如果在R中进行模拟后使用鼠标滚轮,Windows 7将无响应

Question

我在Windows 7 64位中使用R 3.0.2.运行模拟输出大于约100000的长度后,如果我在R控制台中使用鼠标滚轮,Windows会无限期冻结.

我曾经让它坐了一个多星期没有回应.强制关闭是唯一的出路,它不会在Windows事件日志中注册.我试图在其他程序中复制该问题,但它似乎只在R中出现.我已经尝试了几个版本的R,每个都卸载并重新安装,使用了几个不同的计算机鼠标和驱动程序,甚至重新安装了Windows.什么都没有解决问题.

我能想到的其他一些共同方面(但尚未确定是因素)就是这样

1. 模拟通常在模拟期间(flush.console()例如使用)向控制台打印迭代次数等,以及

2. 在模拟期间(但不是在完成时)内存使用很高.计算机具有32GB RAM和两个Intel Xeon E5-2687W CPU(8核,3.1GHz).

可能导致此问题的一个示例是:

    foo<-function(X, SD, N, sims){
    output<-vector("list")
    for(i in 1:sims){
        output[[i]]<-rnorm(N, X, SD)
        flush.console()
        cat(paste("Iteration", i, ":", "\n",
            "mean = ", round(mean(output[[i]]),1), "\n",
            "sd   = ", round(sd(output[[i]]), 1), "\n"))
    }
    return(output)
    }

    result<-foo(X=20, SD=2, N=100, sims=100) # but increase N or sims to > 100000

    # Now used the mouse scroll wheel in the R console.  Computer freezes.
    # Can also do rm(list=ls()) after the simulation, then use scroll wheel... Computer still freezes.

Answer 1

您的代码在我的计算机上运行顺利（Rgui.exe 和 Tinn-R + Rterm.exe）。

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Windows 7（64 位）和 R 版本 3.0.2 已修补（2013-10-08 r64039）。

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请参阅下面的输出：

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R version 3.0.2 Patched (2013-10-08 r64039) -- "Frisbee Sailing"\nCopyright (C) 2013 The R Foundation for Statistical Computing\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\n\nR \xc3\xa9 um software livre e vem sem GARANTIA ALGUMA.\nVoc\xc3\xaa pode redistribu\xc3\xad-lo sob certas circunst\xc3\xa2ncias.\nDigite \'license()\' ou \'licence()\' para detalhes de distribui\xc3\xa7\xc3\xa3o.\n\nR \xc3\xa9 um projeto colaborativo com muitos contribuidores.\nDigite \'contributors()\' para obter mais informa\xc3\xa7\xc3\xb5es e\n\'citation()\' para saber como citar o R ou pacotes do R em publica\xc3\xa7\xc3\xb5es.\n\nDigite \'demo()\' para demonstra\xc3\xa7\xc3\xb5es, \'help()\' para o sistema on-line de ajuda,\nou \'help.start()\' para abrir o sistema de ajuda em HTML no seu navegador.\nDigite \'q()\' para sair do R.\n\n> foo<-function(X, SD, N, sims){\n+     output<-vector("list")\n+     for(i in 1:sims){\n+         output[[i]]<-rnorm(N, X, SD)\n+         flush.console()\n+         cat(paste("Iteration", i, ":", "\\n",\n+             "mean = ", round(mean(output[[i]]),1), "\\n",\n+             "sd   = ", round(sd(output[[i]]), 1), "\\n"))\n+     }\n+     return(output)\n+     }\n> \n>     result<-foo(X=20, SD=2, N=100, sims=100) # but increase N or sims to > 100000\nIteration 1 : \n mean =  20.1 \n sd   =  2 \nIteration 2 : \n mean =  20 \n sd   =  2 \nIteration 3 : \n mean =  19.9 \n sd   =  2 \nIteration 4 : \n mean =  19.9 \n sd   =  1.9 \nIteration 5 : \n mean =  19.5 \n sd   =  2.1 \nIteration 6 : \n mean =  20 \n sd   =  2.2 \nIteration 7 : \n mean =  20.2 \n sd   =  2.2 \nIteration 8 : \n mean =  20 \n sd   =  1.8 \nIteration 9 : \n mean =  19.5 \n sd   =  2 \nIteration 10 : \n mean =  20.1 \n sd   =  2.1 \nIteration 11 : \n mean =  19.8 \n sd   =  2 \nIteration 12 : \n mean =  20 \n sd   =  2 \nIteration 13 : \n mean =  20 \n sd   =  1.8 \nIteration 14 : \n mean =  19.9 \n sd   =  1.8 \nIteration 15 : \n mean =  20.2 \n sd   =  2 \nIteration 16 : \n mean =  20.2 \n sd   =  1.8 \nIteration 17 : \n mean =  20.5 \n sd   =  2.2 \nIteration 18 : \n mean =  20 \n sd   =  1.9 \nIteration 19 : \n mean =  19.8 \n sd   =  1.8 \nIteration 20 : \n mean =  19.9 \n sd   =  2.2 \nIteration 21 : \n mean =  20.2 \n sd   =  2 \nIteration 22 : \n mean =  19.7 \n sd   =  1.8 \nIteration 23 : \n mean =  19.8 \n sd   =  2 \nIteration 24 : \n mean =  19.8 \n sd   =  1.9 \nIteration 25 : \n mean =  19.9 \n sd   =  2.1 \nIteration 26 : \n mean =  20.3 \n sd   =  2.1 \nIteration 27 : \n mean =  19.6 \n sd   =  2 \nIteration 28 : \n mean =  20 \n sd   =  2.1 \nIteration 29 : \n mean =  20 \n sd   =  2.2 \nIteration 30 : \n mean =  19.9 \n sd   =  1.7 \nIteration 31 : \n mean =  19.9 \n sd   =  1.8 \nIteration 32 : \n mean =  19.8 \n sd   =  1.9 \nIteration 33 : \n mean =  20.1 \n sd   =  2.1 \nIteration 34 : \n mean =  20.3 \n sd   =  2.2 \nIteration 35 : \n mean =  20.2 \n sd   =  2 \nIteration 36 : \n mean =  20.1 \n sd   =  2 \nIteration 37 : \n mean =  19.8 \n sd   =  2.1 \nIteration 38 : \n mean =  20 \n sd   =  2 \nIteration 39 : \n mean =  20.1 \n sd   =  1.9 \nIteration 40 : \n mean =  20.1 \n sd   =  2 \nIteration 41 : \n mean =  19.8 \n sd   =  2.1 \nIteration 42 : \n mean =  19.9 \n sd   =  2 \nIteration 43 : \n mean =  19.8 \n sd   =  1.8 \nIteration 44 : \n mean =  20.1 \n sd   =  1.7 \nIteration 45 : \n mean =  20.1 \n sd   =  1.8 \nIteration 46 : \n mean =  20.1 \n sd   =  1.9 \nIteration 47 : \n mean =  20 \n sd   =  2.2 \nIteration 48 : \n mean =  19.8 \n sd   =  1.9 \nIteration 49 : \n mean =  19.9 \n sd   =  2.1 \nIteration 50 : \n mean =  19.7 \n sd   =  2 \nIteration 51 : \n mean =  19.9 \n sd   =  2 \nIteration 52 : \n mean =  20.5 \n sd   =  2 \nIteration 53 : \n mean =  20 \n sd   =  2 \nIteration 54 : \n mean =  20.3 \n sd   =  1.9 \nIteration 55 : \n mean =  19.9 \n sd   =  1.9 \nIteration 56 : \n mean =  20.1 \n sd   =  2.1 \nIteration 57 : \n mean =  20.3 \n sd   =  2.2 \nIteration 58 : \n mean =  19.8 \n sd   =  2.3 \nIteration 59 : \n mean =  20.2 \n sd   =  2 \nIteration 60 : \n mean =  19.6 \n sd   =  2.1 \nIteration 61 : \n mean =  19.9 \n sd   =  1.9 \nIteration 62 : \n mean =  20.1 \n sd   =  1.9 \nIteration 63 : \n mean =  20.1 \n sd   =  2.3 \nIteration 64 : \n mean =  19.8 \n sd   =  2.1 \nIteration 65 : \n mean =  20 \n sd   =  2 \nIteration 66 : \n mean =  19.7 \n sd   =  1.9 \nIteration 67 : \n mean =  20.1 \n sd   =  2.1 \nIteration 68 : \n mean =  20.2 \n sd   =  2 \nIteration 69 : \n mean =  20.1 \n sd   =  2 \nIteration 70 : \n mean =  20.2 \n sd   =  2.1 \nIteration 71 : \n mean =  20.1 \n sd   =  2 \nIteration 72 : \n mean =  20.2 \n sd   =  2.1 \nIteration 73 : \n mean =  20.1 \n sd   =  2 \nIteration 74 : \n mean =  20 \n sd   =  2 \nIteration 75 : \n mean =  19.8 \n sd   =  2.2 \nIteration 76 : \n mean =  20.1 \n sd   =  2.2 \nIteration 77 : \n mean =  20.2 \n sd   =  1.5 \nIteration 78 : \n mean =  20.1 \n sd   =  2.2 \nIteration 79 : \n mean =  20.2 \n sd   =  2.1 \nIteration 80 : \n mean =  20.1 \n sd   =  2.1 \nIteration 81 : \n mean =  20 \n sd   =  1.8 \nIteration 82 : \n mean =  20.4 \n sd   =  2.1 \nIteration 83 : \n mean =  20.1 \n sd   =  1.9 \nIteration 84 : \n mean =  20.1 \n sd   =  2.1 \nIteration 85 : \n mean =  20.2 \n sd   =  2 \nIteration 86 : \n mean =  19.8 \n sd   =  2.1 \nIteration 87 : \n mean =  20.1 \n sd   =  2 \nIteration 88 : \n mean =  19.9 \n sd   =  2.1 \nIteration 89 : \n mean =  20 \n sd   =  1.9 \nIteration 90 : \n mean =  19.8 \n sd   =  2.2 \nIteration 91 : \n mean =  19.9 \n sd   =  2 \nIteration 92 : \n mean =  20 \n sd   =  2 \nIteration 93 : \n mean =  19.9 \n sd   =  2.2 \nIteration 94 : \n mean =  19.8 \n sd   =  1.8 \nIteration 95 : \n mean =  19.7 \n sd   =  1.8 \nIteration 96 : \n mean =  20.6 \n sd   =  1.8 \nIteration 97 : \n mean =  20.1 \n sd   =  2 \nIteration 98 : \n mean =  19.8 \n sd   =  1.9 \nIteration 99 : \n mean =  19.9 \n sd   =  1.9 \nIteration 100 : \n mean =  19.8 \n sd   =  1.8 \n> \n

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