R extract regression coefficients from multiply regression via lapply command

Question

I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I\'d like to run a regression of 28 variables on the remaining 2

Answer 1

This is another example of the classic Split-Apply-Combine problem, which can be addressed using the plyr package by @hadley. In your problem, you want to

1. Split data frame by state
2. Apply regressions for each subset
3. Combine coefficients into data frame.

I will illustrate it with the Cars93 dataset available in MASS library. We are interested in figuring out the relationship between horsepower and enginesize based on origin of country.

# LOAD LIBRARIES
require(MASS); require(plyr)

# SPLIT-APPLY-COMBINE
regressions <- dlply(Cars93, .(Origin), lm, formula = Horsepower ~ EngineSize)
coefs <- ldply(regressions, coef)

   Origin (Intercept) EngineSize
1     USA    33.13666   37.29919
2 non-USA    15.68747   55.39211

EDIT. For your example, substitute PUF for Cars93, state for Origin and fm for the formula

Answer 2

I've cleaned up your code slightly:

fm <- z ~ class1+class2+class3+class4+class5+class6+class7+
          xtot+e00200+e00300+e00600+e00900+e01000+p04470+e04800+
          e09600+e07180+e07220+e07260+e06500+e10300+
          e59720+e11900+e18425+e18450+e18500+e19700

PUFsplit <- split(PUF, PUF$state)
mod <- lapply(PUFsplit, function(z) lm(fm, data=z))

Beta <- sapply(mod, coef)

If you wanted, you could even put this all in one line:

Beta <- sapply(lapply(split(PUF, PUF$state), function(z) lm(fm, data=z)), coef)