Compute estimate table for log-log regression

Usage

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