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在ezANOVA函數中動態定義相關變量和自變量R

2012-09-20 72 views
4

我想從ez程序包中運行ezANOVA包中的多個因變量，並將結果保存到多個變量中。每個因變量位於同一數據框的單獨列中。在ezANOVA函數中動態定義相關變量和自變量R

all.dependent.variables <- c("dv1", "dv2") 

for(dependent.variable in all.dependent.variables){ 
    assign(paste(dependent.variable, ".aov.results", sep = ""), 
    ezANOVA(aov.data, 
     dv = dependent.variable, 
     wid = subject, 
     within = .(factor1, factor2), 
     return_aov = TRUE)) 
}

這是一個示例數據幀：

aov.data <- structure(list(subject = structure(c(10L, 11L, 12L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L), .Label = c("1", "2", 
"3", "4", "5", "6", "7", "8", "9", "10", "11", "12"), class = "factor"), 
dv1 = c(650.2, 773.7, 686.4, 436.2, 625.3, 714.2, 892.6, 921.5, 
711.2, 670.2, 725.8, 592.8, 672.7, 731.1, 707.2, 475.1, 645.4, 
786.7, 949.5, 925.8, 715.5, 745.4, 750.8, 579.1, 683.3, 707.6, 
693.7, 492.4, 698.8, 666.9, 914.4, 853.8, 724.4, 718.8, 872.9, 
616.9, 706.4, 766.2, 676.2, 500, 753.8, 712.7, 1012.2, 947.8, 
695.3, 735.6, 843.7, 596.1, 738.3, 705.2, 718.2, 534.1, 805.3, 
814.1, 969.4, 1010.7, -999, 714.4, 815.4, 645.4, 835.4, 830.7, 
776.7, 543.7, 757.2, 841.5, 1107.8, 915.8, -999, 707.4, 809.7, 
671.1, 638.1, 726.7, 660.2, 455.7, 623.5, 716.1, 922.1, 804.5, 
718.2, 674.6, 797.4, 572, 676.7, 726.6, 690.7, 498.8, 624.3, 
764.1, 889.5, 823.4, 672.9, 701.8, 750.4, 557.2, 656.1, 701, 
655.1, 472.7, 658.8, 660.6, 860.9, 811.3, 672.5, 681.7, 849.6, 
571.2, 694, 777.5, 661.3, 488, 670.4, 725.3, 938.1, 862.7, 
616.4, 732.2, 845.9, 582.4, 694.2, 694.6, 743.8, 480.5, 736.7, 
740.9, 988.1, 827.5, 812.4, 725.5, 844.2, 628, 779.3, 770.3, 
686.9, 494.3, 681.5, 850.5, 990.7, 810.1, 692.3, 779.7, 779.8, 
590.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
618.6, 713.9, 609.6, 468.6, 554.3, 580.8, 864.1, 843.3, 662.8, 
645.5, 714.6, 555.5, 670.7, 759.3, 652.2, 468.1, 613.5, 712.3, 
910.7, 782.4, 723.3, 742.8, 775.5, 553.2, 695.3, 726.2, 591.2, 
479.2, 626.1, 643.3, 821.5, 753.9, 818.2, 655.8, 754.4, 592.9, 
703.5, 792.5, 635, 485.3, 644.1, 667.9, 891.3, 780.9, 699.1, 
725.1, 716, 587.2, 706.5, 754.6, 694.3, 485.5, 745.5, 649.3, 
808.4, 780.5, 773.8, 676.3, 687.5, 685.3, 910.4, 821.5, 738.5, 
525, 689.6, 758.4, 1021.5, 792, 789.3, 740.5, 722.8, 717.1, 
653.3, 743.6, 620.3, 460.1, 575.3, 647.1, 849.3, 647, 691.2, 
596.4, 714.6, 531.6, 678.7, 754.7, 600.4, 463.8, 560.8, 636.6, 
844.3, 766.6, 725.3, 628.7, 784.4, 547.9, 630.3, 656.7, 705.3, 
443.3, 607, 630.7, 861.5, 754.4, 770.8, 664.5, 728.3, 546.4, 
741.5, 694, 620.4, 459.3, 587.9, 626.2, 893.8, 756.1, 731.8, 
680.2, 836.4, 566.7, 619.4, 686.4, 704, 445.3, 652.7, 735.3, 
839.4, 833.4, 763.7, 614.5, 794.4, 562.5, 713.2, 735.4, 655.4, 
501.1, 635.6, 661.2, 880.6, 747.8, 807.8, 757.7, 772.4, 560.1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 662.7, 
682.5, 590.1, 443.6, 623.2, 656.6, 852.6, 676.8, 646.6, 646, 
677.6, 518, 664, 665.4, 609.8, 464.2, 696.5, 661, 894.7, 
661.1, 659.6, 657.8, 713.4, 531.5, 739.5, 695.1, 656.5, 498.4, 
648.1, 710, 897.8, 685, 671.3, 657, 767.5, 545.3, 808.6, 
697.5, 667.2, 463.4, 695.3, 652.4, 857.2, 690.3, 766.1, 696.1, 
690.5, 558.8, 746, 708.4, 690, 515.5, 788.8, 929.6, 802.1, 
619.5, 510.8, 654.1, 811.8, 706.5, 977.8, 697.9, 700.9, 497.9, 
700.9, 811.5, 969.3, 723, 886, 815.7, 757.5, 639.5, 688.4, 
704, 617.8, 435.2, 628.8, 603.3, 865.3, 661.6, 645.9, 598.1, 
646.8, 477.2, 646.8, 760.8, 634.3, 452.2, 600.1, 648.2, 923.8, 
625.5, 676.9, 647.3, 688.6, 513.2, 591.9, 641.6, 632.6, 469.6, 
606.4, 610.9, 835.1, 667.8, 599.7, 581.2, 704.4, 502.9, 746.9, 
684.1, 689.3, 475.9, 692, 689, 824.9, 625.9, 696.4, 706.3, 
715.3, 510.9, 650.9, 640.1, 663.5, 471.6, 682.3, 683.2, 831.9, 
702, 685, 624.6, 698.2, 521.6, 759.8, 730.6, 661.1, 473.2, 
644.4, 738.7, 932.5, 685.1, 816, 722.1, 783.3, 526.2, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), dv2 = c(2.941, 
2.941, 5.882, 0, 0, 2.941, 8.824, 23.529, 35.294, 8.824, 
17.647, 5.882, 2.941, 2.941, 2.941, 0, 0, 8.824, 8.824, 44.118, 
29.412, 8.824, 2.941, 5.882, 5.882, 0, 5.882, 5.882, 0, 2.941, 
23.529, 20.588, 17.647, 8.824, 8.824, 11.765, 2.941, 2.941, 
8.824, 17.647, 0, 5.882, 26.471, 14.706, 47.059, 0, 17.647, 
14.706, 23.529, 0, 23.529, 38.235, 17.647, 52.941, 61.765, 
55.882, 94.118, 5.882, 41.176, 55.882, 17.647, 23.529, 35.294, 
32.353, 20.588, 44.118, 55.882, 44.118, 85.294, 17.647, 41.176, 
55.882, 0, 0, 0, 0, 0, 5.882, 5.882, 0, 29.412, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 5.882, 0, 32.353, 0, 5.882, 0, 0, 0, 0, 
0, 0, 2.941, 5.882, 0, 20.588, 0, 0, 0, 2.941, 0, 0, 0, 0, 
0, 11.765, 2.941, 35.294, 0, 0, 0, 2.941, 0, 5.882, 0, 0, 
20.588, 29.412, 8.824, 55.882, 0, 2.941, 5.882, 0, 0, 5.882, 
2.941, 2.941, 2.941, 14.706, 0, 52.941, 0, 11.765, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.941, 
0, 0, 5.882, 5.882, 29.412, 32.353, 5.882, 5.882, 0, 2.941, 
0, 5.882, 0, 0, 0, 11.765, 41.176, 23.529, 5.882, 5.882, 
0, 17.647, 5.882, 17.647, 2.941, 2.941, 8.824, 26.471, 26.471, 
44.118, 11.765, 11.765, 26.471, 2.941, 0, 5.882, 0, 0, 8.824, 
17.647, 23.529, 44.118, 2.941, 5.882, 11.765, 20.588, 5.882, 
17.647, 26.471, 17.647, 55.882, 47.059, 55.882, 61.765, 5.882, 
32.353, 55.882, 41.176, 2.941, 38.235, 35.294, 0, 76.471, 
50, 67.647, 76.471, 14.706, 55.882, 55.882, 5.882, 2.941, 
0, 0, 0, 0, 2.941, 0, 35.294, 0, 2.941, 0, 5.882, 0, 0, 0, 
0, 8.824, 8.824, 2.941, 20.588, 0, 0, 0, 5.882, 2.941, 0, 
0, 0, 0, 2.941, 0, 35.294, 2.941, 0, 0, 0, 0, 2.941, 0, 0, 
2.941, 0, 0, 38.235, 0, 2.941, 0, 8.824, 2.941, 0, 0, 0, 
32.353, 26.471, 17.647, 41.176, 2.941, 0, 0, 2.941, 2.941, 
5.882, 0, 0, 11.765, 8.824, 0, 55.882, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.941, 0, 5.882, 
0, 0, 0, 5.882, 11.765, 26.471, 8.824, 2.941, 2.941, 2.941, 
0, 8.824, 0, 0, 0, 11.765, 2.941, 11.765, 0, 8.824, 0, 8.824, 
2.941, 11.765, 5.882, 2.941, 2.941, 26.471, 20.588, 26.471, 
0, 2.941, 17.647, 11.765, 0, 2.941, 0, 2.941, 17.647, 20.588, 
20.588, 26.471, 8.824, 5.882, 11.765, 35.294, 14.706, 32.353, 
41.176, 5.882, 44.118, 52.941, 47.059, 73.529, 17.647, 50, 
47.059, 35.294, 11.765, 32.353, 50, 8.824, 73.529, 76.471, 
47.059, 82.353, 14.706, 47.059, 41.176, 0, 0, 0, 2.941, 0, 
0, 14.706, 2.941, 8.824, 0, 0, 0, 8.824, 2.941, 0, 0, 0, 
5.882, 0, 2.941, 2.941, 0, 5.882, 0, 8.824, 0, 2.941, 0, 
0, 2.941, 2.941, 2.941, 11.765, 5.882, 0, 0, 2.941, 0, 2.941, 
0, 0, 2.941, 2.941, 0, 17.647, 5.882, 2.941, 0, 5.882, 5.882, 
11.765, 0, 0, 8.824, 32.353, 0, 44.118, 2.941, 5.882, 0, 
2.941, 5.882, 11.765, 0, 0, 8.824, 17.647, 2.941, 50, 8.824, 
5.882, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0), factor1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L), .Label = c("level1", "level2", "level3"), class = "factor"), 
factor2 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("level1", "level2", "level3" 
), class = "factor")), .Names = c("subject", "dv1", "dv2", 
"factor1", "factor2"), row.names = c(NA, 540L), class = "data.frame")

這樣做的問題是，是R解釋索引dependent.variable作爲說明符在數據幀aov.data的柱，並因此返回以下錯誤：

"dependent.variable" is not a variable in the data frame provided.

我試過用eval()或print()包裝索引，但無濟於事。

來源

2012-09-20 crsh

+0

可以請你給我們一個可重複的例子？ http://tinyurl.com/reproducible-000。一般來說，如果你使用'assign（）'有更好的方法來做事情...... –

+0

感謝您的快速響應。我添加了一個數據框，它應該可以工作。 – crsh

+0

讓我補充一下，認爲我不認爲問題是由'assign（）'造成的。我試過了一個變體，其中所有結果都存儲在列表元素中（'aov.results [[dependent.variable]] < - ...'）。 – crsh

回答

3

好的，這絕對不是你的錯...... ezANOVA在內部做了一些聰明的評估東西，這些東西把你搞砸了。這似乎是一種可怕的咒語，但最好的做法是（1）聯繫軟件包維護人員，看他們是否能找到一種更優雅地修復內部問題的方法; （2）考慮是否可以用更標準方差分析評估不周圍做了這麼多的內部搞亂弄的......

fitlist <- lapply(all.dependent.variables, 
     function(x) { 
      e1 <- expression(ezANOVA(aov.data, 
       dv = x, 
       wid = subject, 
       within = .(factor1, factor2), 
       return_aov = TRUE)) 
      ## now force evaluation of the "dv" component of the call 
      e1[[1]][["dv"]] <- eval(e1[[1]][["dv"]]) 
      eval(e1) 
     })

來源

2012-09-20 16:42:47

+0

聰明嗎？如果它削弱了一般用途的功能，我會說它不那麼聰明。 –

+0

在這種情況下，聰明並不一定是讚美......我在某種程度上同情軟件包作者 - 一旦你開始做任何數量的非標準評估，無論（最初是好的）理由，它似乎總是回來咬你... –

+0

謝謝你，我會投你的答案，但我沒有足夠的聲譽這樣做。 ;） – crsh

3

另一個沒有很好的解決方案：

lapply(all.dependent.variables, function(i) { 
    eval(parse(text= 
    paste0('ezANOVA(data=aov.data, 
        dv=', i,', 
        wid=subject, 
        between=.(factor1, factor2), 
        return_aov = TRUE)') 
)) 
})

來源

2013-02-22 21:19:02

+0

這是一個令人傷心的解決方法，但我發現它比Ben的更清晰 – antoine

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