Hi, I am currently trying to build a regression model for calibration of HPLC outputs. I decided to use a multiplicative error model: Y_i = (a*X_i + b)*eps_i where the eps_i ~ iid N(0, s^2). Now I am having a hard time estimating my parameters ;) So the idea was to apply log() to both sides: Z_i = log(Y_i) = log(a*X_i + b) + log(eps_i) Now the additive errors are lognormally distributed and I could formulate this as a GLM Z_i = g^(-1)(a*X_i + b) + iota_i where iota_i are lognormal and the link function g(x) is exp(x) as g^(-1) = log. So wouldn't the corresponding call for R have to be something like: glm(z ~ x, data=data.frame(x=x, z=log(y)), family=lognormal(link='exp')) this however is not working (there is no lognormal family and no exp link function ^^. How do I estimate those parameters? This seems to be a pretty standard problem to me... Thanks for your comments! Kevin