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?

Here's one way using the `lme4`

package.

```
library(lme4)
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)
fits
#------------
Call: lmList(formula = response ~ year | state, data = d)
Coefficients:
(Intercept) year
CA -1.34420990 0.17139963
NY 0.00196176 -0.01852429
Degrees of freedom: 20 total; 16 residual
Residual standard error: 0.8201316
```

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)
)
library(plyr)
# 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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