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

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

plot_doseprop(
  data,
  metrics,
  metric_var = "PPTESTCD",
  exp_var = "PPORRES",
  dose_var = "DOSE",
  method = "normal",
  ci = 0.95,
  sigdigits = 3,
  se = TRUE
)

Arguments

data

Input dataset for log-log regression. Default expected format is output from PKNCA::pk.nca() (i.e., SDTM PP formatting)

metrics

character vector of exposure metrics in data to plot

metric_var

character string of variable in data containing the values provided in metrics. Default is "PPTESTCD".

exp_var

Character string specifying the variable in data containing the exposure metric (dependent variable) Default is "PPORRES".

dose_var

Character string specifying the variable in data containing the dose (independent variable) Default is "DOSE".

method

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

ci

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

sigdigits

number of significant digits for rounding

se

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

Value

a ggplot plot object

Examples

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