facet_data {invivoPKfit} | R Documentation |
Facet a PK fit
Description
Create a "faceted" [pk()] object.
Usage
facet_data(facets = vars(Chemical, Species), ...)
Arguments
facets |
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 *original* data (i.e., the 'data' argument to [pk()]). |
... |
Additional arguments, not currently used. |
Details
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"
Value
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'].
Author(s)
Caroline Ring, Gilbert Padilla Mercado, Paul Kruse