How do I store "arrays" of statistical models?

Is there an R data structure into which I can store a number of lm or lmer or gam objects? J has boxed arrays, and one can put pretty much any开发者_C百科thing into the cells of such a boxed array. I think that's what I'm looking for in R.

I've tried lists and data frames, to no avail; I thought lists might work.

> testlist <- list()
> testlist[1] <- subject1.2008.gam
Warning message:
In testlist[1] <- subject1.2008.gam :
  number of items to replace is not a multiple of replacement length
> 

Alternatively, is there a way to create and use a variable name on the LHS of <-?

Finally, perhaps you have a better idiom for me to consider. I'm trying to create a collection of GAM models over a set of subjects and years, for example. Later, I want to be able to plot or predict from those models, so I think I need to keep the full model around. Because I want to be able to use this code with different data sets later, I'd like not to hard-code the names of the gam objects nor their number.

While I started by putting the gam() call in a loop, I think one of the apply() functions might work better, but I still need a place to store the output.

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You need the [[ operator for lists, try

testlist[[1]] <- subject1.2008.gam

The other usual tip is that you may want to pre-allocate if you know how many elements you may have, I often do

testlist <- vector(mode="list", length=N)

for a given N.

---

Use [[ to access the list elements:

library(mgcv)
set.seed(0) ## simulate some data... 
dat <- gamSim(1,n=400,dist="normal",scale=2)

mods <- vector(mode = "list", length = 3)
for(i in seq_along(mods)) {
    mods[[i]] <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
}

Giving:

> str(mods, max = 1)
List of 3
 $ :List of 43
  ..- attr(*, "class")= chr [1:3] "gam" "glm" "lm"
 $ :List of 43
  ..- attr(*, "class")= chr [1:3] "gam" "glm" "lm"
 $ :List of 43
  ..- attr(*, "class")= chr [1:3] "gam" "glm" "lm"

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The other answers show how to use an index and [[ ]] but you can also do something like

x1  <- 1:10  ; y1  <-  30*x1 + rnorm(10)
x2  <- rnorm(20)  ; y2  <- 30*x2 + 100 + rnorm(20)
lm1 <- lm(y1 ~ x1); lm2 <- lm(y2 ~ x2) 

testlist <- list( A = lm1, Z = lm2 ) 
testlist$Z
testlist$Z$model$y2