我正在尝试使用此source("https://www.r-statistics.com/wp-content/uploads/2010/02/Friedman-Test-with-Post-Hoc.r.txt")中的代码在我的数据上运行Friedman测试的事后测试。
当我运行我的数据代码时：
friedman.test.with.post.hoc(Rating ~ weeks | Student ,Qr)
我收到错误：Error in .local(.Object, ...) : ‘x’ is not a balanced factor
我不确定它是指什么，但我已经检查了我的数据缺失数据点等...
有谁知道这可能意味着什么或我如何解决它？
请查看我的数据。
干杯！
> dput(Qr)
structure(list(Student = c(789331L, 805933L, 826523L, 832929L,
838607L, 826523L, 832929L, 838607L, 841903L, 843618L, 852125L,
876406L, 879972L, 885650L, 888712L, 903303L, 957206L, 957759L,
959200L, 968728L, 971179L, 952797L, 965873L, 967416L, 789331L,
826523L, 826523L, 843618L, 852125L, 876406L, 879972L, 885650L,
888712L, 903303L, 796882L, 831487L, 834598L, 836364L, 839802L,
855524L, 873527L, 885409L, 894218L, 956952L, 957206L, 957759L,
959200L, 962490L, 968728L, 969005L, 976863L, 981621L, 952797L,
965873L, 975424L, 789331L, 805933L, 826523L, 838607L, 826523L,
838607L, 841903L, 843618L, 852125L, 876406L, 888712L, 903303L,
796882L, 830271L, 831487L, 834598L, 836364L, 839802L, 855524L,
873527L, 885409L, 894218L, 957206L, 957759L, 968728L, 969005L,
971179L, 981621L, 952797L, 789331L, 805933L, 826523L, 832929L,
838607L, 826523L, 832929L, 838607L, 841903L, 843618L, 876406L,
879972L, 885650L, 888712L, 903303L, 796882L, 827911L, 830271L,
831487L, 834598L, 836364L, 839802L, 855524L, 873527L, 885409L,
894218L, 928026L, 932196L, 955389L, 956952L, 957206L, 957759L,
959200L, 962490L, 968728L, 969005L, 971179L, 976863L, 981621L,
975424L, 789331L, 805933L, 826523L, 832929L, 838607L, 826523L,
832929L, 838607L, 841903L, 843618L, 852125L, 876406L, 879972L,
885650L, 903303L, 796882L, 827911L, 830271L, 831487L, 834598L,
836364L, 839802L, 855524L, 873527L, 885409L, 894218L, 928026L,
955389L, 957206L, 957759L, 962490L, 968728L, 969005L, 971179L,
981621L, 965873L, 967416L, 975424L, 789331L, 805933L, 826523L,
832929L, 838607L, 826523L, 832929L, 838607L, 843618L, 852125L,
876406L, 885650L, 888712L, 903303L, 796882L, 830271L, 831487L,
834598L, 836364L, 839802L, 855524L, 873527L, 885409L, 894218L,
957759L, 962490L, 968728L, 969005L, 976863L, 952797L, 965873L,
789331L, 805933L, 826523L, 832929L, 838607L, 826523L, 832929L,
838607L, 841903L, 843618L, 852125L, 876406L, 879972L, 885650L,
888712L, 796882L, 827911L, 830271L, 831487L, 834598L, 836364L,
839802L, 855524L, 873527L, 885409L, 894218L, 955389L, 957206L,
957759L, 959200L, 962490L, 968728L, 971179L, 981621L, 952797L,
965873L, 967416L, 789331L, 805933L, 826523L, 838607L, 826523L,
838607L, 843618L, 852125L, 876406L, 879972L, 885650L, 888712L,
903303L, 796882L, 827911L, 830271L, 834598L, 836364L, 839802L,
855524L, 885409L, 894218L, 956952L, 957206L, 957759L, 959200L,
962490L, 968728L, 981621L, 952797L, 965873L, 967416L, 789331L,
805933L, 838607L, 838607L, 841903L, 843618L, 852125L, 876406L,
879972L, 885650L, 888712L, 903303L, 796882L, 830271L, 831487L,
834598L, 836364L, 839802L, 855524L, 873527L, 885409L, 894218L,
955389L, 956952L, 957206L, 957759L, 968728L, 969005L, 971179L,
976863L, 981621L, 952797L, 965873L, 967416L, 975424L, 789331L,
805933L, 826523L, 838607L, 826523L, 838607L, 841903L, 843618L,
852125L, 876406L, 879972L, 885650L, 888712L, 903303L, 796882L,
831487L, 836364L, 839802L, 855524L, 873527L, 885409L, 955389L,
956952L, 957206L, 957759L, 959200L, 962490L, 968728L, 969005L,
971179L, 976863L, 981621L, 952797L, 965873L, 967416L, 789331L,
838607L, 838607L, 841903L, 843618L, 876406L, 879972L, 885650L,
888712L, 903303L, 796882L, 830271L, 831487L, 836364L, 839802L,
855524L, 955389L, 957206L, 957759L, 962490L, 968728L, 965873L
), Type = c("SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
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"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
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"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS",
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"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "FYS",
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"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "FYS",
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "FYS", "FYS", "FYS", "FYS",
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS",
"FYS", "FYS", "FYS", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "FYS", "FYS",
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS",
"FYS", "FYS", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "FYS", "FYS", "FYS", "FYS",
"FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS", "FYS",
"FYS", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR",
"SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "SNR", "FYS",
"FYS", "FYS", "FYS", "FYS", "FYS"), timePeriod = c("Rt1", "Rt1",
"Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1",
"Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1", "Rt1",
"Rt1", "Rt1", "Rt1", "Rt1", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10",
"Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10",
"Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10",
"Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10", "Rt10",
"Rt10", "Rt10", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11",
"Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11",
"Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11",
"Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt11", "Rt2",
"Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2",
"Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2",
"Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2",
"Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2", "Rt2",
"Rt2", "Rt2", "Rt2", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3",
"Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3",
"Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3",
"Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt3",
"Rt3", "Rt3", "Rt3", "Rt3", "Rt3", "Rt4", "Rt4", "Rt4", "Rt4",
"Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4",
"Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4",
"Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4", "Rt4",
"Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5",
"Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5",
"Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5",
"Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5", "Rt5",
"Rt5", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6",
"Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6",
"Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6",
"Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt6", "Rt7", "Rt7", "Rt7",
"Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7",
"Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7",
"Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt7",
"Rt7", "Rt7", "Rt7", "Rt7", "Rt7", "Rt8", "Rt8", "Rt8", "Rt8",
"Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8",
"Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8",
"Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8", "Rt8",
"Rt8", "Rt8", "Rt8", "Rt8", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9",
"Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9",
"Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9", "Rt9"), weeks = structure(c(5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
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4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L), .Label = c("Apr5", "Feb1", "Feb15", "Feb8", "Jan11",
"Jan25", "Mar1", "Mar15", "Mar22", "Mar29", "Mar8"), class = "factor"),
Rating = c(4L, 5L, 4L, 4L, 5L, 4L, 4L, 5L, 2L, 4L, 3L, 3L,
3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 5L, 4L, 1L, 4L, 5L, 5L, 5L,
5L, 4L, 4L, 4L, 5L, 5L, 3L, 3L, 5L, 4L, 3L, 1L, 4L, 4L, 4L,
4L, 5L, 4L, 4L, 4L, 4L, 1L, 5L, 4L, 5L, 4L, 5L, 3L, 5L, 4L,
4L, 4L, 4L, 4L, 3L, 5L, 3L, 4L, 5L, 4L, 5L, 5L, 5L, 4L, 4L,
4L, 5L, 4L, 4L, 5L, 5L, 5L, 1L, 5L, 5L, 5L, 4L, 4L, 4L, 3L,
4L, 5L, 3L, 4L, 5L, 4L, 4L, 4L, 3L, 4L, 4L, 1L, 3L, 5L, 4L,
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5L, 3L, 4L, 4L, 2L, 5L, 4L, 2L, 5L, 4L, 5L, 4L, 4L, 4L, 4L,
4L, 3L, 3L, 5L, 4L, 4L, 3L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L,
4L, 5L, 3L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 5L, 4L, 5L, 5L,
3L, 4L, 4L, 4L, 4L, 3L, 5L, 4L, 5L, 4L, 3L, 2L, 4L, 4L, 5L,
3L, 5L, 4L, 3L, 4L, 3L, 5L, 4L, 4L, 4L, 5L, 4L, 5L, 5L, 4L,
5L, 3L, 4L, 4L, 5L, 5L, 3L, 4L, 5L, 4L, 4L, 3L, 3L, 5L, 4L,
4L, 5L, 3L, 5L, 4L, 4L, 1L, 5L, 5L, 4L, 5L, 4L, 3L, 5L, 3L,
4L, 4L, 5L, 5L, 5L, 5L, 3L, 5L, 4L, 4L, 4L, 5L, 4L, 3L, 1L,
5L, 3L, 4L, 4L, 4L, 5L, 3L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 5L,
4L, 5L, 5L, 3L, 5L, 4L, 4L, 4L, 4L, 4L, 3L, 4L, 5L, 5L, 3L,
3L, 5L, 5L, 3L, 3L, 5L, 4L, 5L, 4L, 1L, 2L, 4L)), .Names = c("Student",
"Type", "timePeriod", "weeks", "Rating"), row.names = c(1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
32L, 33L, 34L, 36L, 38L, 41L, 42L, 43L, 45L, 47L, 50L, 54L, 55L,
56L, 57L, 58L, 59L, 60L, 61L, 64L, 65L, 66L, 67L, 68L, 69L, 70L,
71L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 83L, 84L, 85L, 86L, 88L,
89L, 90L, 91L, 93L, 94L, 96L, 97L, 98L, 99L, 100L, 103L, 104L,
105L, 107L, 108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 120L,
121L, 124L, 125L, 126L, 128L, 129L, 133L, 134L, 135L, 136L, 137L,
138L, 139L, 140L, 141L, 142L, 144L, 145L, 146L, 147L, 148L, 149L,
150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L,
161L, 162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L,
172L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L, 184L, 185L,
186L, 187L, 188L, 189L, 190L, 192L, 193L, 194L, 195L, 196L, 197L,
198L, 199L, 200L, 201L, 202L, 203L, 204L, 206L, 208L, 209L, 211L,
212L, 213L, 214L, 216L, 218L, 219L, 220L, 221L, 222L, 223L, 224L,
225L, 226L, 227L, 228L, 230L, 231L, 232L, 234L, 235L, 236L, 237L,
239L, 240L, 241L, 242L, 243L, 244L, 245L, 246L, 247L, 253L, 255L,
256L, 257L, 259L, 261L, 262L, 265L, 266L, 267L, 268L, 269L, 270L,
271L, 272L, 273L, 274L, 275L, 276L, 277L, 278L, 279L, 281L, 282L,
283L, 284L, 285L, 286L, 287L, 288L, 289L, 290L, 291L, 294L, 296L,
297L, 298L, 299L, 300L, 302L, 304L, 305L, 306L, 307L, 309L, 310L,
311L, 313L, 314L, 316L, 318L, 319L, 320L, 321L, 322L, 323L, 324L,
325L, 326L, 327L, 329L, 330L, 331L, 332L, 334L, 335L, 339L, 340L,
341L, 342L, 343L, 344L, 348L, 349L, 350L, 351L, 353L, 354L, 357L,
360L, 361L, 362L, 363L, 364L, 365L, 366L, 367L, 368L, 369L, 371L,
372L, 373L, 374L, 375L, 376L, 377L, 378L, 379L, 382L, 383L, 384L,
385L, 388L, 389L, 390L, 391L, 392L, 393L, 394L, 395L, 396L, 397L,
398L, 399L, 401L, 402L, 404L, 405L, 406L, 407L, 408L, 409L, 410L,
411L, 412L, 413L, 416L, 418L, 419L, 420L, 421L, 422L, 426L, 427L,
428L, 429L, 430L, 431L, 432L, 433L, 434L, 435L, 436L, 437L, 438L,
439L, 441L, 445L, 448L, 449L, 450L, 452L, 453L, 454L, 455L, 456L,
457L, 459L, 460L, 462L, 463L, 464L, 470L, 472L, 473L, 475L, 476L,
482L), class = "data.frame", na.action = structure(c(17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
35L, 37L, 39L, 40L, 44L, 46L, 48L, 49L, 51L, 52L, 53L, 62L, 63L,
72L, 73L, 74L, 82L, 87L, 92L, 95L, 101L, 102L, 106L, 116L, 117L,
118L, 119L, 122L, 123L, 127L, 130L, 131L, 132L, 143L, 173L, 174L,
175L, 191L, 205L, 207L, 210L, 215L, 217L, 229L, 233L, 238L, 248L,
249L, 250L, 251L, 252L, 254L, 258L, 260L, 263L, 264L, 280L, 292L,
293L, 295L, 301L, 303L, 308L, 312L, 315L, 317L, 328L, 333L, 336L,
337L, 338L, 345L, 346L, 347L, 352L, 355L, 356L, 358L, 359L, 370L,
380L, 381L, 386L, 387L, 400L, 403L, 414L, 415L, 417L, 423L, 424L,
425L, 440L, 442L, 443L, 444L, 446L, 447L, 451L, 458L, 461L, 465L,
466L, 467L, 468L, 469L, 471L, 474L, 477L, 478L, 479L, 480L, 481L,
483L, 484L), .Names = c("17", "18", "19", "20", "21", "22", "23",
"24", "25", "26", "27", "28", "29", "30", "31", "35", "37", "39",
"40", "44", "46", "48", "49", "51", "52", "53", "62", "63", "72",
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"143", "173", "174", "175", "191", "205", "207", "210", "215",
"217", "229", "233", "238", "248", "249", "250", "251", "252",
"254", "258", "260", "263", "264", "280", "292", "293", "295",
"301", "303", "308", "312", "315", "317", "328", "333", "336",
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"415", "417", "423", "424", "425", "440", "442", "443", "444",
"446", "447", "451", "458", "461", "465", "466", "467", "468",
"469", "471", "474", "477", "478", "479", "480", "481", "483",
"484"), class = "omit"))
答案 0 :(得分：0)
问题在于您的数据。弗里德曼的测试类似于经典的平衡双因素方差分析，但您的数据并不均衡。
> xtabs(~ Student + weeks, data=Qr)
weeks
Student Apr5 Feb1 Feb15 Feb8 Jan11 Jan25 Mar1 Mar15 Mar22 Mar29 Mar8
789331 1 1 1 1 1 1 1 1 1 1 1
796882 1 1 1 1 0 1 1 1 1 1 1
805933 1 1 1 1 1 1 1 1 0 0 1
826523 2 2 2 2 2 2 2 2 0 2 0
827911 0 1 1 0 0 1 1 0 0 0 0
830271 1 1 1 1 0 1 1 0 1 0 1
831487 1 1 1 1 0 1 0 1 1 1 1
832929 0 2 2 2 2 2 0 0 0 0 0
834598 1 1 1 1 0 1 1 0 0 1 1
836364 1 1 1 1 0 1 1 1 1 1 1
838607 2 2 2 2 2 2 2 2 2 0 2
839802 1 1 1 1 0 1 1 1 1 1 1
841903 1 1 1 0 1 1 0 1 1 0 1
843618 1 1 1 1 1 1 1 1 1 1 1
852125 1 1 1 1 1 0 1 1 0 1 1
855524 1 1 1 1 0 1 1 1 1 1 1
873527 1 1 1 1 0 1 0 1 0 1 1
876406 1 1 1 1 1 1 1 1 1 1 1
879972 0 1 1 0 1 1 1 1 1 1 1
885409 1 1 1 1 0 1 1 1 0 1 1
885650 0 1 1 1 1 1 1 1 1 1 1
888712 1 0 1 1 1 1 1 1 1 1 1
894218 1 1 1 1 0 1 1 0 0 1 1
903303 1 1 0 1 1 1 1 1 1 1 1
928026 0 1 0 0 0 1 0 0 0 0 0
932196 0 0 0 0 0 1 0 0 0 0 0
952797 1 0 1 1 1 0 1 1 0 1 1
955389 0 1 1 0 0 1 0 1 1 0 1
956952 0 0 0 0 0 1 1 1 0 1 1
957206 1 1 1 0 1 1 1 1 1 1 1
957759 1 1 1 1 1 1 1 1 1 1 1
959200 0 0 1 0 1 1 1 1 0 1 0
962490 0 1 1 1 0 1 1 1 1 1 0
965873 0 1 1 1 1 0 1 1 1 1 1
967416 0 1 1 0 1 0 1 1 0 0 1
968728 1 1 1 1 1 1 1 1 1 1 1
969005 1 1 0 1 0 1 0 1 0 1 1
971179 1 1 1 0 1 1 0 1 0 0 1
975424 0 1 0 0 0 1 0 0 0 1 1
976863 0 0 0 1 0 1 0 1 0 1 1
981621 1 1 1 0 0 1 1 1 0 1 1
对于传统的弗里德曼测试，你应该对该矩阵中的所有条目都有相同的数字。