使用 ANOVA w/repeated 测量时出现意外错误：lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) 中的错误：0（非 NA ) 个案

Question

我有四个导出和处理相同的数据集（尽管由于 Landsat 场景的可用性而大小不同）

我正在尝试使用以下公式计算方差分析：

res.aov <- anova_test(
  data = LST_Weather_dataset_ANOVA, dv = LST, wid = JulianDay,
  within = c(Buffer, TimePeriod),
  effect.size = "ges",
  detailed = TRUE,
)
get_anova_table(res.aov, correction = "auto")

在哪里： *) LST = 以 C 为单位的表面温度偏差 *) JulianDay = 自年初以来的天数 *) Buffer = 一个值 100-1900 - 太阳能发电厂边界向外的 19 个区域之一（每个 100m 宽） *) TimePeriod = 太阳能发电厂pre-/post-construction对应值为0或1的因子。

目的是调查安装施工是否影响了邻近地表温度。

方差分析在三个站点成功运行，但在第四个站点没有运行并失败并显示错误：

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases

我有 4 列 381 行数据（摘录如下），我在这里能想到的唯一区别是我不得不从时间序列中删除两个成对的月份，因为其中一个月份的数据不可用.这意味着有 20 个月的数据，而不是 24 个月。每个其他处理步骤都是相同的。

在线阅读我已经搜索了N/As（有none），但由于每个单元格都有数据，因此看不到没有值的级别。不过，我不知道如何正确评估这一点，因为这似乎是错误的根源。

我希望有人知道所需的代码and/or能够提出前进的方向。

Buffer  LST         JulianDay   TimePeriod
1800    -0.04576149 73          2
1900    -0.03422945 73          2
1900    -0.02089755 302         1
1900    -0.02062432 96          1
1900    -0.01465229 192         1
1900    -0.00643754 128         1
1900    -0.00333345 105         2
1800    -0.00266312 366         1
1900    -0.00181226 201         2
1900    -0.00158173 169         2
1900    -1.81E-05   41          2
1800    0.00144813  128         1
and 367 additional rows...

[编辑]

根据以下评论：

dput() 整个数据帧
dput() 子集（如建议的那样）

感谢@Dion 注意到 anova_test 来自 RStatix 包。

1)

> dput(LST_Weather_dataset_ANOVA)
structure(list(Buffer = c(100L, 200L, 300L, 400L, 500L, 600L, 
700L, 800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L, 100L, 200L, 300L, 400L, 500L, 600L, 700L, 
800L, 900L, 1000L, 1100L, 1200L, 1300L, 1400L, 1500L, 1600L, 
1700L, 1800L, 1900L), LST = c(0.91797777, 0.95083024, 0.79129483, 
0.74791195, 0.68740945, 0.64516119, 0.74870729, 0.78357522, 0.83663769, 
0.82156894, 0.77440129, 0.62769619, 0.52052404, 0.46497939, 0.34456476, 
0.20359411, 0.11688336, 0.04136486, -0.02089755, 1.15111659, 
1.20353638, 1.11717501, 1.0286145, 0.90984545, 0.82983601, 0.78186792, 
0.73227976, 0.6989393, 0.65015275, 0.56241798, 0.39651023, 0.34213091, 
0.3386525, 0.24000145, 0.11809023, 0.07704512, -0.00266312, 0.01273022, 
1.04229626, 1.14347392, 1.1156609, 1.10575157, 1.01202522, 0.77829087, 
0.80477079, 0.79677169, 0.83116477, 0.83242401, 0.82394197, 0.72073306, 
0.64099082, 0.58188225, 0.43328083, 0.28349521, 0.19752629, 0.10636456, 
0.01987005, 0.74458844, 0.71512573, 0.6395358, 0.65294657, 0.63325921, 
0.56155255, 0.60860815, 0.60614753, 0.59989994, 0.58766288, 0.57257261, 
0.50018929, 0.4367402, 0.40497079, 0.31822141, 0.2300726, 0.16928876, 
0.09449034, 0.01799424, 0.82747052, 0.78262774, 0.65488597, 0.62609552, 
0.60057131, 0.59950609, 0.6609992, 0.6876772, 0.73196883, 0.75516596, 
0.75554112, 0.64167458, 0.54703129, 0.49947692, 0.38230481, 0.25519237, 
0.16087274, 0.07759223, 0.00820849, 0.75009747, 0.71421977, 0.62411035, 
0.58621041, 0.58438012, 0.61346156, 0.72712994, 0.81372726, 0.87579554, 
0.88934787, 0.87369461, 0.74686202, 0.64084028, 0.5599638, 0.40021941, 
0.23612052, 0.13408522, 0.04484869, -0.02062432, 0.22133116, 
0.28562902, 0.24359043, 0.17788898, 0.16563242, 0.11740664, 0.10102937, 
0.07328697, 0.07948283, 0.07521508, 0.08526232, 0.0548022, 0.04632606, 
0.06670398, 0.03262545, 0.00650875, 0.01186519, 0.00144813, -0.00643754, 
0.26360849, 0.22139941, 0.16915041, 0.13499715, 0.12846785, 0.15351528, 
0.15321108, 0.13963269, 0.13413671, 0.13097696, 0.15897844, 0.15489366, 
0.12600815, 0.12363834, 0.0943688, 0.07324289, 0.0565765, 0.04005241, 
0.01346488, 0.42361198, 0.39149841, 0.29086274, 0.21492842, 0.20664552, 
0.24524285, 0.30548979, 0.35256808, 0.37350282, 0.38680061, 0.38567758, 
0.31177736, 0.24643091, 0.22001284, 0.14356522, 0.07076854, 0.04168654, 
0.01276553, -0.01465229, 0.57032414, 0.50658577, 0.41717664, 
0.36134446, 0.35794989, 0.38457285, 0.43700723, 0.48358206, 0.50516801, 
0.50086146, 0.49398709, 0.41516438, 0.33165215, 0.28357127, 0.20030152, 
0.11993505, 0.08438345, 0.05755944, 0.01071499, 0.04963208, 0.34087747, 
0.38385889, 0.40408637, 0.41182138, 0.15662208, 0.18857013, 0.17978741, 
0.1533216, 0.1451422, 0.14890638, 0.14090521, 0.1782449, 0.23624089, 
0.21003477, 0.13812217, 0.10759364, 0.07225312, 0.03185378, 0.27507486, 
0.54404521, 0.56568824, 0.58543167, 0.49124799, 0.28299777, 0.27514982, 
0.27526446, 0.27376722, 0.24620415, 0.22871699, 0.19647326, 0.2450593, 
0.27133386, 0.15248773, 0.06240341, 0.04933824, 0.03356535, -1.81e-05, 
0.21776379, 0.37010032, 0.32743525, 0.30588107, 0.31226738, 0.30518286, 
0.32637517, 0.31003415, 0.23691586, 0.1985241, 0.16143326, 0.12384526, 
0.11556386, 0.09243356, 0.05773894, 0.03660942, 0.02173758, -0.04576149, 
-0.03422945, 0.06214728, 0.26440563, 0.24838816, 0.22704611, 
0.17230754, 0.15660109, 0.18689433, 0.24464547, 0.28273218, 0.29602945, 
0.29992488, 0.24679735, 0.24521192, 0.23913767, 0.15081173, 0.08724556, 
0.05561237, 0.02530266, -0.00333345, 0.11993489, 0.20504424, 
0.17323488, 0.14541868, 0.10994579, 0.12741154, 0.17959797, 0.22553943, 
0.26564836, 0.29760832, 0.3207305, 0.28592135, 0.26551685, 0.2493214, 
0.15767906, 0.0883716, 0.05058495, 0.02207594, 0.00162532, 0.05621313, 
0.08020623, 0.05187855, 0.02643543, 0.02422505, 0.05372454, 0.09563737, 
0.14735627, 0.18199015, 0.22456299, 0.25302274, 0.21978124, 0.19092835, 
0.18255829, 0.11850551, 0.0581734, 0.03406168, 0.01868243, -0.00158173, 
0.00980756, 0.07077972, 0.05126985, 0.03126771, 0.01828044, 0.00678076, 
0.03566275, 0.05622289, 0.07218645, 0.08767578, 0.11078182, 0.08827425, 
0.08881865, 0.10037876, 0.05952601, 0.03440435, 0.01843206, 0.0091852, 
-0.00181226, 0.08737325, 0.14470842, 0.13066747, 0.12324597, 
0.12014198, 0.13435757, 0.17843025, 0.19926835, 0.20503774, 0.20485414, 
0.2124073, 0.1864257, 0.18810996, 0.20665551, 0.13839744, 0.08488387, 
0.06246853, 0.03463723, 0.00349753, 0.35245488, 0.57692156, 0.64897028, 
0.67306088, 0.68344534, 0.56106697, 0.52144197, 0.49250191, 0.47494065, 
0.4359944, 0.39638743, 0.32554099, 0.28717774, 0.2826675, 0.22703594, 
0.18186983, 0.15875118, 0.09672536, 0.04305742, 0.24294606, 0.54654222, 
0.56344638, 0.53312729, 0.47324972, 0.34482643, 0.34915085, 0.33729055, 
0.32086985, 0.29578347, 0.25030669, 0.17928298, 0.17007511, 0.18375903, 
0.15222616, 0.10934224, 0.07536797, 0.04154465, 0.02550096), 
    JulianDay = c(302L, 302L, 302L, 302L, 302L, 302L, 302L, 302L, 
    302L, 302L, 302L, 302L, 302L, 302L, 302L, 302L, 302L, 302L, 
    302L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 
    366L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 366L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
    16L, 16L, 16L, 16L, 16L, 16L, 16L, 64L, 64L, 64L, 64L, 64L, 
    64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 
    64L, 64L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 
    80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 80L, 96L, 96L, 96L, 
    96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 96L, 
    96L, 96L, 96L, 96L, 128L, 128L, 128L, 128L, 128L, 128L, 128L, 
    128L, 128L, 128L, 128L, 128L, 128L, 128L, 128L, 128L, 128L, 
    128L, 128L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 
    160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 160L, 
    160L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 
    192L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 192L, 
    224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 
    224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 224L, 9L, 
    9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 
    41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 41L, 73L, 73L, 
    73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 73L, 
    73L, 73L, 73L, 73L, 73L, 105L, 105L, 105L, 105L, 105L, 105L, 
    105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 105L, 
    105L, 105L, 105L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 
    137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 137L, 
    137L, 137L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 
    169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 169L, 
    169L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 
    201L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 201L, 
    217L, 217L, 217L, 217L, 217L, 217L, 217L, 217L, 217L, 217L, 
    217L, 217L, 217L, 217L, 217L, 217L, 217L, 217L, 217L, 313L, 
    313L, 313L, 313L, 313L, 313L, 313L, 313L, 313L, 313L, 313L, 
    313L, 313L, 313L, 313L, 313L, 313L, 313L, 313L, 361L, 361L, 
    361L, 361L, 361L, 361L, 361L, 361L, 361L, 361L, 361L, 361L, 
    361L, 361L, 361L, 361L, 361L, 361L, 361L), TimePeriod = 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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA, 
-380L))

> dput(LST_Weather_dataset_ANOVA[sample(1:nrow(LST_Weather_dataset_ANOVA), 50),])
structure(list(Buffer = c(800L, 1400L, 500L, 200L, 400L, 1400L, 
100L, 1600L, 1800L, 100L, 1400L, 1500L, 900L, 700L, 800L, 600L, 
400L, 1300L, 500L, 700L, 700L, 300L, 700L, 200L, 200L, 500L, 
500L, 900L, 1000L, 1300L, 1400L, 1600L, 700L, 400L, 500L, 200L, 
400L, 1500L, 1400L, 800L, 500L, 1200L, 1500L, 1900L, 600L, 800L, 
100L, 1000L, 900L, 1100L), LST = c(0.48358206, 0.46497939, 0.41182138, 
0.07077972, 0.17788898, 0.18255829, 0.21776379, 0.03660942, 0.04154465, 
0.42361198, 0.49947692, 0.38230481, 0.28273218, 0.18857013, 0.33729055, 
0.56106697, 0.13499715, 0.28717774, 0.12014198, 0.78186792, 0.74870729, 
0.56344638, 0.18689433, 0.54404521, 0.78262774, 0.60057131, 1.01202522, 
0.20503774, 0.13097696, 0.34213091, 0.5599638, 0.08724556, 0.17843025, 
1.0286145, 0.01828044, 0.22139941, 0.67306088, 0.15248773, 0.22001284, 
0.27526446, 0.02422505, 0.50018929, 0.31822141, 0.01799424, 0.56155255, 
0.13963269, 0.27507486, 0.29578347, 0.18199015, 0.3207305), JulianDay = c(224L, 
302L, 9L, 201L, 128L, 169L, 73L, 73L, 361L, 192L, 80L, 80L, 105L, 
9L, 361L, 313L, 160L, 313L, 217L, 366L, 302L, 361L, 105L, 41L, 
80L, 80L, 16L, 217L, 160L, 366L, 96L, 105L, 217L, 366L, 201L, 
160L, 313L, 41L, 192L, 41L, 169L, 64L, 64L, 64L, 64L, 160L, 41L, 
361L, 169L, 137L), TimePeriod = c(1L, 1L, 2L, 2L, 1L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 
2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L)), row.names = c(179L, 
14L, 195L, 306L, 118L, 299L, 229L, 244L, 379L, 153L, 90L, 91L, 
256L, 197L, 369L, 348L, 137L, 355L, 328L, 26L, 7L, 364L, 254L, 
211L, 78L, 81L, 43L, 332L, 143L, 32L, 109L, 263L, 330L, 23L, 
309L, 135L, 346L, 224L, 166L, 217L, 290L, 69L, 72L, 76L, 63L, 
141L, 210L, 371L, 294L, 277L), class = "data.frame")

Answer 1

昨晚熨烫时，我想知道 JulianDay 是否可能是错误的来源。它是从因变量数据中导出的 Landsat 场景的日期导出的，因此每个站点都不同。

编辑数据框以将 JulianDay 列替换为 Month 并将代码修改为：

str(LST_Weather_dataset_ANOVA)
res.aov <- anova_test(
  data = LST_Weather_dataset_ANOVA, dv = LST, wid = Month,
  within = c(Buffer, TimePeriod),
  effect.size = "ges",
  detailed = TRUE,
)
get_anova_table(res.aov, correction = "auto")

...ANOVA 测试运行成功：

> res.aov <- anova_test(
+   data = LST_Weather_dataset_ANOVA, dv = LST, wid = Month,
+   within = c(Buffer, TimePeriod),
+   effect.size = "ges",
+   detailed = TRUE,
+ )
> get_anova_table(res.aov, correction = "auto")
ANOVA Table (type III tests)

             Effect DFn DFd    SSn   SSd      F        p p<.05   ges
1       (Intercept)   1   9 36.781 6.593 50.212 5.75e-05     * 0.735
2            Buffer  18 162  8.042 3.041 23.801 1.81e-36     * 0.378
3        TimePeriod   1   9  5.065 2.506 18.194 2.00e-03     * 0.276
4 Buffer:TimePeriod  18 162  1.713 1.117 13.800 2.71e-24     * 0.114

但是我还是不太明白为什么...

希望这能让有人发表评论并提供解释？

Answer 2

您正在运行重复方差分析，这需要在您指定的范围内对每个人进行完整的观察。在您的情况下，您需要确保每个 JulianDay，Buffer 和 TimePeriod

的每个组合的观察都是完整的

我们可以使用 table() 将其制表，您可以看到所有 JulianDays 都是不完整的，例如 9 和 16：

with(LST_Weather_dataset_ANOVA,table(Buffer,TimePeriod,JulianDay))[,,c("9","16")]
, , JulianDay = 9

      TimePeriod
Buffer 1 2
  100  0 1
  200  0 1
  300  0 1
  400  0 1
  500  0 1
  600  0 1
  700  0 1
  800  0 1
  900  0 1
  1000 0 1
  1100 0 1
  1200 0 1
  1300 0 1
  1400 0 1
  1500 0 1
  1600 0 1
  1700 0 1
  1800 0 1
  1900 0 1

, , JulianDay = 16

      TimePeriod
Buffer 1 2
  100  1 0
  200  1 0
  300  1 0
  400  1 0
  500  1 0
  600  1 0
  700  1 0
  800  1 0
  900  1 0
  1000 1 0
  1100 1 0
  1200 1 0
  1300 1 0
  1400 1 0
  1500 1 0
  1600 1 0
  1700 1 0
  1800 1 0
  1900 1 0

正如您所注意到的，如果您协调站点之间的日期，它将起作用。我不太确定你是如何将 JulianDay 转换为月份的，但是使用你的数据，如果我这样做就可以了

df = LST_Weather_dataset_ANOVA
df$Month = months(strptime(paste("2020",df$JulianDay),"%Y %j"))
df = subset(df,Month %in% c("May","June"))
with(df,table(Buffer,TimePeriod,Month))

, , Month = June

      TimePeriod
Buffer 1 2
  100  1 1
  200  1 1
  300  1 1
  400  1 1
  500  1 1
  600  1 1
  700  1 1
  800  1 1
  900  1 1
  1000 1 1
  1100 1 1
  1200 1 1
  1300 1 1
  1400 1 1
  1500 1 1
  1600 1 1
  1700 1 1
  1800 1 1
  1900 1 1

, , Month = May

      TimePeriod
Buffer 1 2
  100  1 1
  200  1 1
  300  1 1
  400  1 1
  500  1 1
  600  1 1
  700  1 1
  800  1 1
  900  1 1
  1000 1 1
  1100 1 1
  1200 1 1
  1300 1 1
  1400 1 1
  1500 1 1
  1600 1 1
  1700 1 1
  1800 1 1
  1900 1 1

你可以看到 6 月和 5 月，它们是完整的（没有零），如果我们运行 anova，它有效：

res.aov <- anova_test(
  data = df, dv = LST, wid = Month,
  within = c(Buffer, TimePeriod),
  effect.size = "ges",
  detailed = TRUE,
)

ANOVA Table (type III tests)

             Effect DFn DFd   SSn   SSd       F        p p<.05   ges
1       (Intercept)   1   1 1.217 0.005 222.936 4.30e-02     * 0.933
2            Buffer  18  18 0.256 0.026   9.933 5.49e-06     * 0.746
3        TimePeriod   1   1 0.013 0.048   0.274 6.93e-01       0.130
4 Buffer:TimePeriod  18  18 0.181 0.008  21.476 1.20e-08     * 0.674

使用 ANOVA w/repeated 测量时出现意外错误：lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) 中的错误：0（非 NA ) 个案

Unexpected error while using ANOVA w/repeated measures: Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases

r

anova