Hello,
Your example doesn't run, lmList needs a 'data' argument.
set.seed(8109)
d <- rep(1:10, each=10)
x <- rnorm(100)
e <- rnorm(100)
y <- 2*x + e
dat <- data.frame(x=x, y=y, d=d, e=e)
model <- lmList(y ~ x | d, data = dat)
predict(model)
To the op: if you already have the larger data.farme divided into a list
of smaller df's, such as the 'lst' below, you can use lapply.
df.lst <- split(dat, dat$d)
model.lst <- lapply(df.lst, function(.data) lm(y ~ x, data = .data))
lapply(model.lst, coef) # just an example
lapply(model.lst, predict) # what you want
Hope this helps,
Rui Barradas
Em 24-08-2012 16:05, Daniel Malter escreveu:
The easiest way may be to use lmList in the nlme library:
#simulate data
d<-rep(1:10,each=10)
x<-rnorm(100)
e<-rnorm(100)
y<-2*x+e
require(nlme) #or install and load package
lmList(y~x|d)
#predicted values are obtained with:
predict(lmList(y~x|d)
HTH,
Daniel
jeff6868 wrote
Hi everybody,
I have a question about applying a specific function (with the
calculations I want to do), on a list of elements.
Each elements are like a data.frame (with nrows and ncolumns), and have
the same structure.
At frist, I had a big data.frame that I splitted in all my elements of my
list. They have been splitted by day.
For example, the name of the first element of my list is "2011-01-01", and
is a data.frame corresponding to all my data from this specific date. Then
my second element is "2011-01-02", etc....
My question is: how can I apply a function on each element separately (a
bit like a loop)?
For example, if my data from the first element "2011-01-01" is:
element1 <- data.frame(x=rnorm(1:10),data=c(1:10))
I would like to do a regression between "x" and "data (so lm(data ~x) ),
to get the predicted values of the regression, and then to keep the
results in a new object.
And then, do the same with the second element (regression between "x" and
"data" of the second element), keep the results of the predicted values
and keep the results.
... and so one with 200 elements.
Is there any way to do this?
Thanks a lot!
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