" hyperparameter - For each model in `stat_model`, calls its `params_fun`, the function that, based on the data, determines whether to optimize each model parameter, and calculates lower/upper bounds and starting guesses for each model parameter to be optimized. Only non-excluded observations are passed to each model's `params_fun`.
Lower bounds for each "sigma" hyperparameter are set to `sqrt(.Machine$double_eps)`.
Upper bounds for each "sigma" hyperparameter are calculated as the standard deviation of observations in the corresponding error SD group (see [combined_sd()]), with any specified transformations applied (dose-normalization and/or log10-transformation). If the combined SD is non-finite or less than the sigma lower bound, then the maximum concentration is used as an upper bound; if this still returns a non-finite value or a value less than the lower bound, then a constant value of 1000 is substituted.
The starting guess for each "sigma" hyperparameter is one-tenth of the upper bound.