相关疑难解决方法(0)

人们用什么技巧来管理交互式R会话的可用内存？我使用下面的函数[根据Petr Pikal和David Hinds在2004年的r-help列表中的帖子]列出(和/或排序)最大的对象,偶尔列出rm()其中的一些.但到目前为止,最有效的解决方案是在具有充足内存的64位Linux下运行.

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# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)
    obj.dim <- t(napply(names, function(x)
                        as.numeric(dim(x))[1:2]))
    vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
    obj.dim[vec, 1] <- napply(names, length)[vec]
    out <- data.frame(obj.type, …

我遇到了试图在R中使用大对象的问题.例如:

> memory.limit(4000)
> a = matrix(NA, 1500000, 60)
> a = matrix(NA, 2500000, 60)
> a = matrix(NA, 3500000, 60)
Error: cannot allocate vector of size 801.1 Mb
> a = matrix(NA, 2500000, 60)
Error: cannot allocate vector of size 572.2 Mb # Can't go smaller anymore
> rm(list=ls(all=TRUE))
> a = matrix(NA, 3500000, 60) # Now it works
> b = matrix(NA, 3500000, 60)
Error: cannot allocate vector of size 801.1 Mb # But that is all there …