I'll illustrate what I want to do with a Poisson GLM first. I have a GLM with only factor co-variates, thus, to bootstrap this GLM what I can do is e.g. take a single random observation without the response, predict the response using my Poisson GLM, and get a value $\lambda$. Since this is Poisson, the predicted expected value (response) is exactly the same as the parameter I need to for the distribution: I can sample a number from uniform $[0, 1]$ and quantile this number from a $POI(\lambda)$.
Now, is it possible to do this with the Gamma GLM (log-link)? The expected value of a gamma distribution is $\alpha\beta$ or $\frac{\alpha}{\beta}$, depending on your definition. I'm also open to other ideas.
MASSthat comes with R provides utilities for exactly this purpose). You can then easily convert to a shape-scale or shape rate parameterization as suits your needs. (In R you'd usergammato generate random gammas). – Glen_b May 21 '23 at 11:00MASS. – AyamGorengPedes May 21 '23 at 11:07library(help="MASS")should give you a window that lists the functions and data sets in MASS (which comes with R). In that list, you should be able to findgamma.dispersionandgamma.shape. (A reference for the dispersion-mean form is Dunn & Smyth Generalized Linear Models With Examples in R, page 428. The shape mean form simply replaces each $1/\phi$ with $\alpha$. Or take the shape-scale form and replace every occurrence of the scale parameter with $\mu/\alpha$; ... ctd – Glen_b Jun 02 '23 at 02:31