I'm reviewing a paper which has the following biological experiment. A device is used to expose cells to varying amounts of fluid shear stress. As greater shear stress is applied to the cells, more of them start to detach from the substrate. At each level of shear stress, they count the cells that remain attached, and since they know the total number of cells that were attached at the beginning, they can calculate a fractional attachment (or detachment).
If you plot the adherent fraction vs. shear stress, the result is a logistic curve. In theory, each individual cell is a single observation, but obviously there are thousands or tens of thousand of cells, so the data set would be gigantic, if it was set up in the usual way (with each row being an observation).
So, naturally, my question (as stated in the title) should make sense now. How do we do a logistic regression using the fractional outcome as the D.V.? Is there some automatic transform that can be done in glm?
Along the same lines, if there were potentially 3 or more (fractional) measurements, how would one do this for a multinomial logistic regression?