Plot data and model fits from a [pk()] object.
# S3 method for pk
plot(
x,
newdata = NULL,
model = NULL,
method = NULL,
use_scale_conc = FALSE,
time_trans = FALSE,
log10_C = NULL,
plot_data_aes = NULL,
plot_point_aes = NULL,
facet_fun = NULL,
facet_fun_args = NULL,
drop_nonDetect = FALSE,
plot_fit_aes = NULL,
n_interp = 10,
fit_limits = NULL,
print_out = FALSE,
best_fit = FALSE,
...
)A [ggplot2::ggplot()]-class plot object.
A [pk()] object. In this case `x` is used to align with generic method.
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$data`.
Character: One or more of the models fitted. Curve fits will be plotted for these models. Default `NULL` to plot fits for all models in `x$stat_model`.
Character: One or more of the [optimx::optimx()] methods used. Default `NULL` to plot fits for all methods in `x$settings_optimx$method`.
Possible values: `TRUE`, `FALSE`, or a named list with elements `dose_norm` and `log10_trans` which themselves should be either `TRUE` or `FALSE`. If `use_scale_conc = FALSE` (the default for this function), then the data and fits will be plotted without any dose-normalization or log-transformation. If `use_scale_conc = TRUE` , then the concentration scaling/transformations in `x` will be applied to the y-axis (concentration axis). If `use_scale_conc = list(dose_norm = ..., log10_trans = ...)`, then the specified dose normalization and/or log10-transformation will be applied to the y-axis (concentration axis) of the plots.
Default `FALSE`. Determines whether time values will be transformed.
Default `NULL`. Determines whether y-axis (concentration) should be log10 transformed. Takes `TRUE` or `FALSE` values. Otherwise it defaults to the value determined from `use_scale_conc`.
Optional: Aesthetic mapping for the plot layer that visualizes the data. Default `NULL`, in which case a default mapping will be used based on the value of `use_scale_conc`.
Optional: Aesthetic mappings for geom_point layer that determines the fill of the points. Defaults to `NULL`.
Default `"facet_grid"`. Optional: The name of the `ggplot2` faceting function to use: [ggplot2::facet_grid()], [ggplot2::facet_wrap()], or `'none'` to do no faceting. Default `NULL`, in which case a default faceting will be applied based on the value of `use_scale_conc`.
A named list of arguments to the faceting function in `facet_fun` (if any). Default: ``` list(rows = ggplot2::vars(Route), cols= ggplot2::vars(Media), scales = "free_y", labeller = "label_both") ```
Default `FALSE`. Whether to eliminate observations below the level of quantification (LOQ).
Optional: Aesthetic mapping for the plot layer that visualizes the fitted curves. Default `NULL`, in which case a default mapping will be used based on the value of `use_scale_conc`.
For plotting: the number of time points to interpolate between each observed time point. Default 10.
Default `NULL`. c(Upper Bound, Lower Bound). Supply a numeric vector. These values filter the predicted values for fits to not exceed 2.25x of the maximum observed concentration values for each `data_group` in the `pk` object. When there is a log10 transformation of concentration values, it limits predicted values to 1/20th of the minimum observed concentration values and 5 times the maximum value.
For plotting: whether the output of the function should be the list of plots. Default `FALSE`.
Default FALSE. Determines whether fit plot outputs only the best fit from `get_winning_model()`
Additional arguments not in use.
Caroline Ring, Gilberto Padilla Mercado
If the [pk()] object has not been fitted, then only the data will be plotted (because no curve fits exist).