Apply lm to subset of data frame defined by a third column of the frame

I've got a data frame containing a vector of x values, a vector of y values, and a vector of IDs:

x <- rep(0:3, 3)
y <- runif(12)
ID <- c(rep("a", 4), rep("b", 4), rep("c", 4))
df <- data.frame(ID=ID, x=x, y=y)

I'd like to create a separate lm for the subset of x's and y's sharing the same ID. The following code gets the job done:

a.lm <- lm(x~y, data=subset(df, ID=="a"))
b.lm <- lm(x~y, data=subset(df, ID=="b"))
c.lm <- lm(x~y, data=subset(df, ID=="c"))

Except that th开发者_StackOverflow中文版is is very brittle (future data sets might have different IDs) and un-vectorized. I'd also like to store all the lms in a single data structure. There must be an elegant way to do this, but I can't find it. Any help?

---

Using base functions, you can split your original dataframe and use lapply on that:

lapply(split(df,df$ID),function(d) lm(x~y,d))
$a

Call:
lm(formula = x ~ y, data = d)

Coefficients:
(Intercept)            y  
    -0.2334       2.8813  


$b

Call:
lm(formula = x ~ y, data = d)

Coefficients:
(Intercept)            y  
     0.7558       1.8279  


$c

Call:
lm(formula = x ~ y, data = d)

Coefficients:
(Intercept)            y  
      3.451       -7.628  

---

How about

library(nlme) ## OR library(lme4)
lmList(x~y|ID,data=d)

?

---

Use some of the magic in the plyr package. The function dlply takes a data.frame, splits it, applies a function to each element, and combines it into a list. This is perfect for your application.

library(plyr)
#fitList <- dlply(df, .(ID), function(dat)lm(x~y, data=dat))
fitList <- dlply(df, .(ID), lm, formula=x~y) # Edit

This creates a list with a model for each subset of ID:

str(fitList, max.level=1)

List of 3
 $ a:List of 12
  ..- attr(*, "class")= chr "lm"
 $ b:List of 12
  ..- attr(*, "class")= chr "lm"
 $ c:List of 12
  ..- attr(*, "class")= chr "lm"
 - attr(*, "split_type")= chr "data.frame"
 - attr(*, "split_labels")='data.frame':    3 obs. of  1 variable:

This means you can subset the list and work with that. For example, to get the coefficients for your lm model where ID=="a":

> coef(fitList$a)
(Intercept)           y 
   3.071854   -3.440928