nca.pk {invivoPKfit} | R Documentation |
NCA for a 'pk' object
Description
Non-compartmental analysis for a 'pk' object
Usage
## S3 method for class 'pk'
nca(
obj,
newdata = NULL,
nca_group = NULL,
exclude = TRUE,
dose_norm = FALSE,
suppress.messages = NULL,
...
)
Arguments
obj |
A [pk()] model object. Must be fitted, or the function will exit with an error. |
newdata |
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)'. |
nca_group |
A list of variables provided using a 'dplyr::vars()' call. The data (either 'newdata' or 'obj$data') will be grouped according to the unique combinations of these variables. For each unique combination of these variables in the data, a set of TK statistics will be computed. The default is 'NULL', to use the same data grouping that was set in [stat_nca()] for the 'pk' object. However, you may specify a different data grouping if you wish. |
exclude |
Logical: 'TRUE' to group the data for NCA after removing any observations in the data marked for exclusion (if there is a variable 'exclude' in the data, an observation is marked for exclusion when 'exclude NCA, regardless of exclusion status. Default 'TRUE'. |
dose_norm |
Logical: 'TRUE' to perform NCA after dose-normalizing concentrations. 'FALSE' (default) to perform NCA on un-transformed concentrations. |
suppress.messages |
Logical: whether to suppress message printing. If NULL (default), uses the setting in 'obj$settings_preprocess$suppress.messages' |
... |
Additional arguments. Currently not in use. |
Details
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).
Value
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).
Author(s)
Caroline Ring