Get winning model for a fitted `pk` object
# S3 method for pk
get_winning_model(obj, newdata = NULL, method = NULL, criterion = "AIC", ...)A data.frame with one row for each `data_group`, `model` and `method` and The return value has attribute `criterion` giving the name of the criterion function used to compare models.
A [pk()] object
Optional: A `data.frame` containing new data to plot. Must contain at least variables `Chemical`, `Species`, `Route`, `Media`, `Dose`, `Time`, `Time.Units`, `Conc`, `Detect`, `Conc_SD`. Default `NULL`, to use the data in `obj$data`.
Character: One or more of the [optimx::optimx()] methods used in fitting. The winning model will be determined for each of these methods. Default `NULL` to get the winning model for each method in `obj$settings_optimx$method`.
The name of a criterion function to use for model comparison. Default "AIC". Must be the name of a function that (as for `AIC`) accepts arguments `obj`, `newdata`, `method` and `model` (may accept other arguments, specified in `...`) and returns output as for `AIC`: a data.frame with a column with the same name as `criterion` that has calculated values for model comparison. The "winning" value will be the smallest value.
Optional: Other arguments to `criterion` function.
Caroline Ring, Gilberto Padilla Mercado
Get the winning model (i.e. the model with the lowest value of the criterion specified in `criterion`) for a fitted `pk` object, for a specified method, and optionally for a specified new dataset. When there are ties it will return the first encounter, where the priority is: model_1comp > model_2comp > model_flat.