R help from GLM:
offset: this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be 'NULL' or a numeric vector of length either one or
equal to the number of cases. One or more 'offset' terms can
be included in the formula instead or as well, and if both
are specified their sum is used. See 'model.offset'.
Example:
clotting <- data.frame(
u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
> glm(lot1 ~ log(u), data=clotting, family=Gamma)
Call: glm(formula = lot1 ~ log(u), family = Gamma, data = clotting)
Coefficients:
(Intercept) log(u)
-0.01655 0.01534
Degrees of Freedom: 8 Total (i.e. Null); 7 Residual
Null Deviance: 3.513
Residual Deviance: 0.01673 AIC: 37.99
> glm(lot1 ~ log(u), data=clotting, family=Gamma,offset=rep(10,9))
Call: glm(formula = lot1 ~ log(u), family = Gamma, data = clotting, offset = rep(10, 9))
Coefficients:
(Intercept) log(u)
-10.01655 0.01534
Degrees of Freedom: 8 Total (i.e. Null); 7 Residual
Null Deviance: 3.513
Residual Deviance: 0.01673 AIC: 37.99
Then you will see the intercept decreases 10