Non-compartmental analysis for a `pk` object
# S3 method for pk
nca(
obj,
newdata = NULL,
nca_group = NULL,
exclude = TRUE,
dose_norm = FALSE,
suppress.messages = NULL,
...
)A `data.frame` with variables including all the grouping variables in `nca_group`, `nca_group_id`; `design` (the auto-detected study design for this group); `param_name` (the name of the NCA parameter); `param_value` (the NCA parameter value); `param_sd_z` (standard deviation of the estimated NCA parameter value, if available); `param_units` (the units of the NCA parameter, derived from the units of the data).
A [pk()] model object. Must be fitted, or the function will exit with an error.
Optional: A `data.frame` containing new data for which to compute the TK stats. Must contain at least variables `Chemical`, `Species`, `Route`, `Dose`, `Conc`, `Dose.Units`, `Conc.Units`, and `Time.Units`, and any other variables named in `tk_grouping`. Default `NULL`, to use the data in `get_data(obj)`.
A list of variables provided using a `dplyr::vars()` call. The data (either `newdata` or `obj$data`) will be grouped according to the unique combinations of these variables. For each unique combination of these variables in the data, a set of TK statistics will be computed. The default is `NULL`, to use the same data grouping that was set in [stat_nca()] for the `pk` object. However, you may specify a different data grouping if you wish.
Logical: `TRUE` to group the data for NCA after removing any observations in the data marked for exclusion (if there is a variable `exclude` in the data, an observation is marked for exclusion when `exclude NCA, regardless of exclusion status. Default `TRUE`.
Logical: `TRUE` to perform NCA after dose-normalizing concentrations. `FALSE` (default) to perform NCA on un-transformed concentrations.
Logical: whether to suppress message printing. If NULL (default), uses the setting in `obj$settings_preprocess$suppress.messages`
Additional arguments. Currently not in use.
Caroline Ring
Perform non-compartmental analysis of data in a `pk` object (or optionally, new data), using data groupings defined by `get_nca_group()` for the `pk` object (or optionally, new groupings). If you provide both `newdata` and `nca_group`, then everything in the `pk` object will be ignored and you will simply be doing NCA *de novo* (which may be what you want).