r - 在 ezANOVA 函数中动态定义因变量和自变量-6ren

r - 在 ezANOVA 函数中动态定义因变量和自变量

转载作者：行者123 更新时间：2023-12-03 15:52:59

我想运行一个 ezANOVA来自 ez在循环中打包多个因变量并将结果保存到多个变量中。每个因变量位于同一数据框的单独列中。

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))
}

这是一个示例数据框:

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")

这种方法的问题在于 R 解释了索引 dependent.variable作为数据框中列的说明符 aov.data因此返回以下错误:

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

我试过用 eval() 包装索引或 print()但无济于事。

最佳答案

另一个不好的解决方案:

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

关于r - 在 ezANOVA 函数中动态定义因变量和自变量，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/12516260/

文章推荐： php - 为什么 PHP DOMDocument loadHTML 对波斯字符不起作用？

文章推荐：如果按不同索引排序，Delphi 7 TClientDataSet 聚合不起作用

文章推荐： delphi xe3 dll注入(inject)64位dll到64位进程不起作用

文章推荐：用于在包含标题文本的强标记后抓取文本数据的 xpath 语法

linux - compiler.h 文件中有 __cond_lock(x,c) 定义，但没有 __cond_unlock(x,c) 定义？
在complier.h中有一个宏定义如下: # define __cond_lock(x,c) ((c) ? ({ __acquire(x); 1; }) : 0) 但是这里我有一个问题，就是哪里
CURLOPT_ 定义
curl_easy_setopt 的选项在哪里？定义？我试图寻找 CURLOPT_VERBOSE 和其他一些整数值，但这些似乎没有在 curl.h 中明确定义。最佳答案第 792 行: #ifde
dllimport静态数据成员的C++定义
我确实有一个如下所示的类(class): //.h file class __declspec(dllimport) MyClass { public: //stuff pri
关系代数与逻辑优化规则(一):定义
作者: zhuwenzhuang, 2024.05.08. 阅读前假设读者熟悉数据库使用,了解 SQL 的语法和关系算子的大概含义, 能通过 EXPLAIN 命令查看数据库执行计划. 0 前言
Swagger header 定义
我似乎无法找到是否可以声明一个 header 对象以便在响应 header 中重用它，有一些示例定义了响应模式的对象，但它不会转置为响应 header 。我只设法制作了一个可重用的响应对象，如下所示:
CSS 选择器 * + * 定义？
css 选择器 * + * 实际上是什么意思？当您执行检查元素时，您可以在谷歌浏览器的控制台中看到它。在我看来，这似乎是对 "Every second child"应用一种风格，但仍然想确定。谁能帮我
Haskell primPutChar 定义
我试图弄清楚基本的IO Haskell 函数是定义好的，所以我使用了this reference我到了putChar函数定义: putChar :: Char -> IO () putChar
.net - TargetFrameworkAttribute 定义
我得到了一个自动生成的文件，该文件定义了程序集属性，我正在尝试理解内容。 [assembly: global::System.Runtime.Versioning.TargetFrameworkAtt
gnuplot，检查函数是否存在(定义)
This文档演示了如何检查变量是否先前已在 gnuplot 脚本中定义。文档中的示例: a = 10 if (exists("a")) print "a is defined" if (!exist
scheme - 定义、让和设置之间的区别!
好吧，这是一个相当基本的问题:我正在关注 SICP 视频，我对 define、let 和之间的区别有点困惑设置!. 1) 根据 Sussman 在视频中的说法，define 只允许为变量附加一个值一
枚举值的 XSD 定义
我一直在尝试定义一个包含只能具有以下三个值之一的字段的 XSD: 绿色红色蓝色本质上，我想在架构级别定义严格的枚举。我的第一次尝试似乎是错误的，我不确定修复它的“正确”方法。
class - “POCO”定义
有人可以定义“POCO”到底是什么意思吗？我越来越频繁地遇到这个术语，我想知道它是否仅与普通类有关还是意味着更多？最佳答案 “普通旧式 C# 对象” 只是一个普通的类，没有描述基础结构问题或域对象不
django CharField 定义
在我经常看到的一些django模型中 myfield = models.CharField(_('myfield')) class_name = models.CharField(_('Type'),
c - boolean 定义
每当 BOOL 数据类型不容易预定义时，我都会使用以下定义进行 boolean 运算， typedef unsigned char BOOL; (由于内存使用)。我意识到出于性能原因，使用本地总线宽
Java: vector 定义
l_ABC_BEANVector = utilRemote.fnGetVector("ABC_COVBEANVector"); 编码的含义是什么？任何帮助，我真的很感激。谢谢最佳答案唯一可以肯定地
JAVACC token 定义
我正在使用 javacc 开发一个项目，我遇到问题并需要一些帮助，我的文件中有这样的内容: STRING COPYRIGHT (C) 2003, 2004 SYNOPSYS, INC.; 我为单词 S
Haskell primPutChar 定义
我想弄清楚基本的 IO定义了 Haskell 函数，所以我使用了 this reference然后我到了 putChar函数定义: putChar :: Char -> IO () putCha
python - 定义@property
我在具体类中使用 @property 定义 getter 时遇到问题。这是Python代码: from abc import ABCMeta, abstractproperty class abstr
C 定义/全局变量依赖于其他东西
我正在为大学用 C 语言编写一个小游戏，但我陷入了困境。我(在头文件中)有这个结构: typedef struct{ game_element field[MAX_ROWS][MAX_COLU
c - 如何从flex文件中读取规则(定义)？
我一直在 .l 文件中创建标记定义。由于数据集数量庞大，它变得有点乏味。有没有办法读取文件中的所有单词，例如包含所有名词的 noun.txt 并给所有名词一个标记。基本上，我想自动化这部分: %%

行者123

个人简介

我是一名优秀的程序员,十分优秀！

作者热门文章

滴滴打车优惠券免费领取

全站热门文章

首页

博学

6Ren·AI

商城

r - 在 ezANOVA 函数中动态定义因变量和自变量