search for: fmid

...someone has already solved my problem, but I thought I would ask quickly, just in case someone has. Let' say I start with a (flattened panel) model that says y[i] = x[i] + b*(T-x[i]) easy enough---this is just a linear model. I could also make this a fixed-effects model if I change T to T[fmid], where fmid is the firm's id. I know I can do this faster, but logically, what I want to estimate is lm( y ~ as.factor(fmid) + x ). I have about 100,000 observations, and about 10,000 firm ids. now, let me move to a world in which b is a function of the distance between T and x, b= a+c*(T-x...

...print(summary(lm( Y ~ X + fe))) I would like to know how to get the same coefficient on X using nlme. (I cannot use this ordinary lm method in my real application, simply because I have 10,000 fixed effects.) I tried a variety of arguments to the "fixed" nlme parameter (e.g., fixed=list(fmid)), but did not get the syntax right. could someone please tell me the magic spell? may I also suggest that such an example be added to the nlme examples documentation, too, please? help appreciated. regards, /ivo