Compute estimate table for log-log regression

Source: R/plot_doseprop.R

Compute estimate table for log-log regression

df_loglog(fit, method = "normal", ci = 0.95, sigdigits = 3)

Arguments

fit

lm model object for the log-log regression

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

Value

data.frame

Examples

mod_auc <- mod_loglog(dplyr::filter(data_sad_nca, PPTESTCD == "aucinf.obs"))
df_loglog(mod_auc)
#>   Intercept StandardError  CI Power   LCL  UCL Proportional
#> 1      4.04        0.0663 95% 0.997 0.867 1.13         TRUE
#>                            PowerCI    Interpretation
#> 1 Power: 0.997 (95% CI 0.867-1.13) Dose-proportional

mod_cmax <- mod_loglog(dplyr::filter(data_sad_nca, PPTESTCD == "cmax"))
df_loglog(mod_cmax)
#>   Intercept StandardError  CI Power   LCL  UCL Proportional
#> 1      1.09        0.0616 95%  1.07 0.947 1.19         TRUE
#>                           PowerCI    Interpretation
#> 1 Power: 1.07 (95% CI 0.947-1.19) Dose-proportional