Perform a log-log Regression
mod_loglog(data, exp_var = "PPORRES", dose_var = "DOSE")Input dataset for log-log regression.
Default expected format is output from PKNCA::pk.nca() (i.e., SDTM PP formatting)
Character string specifying the variable in data containing the exposure metric (dependent variable)
Default is "PPORRES".
Character string specifying the variable in data containing the dose (independent variable)
Default is "DOSE".
lm object
mod_auc <- mod_loglog(dplyr::filter(data_sad_nca, PPTESTCD == "aucinf.obs"))
summary(mod_auc)
#>
#> Call:
#> stats::lm(formula = form, data = data)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.80124 -0.29492 -0.03507 0.14386 1.24984
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 4.04173 0.30499 13.25 5.5e-15 ***
#> log(DOSE) 0.99657 0.06629 15.03 < 2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.4606 on 34 degrees of freedom
#> Multiple R-squared: 0.8692, Adjusted R-squared: 0.8654
#> F-statistic: 226 on 1 and 34 DF, p-value: < 2.2e-16
#>
mod_cmax <- mod_loglog(dplyr::filter(data_sad_nca, PPTESTCD == "cmax"))
summary(mod_cmax)
#>
#> Call:
#> stats::lm(formula = form, data = data)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.95350 -0.31769 0.00194 0.26984 0.98285
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 1.0917 0.2834 3.852 0.000494 ***
#> log(DOSE) 1.0680 0.0616 17.337 < 2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.428 on 34 degrees of freedom
#> Multiple R-squared: 0.8984, Adjusted R-squared: 0.8954
#> F-statistic: 300.6 on 1 and 34 DF, p-value: < 2.2e-16
#>