Linear Regression and group by in R


I want to do a linear regression in R using the lm() function. My data is an annual time series with one field for year (22 years) and another for state (50 states). I want to fit a regression for each state so that at the end I have a vector of lm responses. I can imagine doing for loop for each state then doing the regression inside the loop and adding the results of each regression to a vector. That does not seem very R-like, however. In SAS I would do a 'by' statement and in SQL I would do a 'group by'. What's the R way of doing this?

10/7/2016 8:04:48 PM

Accepted Answer

Here's one way using the lme4 package.

 d <- data.frame(state=rep(c('NY', 'CA'), c(10, 10)),
                 year=rep(1:10, 2),
                 response=c(rnorm(10), rnorm(10)))

 xyplot(response ~ year, groups=state, data=d, type='l')

 fits <- lmList(response ~ year | state, data=d)
Call: lmList(formula = response ~ year | state, data = d)
   (Intercept)        year
CA -1.34420990  0.17139963
NY  0.00196176 -0.01852429

Degrees of freedom: 20 total; 16 residual
Residual standard error: 0.8201316
10/2/2018 2:59:03 AM

Here's an approach using the plyr package:

d <- data.frame(
  state = rep(c('NY', 'CA'), 10),
  year = rep(1:10, 2),
  response= rnorm(20)

# Break up d by state, then fit the specified model to each piece and
# return a list
models <- dlply(d, "state", function(df) 
  lm(response ~ year, data = df))

# Apply coef to each model and return a data frame
ldply(models, coef)

# Print the summary of each model
l_ply(models, summary, .print = TRUE)

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