Standardizing the coefficients $\beta_1, \dots, \beta_k$ with scale()
does not make sense. This would subtract the mean and divide by the standard deviation across the coefficients: $\tilde \beta_j = (\beta_j - \bar \beta)/\mathrm{SD}(\beta_1, \dots, \beta_k)$.
Multiplying $\beta_j$ with the standard deviation of the corresponding regressor $\mathrm{SD}(x_j)$ would have the same effect as standardizing the regressor $x_j$ (e.g., with scale()
). This makes sense if you want to capture the marginal effect of a one standard deviation change in $x_j$ as opposed to a one unit change in $x_j$.
Additionally, you might find this answer about interpretation of betareg coef useful.