Thanks so much for all this.
The first solution is what I'm going with as I want the terms object to
come along so that predict still works.
On Wed, Jul 27, 2016 at 12:28 PM, William Dunlap via R-devel <
r-devel@r-project.org> wrote:
> Another solution is to only save the parts of the model obje
Another solution is to only save the parts of the model object that
interest you. As long as they don't include the formula (which is
what drags along the environment it was created in), you will
save space. E.g.,
tfun2 <- function(subset) {
junk <- 1:1e6
list(subset=subset, lm(Sepal.Lengt
On 27/07/2016 1:48 PM, Kenny Bell wrote:
In the below, I generate a model from an environment that isn't
.GlobalEnv with a large object that is unrelated to the model
generation. It seems to save the irrelevant object unnecessarily. In
my actual use case, I am running and saving many models in a
One way around this problem is to make a new environment whose
parent environment is .GlobalEnv and which contains only what the
the call to lm() requires and to compute lm() in that environment. E.g.,
tfun1 <- function (subset)
{
junk <- 1:1e+06
env <- new.env(parent = globalenv())
In the below, I generate a model from an environment that isn't
.GlobalEnv with a large object that is unrelated to the model
generation. It seems to save the irrelevant object unnecessarily. In
my actual use case, I am running and saving many models in a loop that
each use a single large data.fram
