Create a "faceted" [pk()] object.
facet_data(...)An object of class `c("pkproto", "pk_facet_data")`. Under the hood, a named `list` containing the arguments provided to this function. Almost always added to a [pk()] object using [`+.pk`].
A set of variables or expressions quoted by [dplyr::vars()], defining groups of data that will each be fitted separately. These variables should appear in the `data` argument to [pk()] after mapping variables.
Caroline Ring, Gilberto Padilla Mercado, Paul Kruse
This function automates the process of doing PK fitting in "batch mode", when you have multiple concentration-dose-time datasets to fit, and you want to fit them all using the same set of instructions.
When you do something like
pk_cvt <- pk(cvt) +
facet_data(
facets = vars(chemicals_analyzed.dsstox_substance_id,
subjects.species_harmonized)
)
Now `pk_cvt` is an object of class `pk_faceted`: under the hood, a [tibble::tibble()] with one row for each group defined by a unique combination of the faceting variables, and a `list` column containing a [pk()] object corresponding to each group.
All of the [pk()] objects in the `list` column contain the same set of instructions, and they will all have the same status (*i.e.*, they are all in the same stage of the workflow at the same time). The only thing different among them is the data.
If you call a `pk` method on a `pk_faceted` object, the method will be applied in turn to the [pk()] object for each group.
If the method returns a [pk()] object (e.g. [preprocess_data.pk()], [data_info.pk()], [prefit.pk()], and [fit.pk()]), then the result for a `pk_faceted` object will be another `pk_faceted` object.
If the method returns something other than a [pk()] object (e.g. [coef.pk()], [coef_sd.pk()], [residuals.pk()], [predict.pk()], ...) then the result for a `pk_faceted` object will simply be a [tibble::tibble()] with a `list` column containing the result for each group -- it won't have class `pk_faceted`.
This function is named by analogy to [ggplot2::facet_wrap()] and [ggplot2::facet_grid()]. Those functions split up a dataset into groups by one or more `factor` variables, and produce a "faceted" grid of plots for each group of data. This function does an analogous thing for a [pk()] analysis. The dataset is split into groups by the unique combinations of variables in `facets`. For each group, a separate [pk()] object is created, using the instructions provided by the user. When methods like [preprocess_data()], [data_info()], [prefit()], and [fit()] are called on the resulting "faceted"