Compute and tabulate estimates for log-log regression

Usage

df_doseprop(
  data,
  metrics,
  metric_var = PPTESTCD,
  exp_var = PPORRES,
  dose_var = DOSE,
  method = "normal",
  ci = 0.9,
  sigdigits = 3
)

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: Column in data containing the values provided in metrics. Accepts bare names or strings. Default is PPTESTCD.
exp_var: Column in data containing the exposure metric (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 derived 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

Value

data.frame

Examples

df_doseprop(data_sad_nca, metrics = c("aucinf.obs", "cmax"))
#>   Intercept StandardError  CI Power   LCL  UCL Proportional
#> 1      4.04        0.0663 90% 0.997 0.888 1.11         TRUE
#> 2      1.09        0.0616 90% 1.070 0.967 1.17         TRUE
#>                            PowerCI    Interpretation   PPTESTCD
#> 1 Power: 0.997 (90% CI 0.888-1.11) Dose-proportional aucinf.obs
#> 2  Power: 1.07 (90% CI 0.967-1.17) Dose-proportional       cmax