I am trying to conduct an ANOVA for a numeric column titled "duration" in the df "Low.Ambiguity" with factors of Variability and Mixed. Both Variability and Mixed are factors with two levels.
I am trying the following code:
# Load low ambiguity experiment .csv.
Low.Ambiguity <- read.csv("Low.Ambiguity.csv")
# Conduct ANOVA for low ambiguity experiment.
aov.ambiguity.low <- aov(Duration ~ Variability*Mixed, data=Low.Ambiguity)
summary(aov)
And receiving the following error message:
Error in object[[i]] : object of type 'closure' is not subsettable
Any insight into my mistake is appreciated!
EDIT -- also note that my df has more columns than I am interested in for this ANOVA:
> dput(Low.Ambiguity)
structure(list(Stimulus = structure(c(20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L,
52L, 2L, 7L, 12L, 9L, 19L, 15L, 24L, 22L, 4L, 6L, 8L, 11L, 17L,
16L, 35L, 26L, 2L, 7L, 12L, 9L, 19L, 15L, 24L, 22L, 4L, 6L, 8L,
11L, 17L, 16L, 35L, 26L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 2L, 3L,
12L, 13L, 19L, 14L, 24L, 45L, 5L, 1L, 8L, 10L, 17L, 18L, 26L,
51L, 2L, 3L, 12L, 13L, 19L, 14L, 24L, 45L, 5L, 1L, 8L, 10L, 17L,
18L, 26L, 51L), .Label = c("t1_block2_hoed3.mp3", "t1_block3_heed2.mp3",
"t1_block3_heed5.mp3", "t1_block3_hoed1.mp3", "t1_block3_hoed2.mp3",
"t1_block3_hoed4.mp3", "t1_block4_heed5.mp3", "t2_block1_hoed3.mp3",
"t2_block2_heed3.mp3", "t2_block2_hoed5.mp3", "t2_block3_hoed1.mp3",
"t2_block4_heed2.mp3", "t2_block4_heed5.mp3", "t3_block1_heed1.mp3",
"t3_block2_heed5.mp3", "t3_block2_hoed2.mp3", "t3_block3_hoed1.mp3",
"t3_block3_hoed4.mp3", "t3_block4_heed3.mp3", "t4_block1_heed1.mp3",
"t4_block1_heed2.mp3", "t4_block1_heed3.mp3", "t4_block1_heed4.mp3",
"t4_block1_heed5.mp3", "t4_block1_hoed1.mp3", "t4_block1_hoed2.mp3",
"t4_block1_hoed3.mp3", "t4_block1_hoed4.mp3", "t4_block1_hoed5.mp3",
"t4_block2_heed1.mp3", "t4_block2_heed2.mp3", "t4_block2_heed4.mp3",
"t4_block2_heed5.mp3", "t4_block2_hoed1.mp3", "t4_block2_hoed3.mp3",
"t4_block2_hoed4.mp3", "t4_block2_hoed5.mp3", "t4_block3_heed1.mp3",
"t4_block3_heed4.mp3", "t4_block3_heed5.mp3", "t4_block3_hoed1.mp3",
"t4_block3_hoed2.mp3", "t4_block3_hoed4.mp3", "t4_block3_hoed5.mp3",
"t4_block4_heed1.mp3", "t4_block4_heed2.mp3", "t4_block4_heed3.mp3",
"t4_block4_heed4.mp3", "t4_block4_heed5.mp3", "t4_block4_hoed1.mp3",
"t4_block4_hoed2.mp3", "t4_block4_hoed3.mp3"), class = "factor"),
Duration = c(539L, 554L, 600L, 607L, 577L, 497L, 517L, 580L,
563L, 569L, 594L, 563L, 623L, 602L, 516L, 600L, 607L, 577L,
531L, 642L, 624L, 566L, 567L, 616L, 652L, 654L, 576L, 556L,
608L, 632L, 662L, 565L, 510L, 458L, 558L, 638L, 483L, 538L,
577L, 600L, 452L, 547L, 510L, 663L, 470L, 503L, 600L, 517L,
510L, 458L, 558L, 638L, 483L, 538L, 577L, 600L, 452L, 547L,
510L, 663L, 470L, 503L, 600L, 517L, 539L, 554L, 600L, 607L,
577L, 497L, 517L, 580L, 563L, 569L, 594L, 563L, 623L, 602L,
516L, 600L, 607L, 577L, 531L, 642L, 624L, 566L, 567L, 616L,
652L, 654L, 576L, 556L, 608L, 632L, 662L, 565L, 510L, 545L,
558L, 488L, 483L, 555L, 577L, 661L, 504L, 537L, 510L, 609L,
470L, 515L, 517L, 662L, 510L, 545L, 558L, 488L, 483L, 555L,
577L, 661L, 504L, 537L, 510L, 609L, 470L, 515L, 517L, 662L
), F0 = c(212L, 213L, 210L, 206L, 204L, 196L, 204L, 204L,
197L, 203L, 201L, 198L, 199L, 203L, 196L, 202L, 205L, 202L,
193L, 195L, 208L, 197L, 202L, 195L, 200L, 201L, 195L, 205L,
202L, 195L, 196L, 195L, 230L, 223L, 219L, 221L, 199L, 200L,
204L, 210L, 215L, 219L, 219L, 220L, 199L, 202L, 202L, 204L,
230L, 223L, 219L, 221L, 199L, 200L, 204L, 210L, 215L, 219L,
219L, 220L, 199L, 202L, 202L, 204L, 212L, 213L, 210L, 206L,
204L, 196L, 204L, 204L, 197L, 203L, 201L, 198L, 199L, 203L,
196L, 202L, 205L, 202L, 193L, 195L, 208L, 197L, 202L, 195L,
200L, 201L, 195L, 205L, 202L, 195L, 196L, 195L, 230L, 235L,
219L, 224L, 199L, 203L, 204L, 196L, 214L, 221L, 219L, 223L,
199L, 207L, 204L, 196L, 230L, 235L, 219L, 224L, 199L, 203L,
204L, 196L, 214L, 221L, 219L, 223L, 199L, 207L, 204L, 196L
), F1 = c(382L, 375L, 384L, 392L, 388L, 576L, 553L, 579L,
586L, 601L, 387L, 393L, 402L, 406L, 587L, 560L, 562L, 553L,
388L, 391L, 412L, 553L, 592L, 556L, 571L, 410L, 404L, 401L,
420L, 580L, 580L, 554L, 448L, 441L, 293L, 291L, 420L, 420L,
388L, 384L, 620L, 630L, 602L, 605L, 571L, 573L, 560L, 553L,
448L, 441L, 293L, 291L, 420L, 420L, 388L, 384L, 620L, 630L,
602L, 605L, 571L, 573L, 560L, 553L, 382L, 375L, 384L, 392L,
388L, 576L, 553L, 579L, 586L, 601L, 387L, 393L, 402L, 406L,
587L, 560L, 562L, 553L, 388L, 391L, 412L, 553L, 592L, 556L,
571L, 410L, 404L, 401L, 420L, 580L, 580L, 554L, 448L, 324L,
293L, 291L, 420L, 422L, 388L, 403L, 619L, 640L, 602L, 654L,
571L, 581L, 553L, 580L, 448L, 324L, 293L, 291L, 420L, 422L,
388L, 403L, 619L, 640L, 602L, 654L, 571L, 581L, 553L, 580L
), F2 = c(3001L, 2916L, 2948L, 2973L, 2947L, 1339L, 1381L,
1381L, 1347L, 1394L, 2943L, 2913L, 2987L, 2940L, 1353L, 1378L,
1325L, 1357L, 3010L, 3008L, 2972L, 1350L, 1397L, 1273L, 1319L,
2963L, 2991L, 3007L, 2989L, 1358L, 1347L, 1248L, 2837L, 2816L,
2780L, 2776L, 2684L, 2718L, 2947L, 2948L, 1247L, 1244L, 1293L,
1264L, 1348L, 1354L, 1378L, 1381L, 2837L, 2816L, 2780L, 2776L,
2684L, 2718L, 2947L, 2948L, 1247L, 1244L, 1293L, 1264L, 1348L,
1354L, 1378L, 1381L, 3001L, 2916L, 2948L, 2973L, 2947L, 1339L,
1381L, 1381L, 1347L, 1394L, 2943L, 2913L, 2987L, 2940L, 1353L,
1378L, 1325L, 1357L, 3010L, 3008L, 2972L, 1350L, 1397L, 1273L,
1319L, 2963L, 2991L, 3007L, 2989L, 1358L, 1347L, 1248L, 2837L,
2884L, 2780L, 2723L, 2684L, 2779L, 2947L, 3058L, 1205L, 1304L,
1293L, 1186L, 1348L, 1392L, 1381L, 1347L, 2837L, 2884L, 2780L,
2723L, 2684L, 2779L, 2947L, 3058L, 1205L, 1304L, 1293L, 1186L,
1348L, 1392L, 1381L, 1347L), F3 = c(3623L, 3607L, 3584L,
3576L, 3680L, 2831L, 2779L, 2915L, 2875L, 2712L, 3641L, 3590L,
3556L, 3584L, 2718L, 2856L, 2674L, 2659L, 3640L, 3656L, 3686L,
2726L, 2685L, 2866L, 2793L, 3516L, 3552L, 3513L, 3579L, 2932L,
2882L, 2882L, 3226L, 3121L, 3867L, 3319L, 3426L, 3269L, 3680L,
3357L, 2711L, 3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L,
3226L, 3121L, 3867L, 3319L, 3426L, 3269L, 3680L, 3357L, 2711L,
3129L, 2786L, 2833L, 2754L, 2771L, 2856L, 2779L, 3623L, 3607L,
3584L, 3576L, 3680L, 2831L, 2779L, 2915L, 2875L, 2712L, 3641L,
3590L, 3556L, 3584L, 2718L, 2856L, 2674L, 2659L, 3640L, 3656L,
3686L, 2726L, 2685L, 2866L, 2793L, 3516L, 3552L, 3513L, 3579L,
2932L, 2882L, 2882L, 3226L, 3315L, 3867L, 3722L, 3426L, 3464L,
3680L, 3642L, 2922L, 2735L, 2786L, 2880L, 2754L, 2861L, 2779L,
2882L, 3226L, 3315L, 3867L, 3722L, 3426L, 3464L, 3680L, 3642L,
2922L, 2735L, 2786L, 2880L, 2754L, 2861L, 2779L, 2882L),
Word = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("heed",
"hoed"), class = "factor"), Vowel = structure(c(1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L), .Label = c("i", "o"), class = "factor"),
F1.Mean = c(398L, 398L, 398L, 398L, 398L, 564L, 564L, 564L,
564L, 564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 564L,
398L, 398L, 398L, 564L, 564L, 564L, 564L, 398L, 398L, 398L,
398L, 564L, 564L, 564L, 397L, 397L, 292L, 292L, 417L, 417L,
398L, 398L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L,
397L, 397L, 292L, 292L, 417L, 417L, 398L, 398L, 627L, 627L,
614L, 614L, 614L, 614L, 566L, 566L, 398L, 398L, 398L, 398L,
398L, 564L, 564L, 564L, 564L, 564L, 398L, 398L, 398L, 398L,
564L, 564L, 564L, 564L, 398L, 398L, 398L, 564L, 564L, 564L,
564L, 398L, 398L, 398L, 398L, 564L, 564L, 564L, 397L, 397L,
292L, 292L, 417L, 417L, 398L, 398L, 627L, 627L, 614L, 614L,
614L, 614L, 566L, 566L, 397L, 397L, 292L, 292L, 417L, 417L,
398L, 398L, 627L, 627L, 614L, 614L, 614L, 614L, 566L, 566L
), F2.Mean = c(2969L, 2969L, 2969L, 2969L, 2969L, 1328L,
1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L,
1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L,
1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L, 2828L,
2828L, 2763L, 2763L, 2721L, 2721L, 2969L, 2969L, 1250L, 1250L,
1247L, 1247L, 1247L, 1247L, 1357L, 1357L, 2828L, 2828L, 2763L,
2763L, 2721L, 2721L, 2969L, 2969L, 1250L, 1250L, 1247L, 1247L,
1247L, 1247L, 1357L, 1357L, 2969L, 2969L, 2969L, 2969L, 2969L,
1328L, 1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 2969L,
1328L, 1328L, 1328L, 1328L, 2969L, 2969L, 2969L, 1328L, 1328L,
1328L, 1328L, 2969L, 2969L, 2969L, 2969L, 1328L, 1328L, 1328L,
2828L, 2828L, 2763L, 2763L, 2721L, 2721L, 2969L, 2969L, 1250L,
1250L, 1247L, 1247L, 1247L, 1247L, 1357L, 1357L, 2828L, 2828L,
2763L, 2763L, 2721L, 2721L, 2969L, 2969L, 1250L, 1250L, 1247L,
1247L, 1247L, 1247L, 1357L, 1357L), Distance = c(36L, 58L,
25L, 7L, 24L, 16L, 54L, 55L, 29L, 76L, 28L, 56L, 18L, 30L,
34L, 50L, 4L, 31L, 42L, 40L, 14L, 25L, 74L, 56L, 11L, 13L,
23L, 38L, 30L, 34L, 25L, 81L, 52L, 46L, 17L, 13L, 37L, 4L,
24L, 25L, 8L, 7L, 48L, 19L, 110L, 115L, 22L, 27L, 52L, 46L,
17L, 13L, 37L, 4L, 24L, 25L, 8L, 7L, 48L, 19L, 110L, 115L,
22L, 27L, 36L, 58L, 25L, 7L, 24L, 16L, 54L, 55L, 29L, 76L,
28L, 56L, 18L, 30L, 34L, 50L, 4L, 31L, 42L, 40L, 14L, 25L,
74L, 56L, 11L, 13L, 23L, 38L, 30L, 34L, 25L, 81L, 52L, 92L,
17L, 40L, 37L, 58L, 24L, 89L, 46L, 56L, 48L, 73L, 110L, 36L,
27L, 17L, 52L, 92L, 17L, 40L, 37L, 58L, 24L, 89L, 46L, 56L,
48L, 73L, 110L, 36L, 27L, 17L), Included = 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), .Label = "Yes", class = "factor"),
Talker = structure(c(4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L,
4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L,
4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 2L,
2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L,
2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), .Label = c("T1 ",
"T2", "T3", "T4"), class = "factor"), Ambiguity = 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), .Label = "Low", class = "factor"),
Variability = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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, 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, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Mixed", "Single"), class = "factor"), Mixed = 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Consistent", "Inconsistent"
), class = "factor")), class = "data.frame", row.names = c(NA,
-128L))
