[英]Unexplained error with anova_test from rstatix
我在使用 package rstatix 運行重復測量方差分析時遇到問題。 我想運行一個重復測量方差分析，來自兩個治療組的患者在兩個不同的時間點報告了結果。
anova_test(data = test1, dv = outcome, wid = id, within = c(group, time))
但是，盡管數據集中沒有丟失數據，但我收到錯誤消息。
lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) 錯誤：0（非 NA）個案
我將不勝感激任何幫助！ 謝謝
數據：
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1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L), .Label = c("-1", "5"), class = "factor"),
outcome = c(5, 6, 16, 15, 8, 6, 15, 0, 5, 6, 6, 2, 8, 9,
11, 4, 13, 11, 13, 11, 6, 4, 10, 8, 18, 0, 11, 12, 10, 0,
7, 2, 10, 9, 10, 8, 10, 8, 21, 11, 6, 3, 25, 17, 6, 3, 11,
8, 7, 3, 14, 9, 16, 0, 10, 11, 7, 6, 13, 5, 12, 8, 8, 11,
5, 3, 11, 9, 13, 15, 10, 12, 16, 5, 27, 28, 7, 7, 11, 5,
26, 25, 7, 0, 6, 7, 5, 7, 16, 16, 9, 4, 18, 14, 6, 6, 22,
0, 10, 0, 7, 0, 10, 8, 7, 5, 8, 6, 7, 6, 11, 2, 6, 8, 24,
22, 8, 7, 9, 11, 14, 5, 10, 7, 9, 10, 5, 9, 13, 2, 10, 7,
16, 0, 18, 8, 8, 10, 21, 20, 5, 8, 6, 5, 9, 10, 7, 2, 14,
6, 10, 7, 9, 10, 9, 11, 5, 3, 11, 10, 5, 5, 16, 6, 8, 20,
5, 6, 6, 4, 5, 0, 10, 9, 6, 1, 9, 6, 17, 9, 14, 10, 11, 8,
12, 17, 10, 7, 12, 8, 7, 2, 27, 23, 22, 27, 12, 9, 6, 9,
10, 5, 9, 5, 6, 6, 20, 18, 11, 0, 9, 5, 5, 0, 23, 0, 15,
13, 13, 6, 24, 26, 24, 3, 10, 9, 12, 7, 17, 4, 14, 6, 5,
2, 12, 6, 7, 10, 6, 2, 10, 8, 14, 1, 10, 5, 8, 4, 10, 7,
19, 12, 11, 9, 0, 0, 8, 6, 11, 5, 6, 10, 14, 9, 18, 0, 9,
2, 20, 24, 8, 8, 10, 6, 21, 0, 8, 9, 5, 4, 27, 20, 10, 8,
8, 2, 28, 28, 8, 2, 11, 7, 10, 10, 8, 5, 10, 10, 10, 7, 8,
8, 6, 5, 7, 7, 16, 0, 21, 23, 11, 3, 10, 10, 7, 8, 5, 8,
5, 0, 6, 4, 6, 0, 10, 0, 9, 17, 7, 8, 10, 10, 6, 3, 9, 0,
12, 0, 18, 0, 11, 8, 13, 3, 10, 7, 9, 6, 6, 2, 12, 8, 7,
6, 15, 8, 20, 9, 18, 3, 13, 6, 16, 2, 10, 14, 6, 4, 5, 5,
10, 4, 6, 6, 10, 8, 11, 8, 9, 6, 5, 4, 7, 4, 9, 14, 12, 11,
15, 4, 14, 11, 9, 4, 11, 5, 13, 13, 5, 5, 6, 1, 6, 2, 6,
5, 8, 0, 17, 7, 13, 12, 10, 0, 6, 1, 13, 16, 7, 0, 6, 3,
10, 8, 12, 8, 9, 4, 16, 6, 26, 19, 7, 10, 14, 13, 12, 0,
8, 3, 12, 5, 8, 6, 7, 2, 9, 7, 22, 11, 17, 13, 12, 10, 8,
6, 11, 8, 9, 8, 12, 15, 5, 0, 5, 5, 28, 28, 9, 15, 12, 5,
8, 4, 10, 0, 7, 8, 8, 4, 7, 6, 10, 4, 7, 3, 10, 5, 15, 16,
16, 16, 9, 7, 7, 28, 7, 4, 13, 15, 10, 12, 14, 12, 13, 10,
7, 4, 13, 4, 4, 5, 17, 19, 14, 0, 15, 10, 7, 4, 4, 0, 12,
11, 12, 3, 9, 0, 7, 11, 14, 11, 12, 0, 23, 14, 7, 6, 28,
28, 12, 5, 12, 1, 5, 1, 13, 11, 9, 4, 6, 6, 10, 10, 7, 0,
10, 8, 12, 8, 7, 0, 5, 9, 7, 0, 5, 3, 4, 0, 14, 14, 7, 8,
18, 14, 11, 9, 13, 11, 9, 7, 5, 0, 8, 8, 9, 11, 10, 6, 10,
7, 9, 3, 14, 6, 5, 6, 28, 28, 20, 11, 7, 0, 11, 4, 7, 0,
13, 7, 11, 9, 11, 0, 16, 8, 8, 3, 19, 11, 14, 0, 6, 0, 21,
11, 7, 9, 8, 4, 17, 5, 11, 7, 6, 7, 8, 7, 12, 5, 22, 18,
11, 5, 18, 12, 8, 0, 8, 8, 16, 12, 5, 0, 9, 8, 18, 10, 15,
15, 7, 0, 19, 20, 16, 0, 15, 9, 5, 0, 11, 9, 8, 0, 25, 26,
10, 16, 13, 8, 21, 20, 10, 8, 10, 0, 15, 14, 13, 15, 19,
0, 10, 11, 14, 1, 25, 2, 12, 5, 5, 2, 5, 3, 5, 10, 12, 7,
8, 8, 25, 26, 10, 15, 9, 8, 10, 6, 3, 3, 13, 0, 16, 9, 24,
4, 16, 13, 14, 1, 12, 6, 7, 5, 9, 6, 11, 7, 5, 0, 12, 8,
6, 11, 17, 11, 22, 0, 1, 3, 25, 15, 9, 3, 7, 0, 6, 0, 10,
19, 7, 0, 5, 0, 7, 0, 10, 16, 15, 21, 8, 4, 28, 28, 3, 4,
17, 8, 3, 2, 18, 11, 9, 0, 8, 10, 1, 6, 16, 17, 3, 2, 6,
0, 6, 4, 13, 10, 11, 9, 2, 0, 13, 0, 2, 0, 5, 0, 7, 4, 10,
1, 11, 0, 12, 5, 12, 4, 9, 1, 11, 3, 2, 3, 11, 9, 4, 0, 11,
6, 13, 5, 5, 13, 21, 0, 8, 8, 9, 0, 10, 0, 5, 9, 11, 0, 6,
0, 9, 0, 12, 3, 10, 0, 11, 5, 14, 11, 9, 0, 10, 0, 16, 0,
9, 10, 5, 4, 10, 0, 2, 0, 18, 7, 5, 3, 7, 7, 4, 0, 8, 0,
7, 0, 2, 0, 27, 0, 6, 4, 8, 10, 19, 3, 5, 0, 8, 10, 11, 11,
8, 0, 8, 1, 2, 0, 9, 15, 9, 5, 6, 0, 21, 0, 7, 10, 10, 10,
7, 2, 9, 4, 6, 3, 9, 0, 13, 1, 11, 6, 19, 5, 18, 0, 6, 1,
15, 19, 13, 15, 0, 0, 5, 16, 7, 0, 22, 2, 15, 0, 9, 0, 11,
1, 2, 0, 5, 0, 9, 0, 6, 0, 10, 0, 6, 0, 8, 0, 9, 0, 18, 2,
6, 2, 5, 0, 8, 0, 12, 0, 4, 2, 10, 0, 15, 0, 8, 5, 12, 0,
6, 0, 8, 0, 9, 0, 9, 0, 4, 0, 9, 0, 7, 0, 11, 0, 20, 0, 8,
0, 2, 0, 16, 0, 3, 0, 8, 0, 17, 0, 18, 0, 16, 0, 5, 0, 10,
0, 14, 0, 6, 0, 10, 0, 28, 0, 0, 0, 10, 0, 12, 0, 8, 0, 5,
0, 8, 0, 18, 0, 15, 0, 5, 0, 18, 0, 12, 0, 20, 0, 1, 0, 3,
0, 10, 0, 17, 0, 7, 0, 15, 0, 9, 0, 8, 0, 11, 0, 6, 0, 10,
0, 3, 0, 11, 0, 8, 0, 7, 0, 1, 0, 11, 0, 27, 0, 15, 0, 9,
0, 13, 0, 7, 0, 9, 0, 5, 0, 5, 0, 10, 0, 14, 0, 8, 0, 3,
0, 6, 0, 11, 0, 17, 0, 6, 0, 12, 0, 7, 0, 14, 0, 25, 0, 7,
0, 7, 0, 10, 0, 7, 0, 7, 0, 5, 0, 7, 0, 6, 0, 13, 0, 6, 0,
1, 0, 8, 0, 6, 0, 8, 0, 9, 0, 9, 0, 11, 0, 4, 0, 14, 0, 1,
0, 5, 0, 10, 0, 9, 0, 14, 0, 18, 0, 12, 0, 8, 0, 7, 0, 10,
0, 4, 0, 6, 0, 8, 0, 14, 0, 14, 0, 6, 0, 6, 0, 5, 0, 14,
0, 0, 0, 7, 0, 9, 0, 5, 0, 27, 0, 9, 0, 11, 0, 7, 0, 7, 0,
6, 0, 5, 0, 15, 0, 15, 0, 19, 0, 15, 0, 14, 0, 7, 0, 2, 0,
6, 0, 8, 0, 6, 0, 9, 0, 6, 0, 12, 0, 8, 0, 12, 0, 10, 0,
10, 0, 11, 0, 9, 0, 5, 0, 5, 0, 18, 0, 4, 0, 18, 0, 12, 0,
20, 0, 8, 0, 11, 0, 9, 0), group = structure(c(1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
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2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
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1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
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2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("0",
"1"), class = "factor")), row.names = c(NA, -1290L), class = c("tbl_df",
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您尚未指定公式，因此它不知道要運行哪個測試。 嘗試添加formula = outcome ~ group*time
所以代碼是這樣的
rstatix::anova_test(data = test1, formula = outcome ~ group*time,
dv = outcome,
wid = id2,
within = c(group, time))
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 group 1 1286 0.806 3.70e-01 0.000626
2 time 1 1286 303.565 3.35e-61 * 0.191000
3 group:time 1 1286 0.309 5.78e-01 0.000240
這只是一個簡單的示例，請查看 rstatix anova_test 文檔以確保您使用正確的公式進行正確的測試。
編輯緩慢的一天，所以我想我會按照你想要的重復測量來嘗試你的數據，但它只是給出了錯誤，我不知道為什么所有數據看起來都很好。 下面的代碼應該可以工作，盡管它是對線性混合 model 的解釋。這似乎比重復測量方差分析更強大、更靈活，而且很多論文都轉向了這種方法。 快速谷歌應該告訴你為什么。
您不需要安裝或運行報告 function，但它提供了對 model 的非常有用的解釋。我不會在這里粘貼 output，因為其中有很多，但值得一試。
devtools::install_github("easystats/report")
library(report)
install.packages("lmerTest")
library(lmerTest)
fit <- lmer(outcome ~ group*time + (1|id), data=test1)
anova(fit)
report(fit)
這應該為您提供以下 output: anova(fit)：
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
group 10.3 10.3 1 643 0.5613 0.4540
time 9846.7 9846.7 1 643 538.0482 <2e-16 ***
group:time 10.0 10.0 1 643 0.5474 0.4597
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
盡管超過 2 個級別，R 中的 rstatix anova_test 仍會出現對比度錯誤
[英]rstatix anova_test in R gives contrast error despite more than 2 levels
盡管設置 effect.size = 'ges'，rstatix package anova_test function 給出了部分 eta 平方
[英]rstatix package anova_test function gives partial eta squared despite setting effect.size = 'ges'
“anova_test” function 誤差（0 (non_NA) 案例）和線性組合的雙向重復 anova
[英]“anova_test” function error (0 (non_NA) cases) and linear combination for two-way repeated anova
錯誤問題：列不存在，同時嘗試在 R 中執行 anova_test
[英]Error problem: Column does not exist, while trying to perform an anova_test in R
rstatix 的 levene_test() 出錯：mutate() 和 filter() 有問題
[英]Error with levene_test() from rstatix: problem with mutate() and filter()
為什么在 R 中使用 summar(aov) 和 anova_test 時得到不同的統計數據？
[英]Why do I get different stats when using summar(aov) and anova_test in R?
雙向重復測量方差分析：lm.fit() 中的錯誤... 0 非 na 情況 (rstatix)
[英]Two-Way Repeated Measures ANOVA: Error in lm.fit()... 0 non-na cases (rstatix)
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