Skip to contents

Plot a dose-proportionality assessment via power law (log-log) regression

Source: R/plot_doseprop.R
plot_doseprop.Rd

Dual-mode wrapper that delegates to df_doseprop() for computation and plot_build_doseprop() for rendering. Accepts either:

  • raw observation data (e.g. PKNCA output) plus a metrics vector — the common one-shot mode; or

  • a precomputed doseprop_stats object returned by df_doseprop() — skip the regression refit and replot with different style / se settings.

On the precomputed path, pipeline arguments (metrics, metric_name_var, metric_value_var, dose_var, method, ci, sigdigits) cannot be honored because the regression does not run again — passing any of them aborts with a message pointing the caller at df_doseprop(). Only style and se are accepted on both paths.

Usage

plot_doseprop(
  data,
  metrics = NULL,
  metric_name_var = "PPTESTCD",
  metric_value_var = "PPORRES",
  dose_var = "DOSE",
  method = "normal",
  ci = 0.9,
  sigdigits = 3,
  se = TRUE,
  style = NULL
)

Arguments

data

Either raw observation data (data.frame, default expected format is output from PKNCA::pk.nca()) or a doseprop_stats object returned by df_doseprop().

metrics

character vector of exposure metrics in data to plot. Required on the raw-data path; ignored when data is a doseprop_stats object.

metric_name_var

Column in data containing the metric names listed in metrics. Accepts bare names or strings. Default is PPTESTCD.

metric_value_var

Column in data containing the exposure metric values (dependent variable). Accepts bare names or strings. Default is PPORRES.

dose_var

Column in data containing the dose (independent variable). Accepts bare names or strings. Default is DOSE.

method

character string specifying the distribution to be used to derive the confidence interval. Options are "normal" (default) and "tdist".

ci

confidence interval to be calculated. Options 0.90 (default) and 0.95.

sigdigits

number of significant digits for rounding.

se

logical to display confidence interval around regression. Default is TRUE.

style

A ggstylekit::style_spec() controlling plot aesthetics. Defaults to style_doseprop(); view the defaults by running style_doseprop() with no arguments. Customize by passing style = style_doseprop(...), or restyle the returned plot with restyle_plot(). The per-metric facet layout is taken from the style's facet_scales (default "free"), facet_nrow, and facet_ncol; the facet, logx, and logy fields are ignored because the builder sets the facet and log-log axes itself.

Value

a ggplot plot object

See also

Other dose proportionality: df_doseprop(), is_doseprop_stats(), plot_build_doseprop(), style_doseprop()

Examples

# Raw-data path
plot_doseprop(dplyr::filter(data_sad_nca, PART == "Part 1-SAD"),
               metrics = c("aucinf.obs", "cmax"))


# Precomputed path: compute once, replot many times
stats <- df_doseprop(dplyr::filter(data_sad_nca, PART == "Part 1-SAD"),
                      metrics = c("aucinf.obs", "cmax"))
plot_doseprop(stats)

plot_doseprop(stats, se = FALSE)