双向重复测量方差分析：lm.fit()... 0 个非 na 案例 (rstatix)
Two-Way Repeated Measures ANOVA: Error in lm.fit()... 0 non-na cases (rstatix)
我正在尝试根据 here 中的描述使用 R Statix 包 运行 双向重复测量方差分析，但是我 运行 正在进入主题错误我的数据集。数据包括 5 个月内在 8 个位置的每个位置记录的 10 个重复生物量测量值（10 个重复 x 8 个位置 x 5 个月 = 400 个观察值）。
head(biomass)
然而，当我运行这个：
library(rstatix)
res.aov <- anova_test(
data = biomass, dv = Biomass, wid = ID,
within = c(Location, SampleMonth)
)
我收到错误：
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases
我看过类似的 posts here and 似乎表明错误是由 lm 中的 NA 引起的。
在上面第一个post的基础上，我尝试运行:
lm(Biomass~Location:SampleMonth, data =biomass)
我注意到一个 Location:SampleMonth 值显示 NA 系数 (LocationMPE:SampleMonth10)。但是，如果我从此位置删除所有数据并重试，所发生的只是另一个 Location:SampleMonth 系数变为 NA。这似乎发生在任何组合（甚至只有 2 个）位置。
我是不是做错了什么？
下面是我整个数据集的 dput()：
biomass <- structure(list(ID = structure(1:400, .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92",
"93", "94", "95", "96", "97", "98", "99", "100", "101", "102",
"103", "104", "105", "106", "107", "108", "109", "110", "111",
"112", "113", "114", "115", "116", "117", "118", "119", "120",
"121", "122", "123", "124", "125", "126", "127", "128", "129",
"130", "131", "132", "133", "134", "135", "136", "137", "138",
"139", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "150", "151", "152", "153", "154", "155", "156",
"157", "158", "159", "160", "161", "162", "163", "164", "165",
"166", "167", "168", "169", "170", "171", "172", "173", "174",
"175", "176", "177", "178", "179", "180", "181", "182", "183",
"184", "185", "186", "187", "188", "189", "190", "191", "192",
"193", "194", "195", "196", "197", "198", "199", "200", "201",
"202", "203", "204", "205", "206", "207", "208", "209", "210",
"211", "212", "213", "214", "215", "216", "217", "218", "219",
"220", "221", "222", "223", "224", "225", "226", "227", "228",
"229", "230", "231", "232", "233", "234", "235", "236", "237",
"238", "239", "240", "241", "242", "243", "244", "245", "246",
"247", "248", "249", "250", "251", "252", "253", "254", "255",
"256", "257", "258", "259", "260", "261", "262", "263", "264",
"265", "266", "267", "268", "269", "270", "271", "272", "273",
"274", "275", "276", "277", "278", "279", "280", "281", "282",
"283", "284", "285", "286", "287", "288", "289", "290", "291",
"292", "293", "294", "295", "296", "297", "298", "299", "300",
"301", "302", "303", "304", "305", "306", "307", "308", "309",
"310", "311", "312", "313", "314", "315", "316", "317", "318",
"319", "320", "321", "322", "323", "324", "325", "326", "327",
"328", "329", "330", "331", "332", "333", "334", "335", "336",
"337", "338", "339", "340", "341", "342", "343", "344", "345",
"346", "347", "348", "349", "350", "351", "352", "353", "354",
"355", "356", "357", "358", "359", "360", "361", "362", "363",
"364", "365", "366", "367", "368", "369", "370", "371", "372",
"373", "374", "375", "376", "377", "378", "379", "380", "381",
"382", "383", "384", "385", "386", "387", "388", "389", "390",
"391", "392", "393", "394", "395", "396", "397", "398", "399",
"400"), class = "factor"), Location = structure(c(6L, 7L, 5L,
8L, 6L, 6L, 8L, 5L, 3L, 8L, 7L, 7L, 8L, 6L, 8L, 1L, 8L, 4L, 8L,
8L, 1L, 3L, 6L, 3L, 7L, 3L, 2L, 8L, 6L, 6L, 4L, 3L, 4L, 3L, 6L,
2L, 2L, 3L, 3L, 7L, 2L, 1L, 4L, 7L, 8L, 6L, 4L, 8L, 7L, 4L, 4L,
8L, 3L, 5L, 4L, 8L, 5L, 4L, 8L, 3L, 3L, 2L, 7L, 2L, 7L, 4L, 5L,
3L, 2L, 8L, 4L, 4L, 5L, 1L, 4L, 7L, 7L, 6L, 2L, 7L, 3L, 3L, 4L,
5L, 7L, 4L, 4L, 1L, 6L, 1L, 3L, 1L, 1L, 1L, 2L, 6L, 1L, 8L, 2L,
5L, 6L, 5L, 3L, 4L, 4L, 8L, 1L, 1L, 8L, 1L, 5L, 5L, 8L, 8L, 8L,
6L, 8L, 7L, 8L, 7L, 3L, 5L, 4L, 7L, 5L, 4L, 5L, 1L, 5L, 1L, 6L,
2L, 8L, 4L, 5L, 4L, 3L, 7L, 8L, 4L, 7L, 7L, 6L, 2L, 2L, 5L, 8L,
5L, 1L, 2L, 7L, 8L, 7L, 8L, 8L, 1L, 4L, 6L, 4L, 2L, 1L, 7L, 2L,
8L, 5L, 6L, 1L, 4L, 1L, 8L, 1L, 7L, 3L, 1L, 2L, 2L, 2L, 1L, 6L,
5L, 2L, 3L, 1L, 5L, 4L, 4L, 4L, 2L, 1L, 5L, 7L, 3L, 7L, 5L, 8L,
8L, 7L, 2L, 1L, 3L, 5L, 1L, 2L, 3L, 8L, 2L, 1L, 3L, 7L, 7L, 7L,
7L, 1L, 2L, 4L, 7L, 3L, 8L, 4L, 7L, 2L, 7L, 1L, 8L, 5L, 4L, 7L,
6L, 8L, 5L, 5L, 3L, 5L, 3L, 1L, 6L, 4L, 6L, 8L, 7L, 6L, 1L, 4L,
7L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 6L, 2L, 3L, 2L, 7L, 5L, 4L, 3L,
3L, 2L, 1L, 1L, 2L, 8L, 6L, 1L, 1L, 7L, 4L, 5L, 5L, 5L, 7L, 6L,
5L, 8L, 3L, 7L, 5L, 5L, 2L, 1L, 6L, 3L, 6L, 1L, 4L, 2L, 8L, 1L,
2L, 7L, 7L, 6L, 5L, 2L, 7L, 4L, 7L, 4L, 5L, 5L, 5L, 6L, 5L, 3L,
1L, 8L, 4L, 1L, 5L, 2L, 4L, 6L, 3L, 3L, 4L, 2L, 5L, 7L, 8L, 5L,
6L, 1L, 8L, 2L, 1L, 7L, 1L, 5L, 4L, 1L, 6L, 6L, 3L, 3L, 4L, 3L,
2L, 8L, 5L, 2L, 3L, 6L, 6L, 5L, 2L, 5L, 7L, 6L, 6L, 3L, 8L, 6L,
7L, 3L, 4L, 2L, 2L, 6L, 5L, 4L, 3L, 3L, 1L, 6L, 1L, 4L, 6L, 2L,
6L, 4L, 6L, 2L, 7L, 8L, 5L, 4L, 3L, 5L, 1L, 2L, 3L, 6L, 2L, 3L,
8L, 7L, 6L, 3L, 8L, 6L, 3L, 8L, 6L, 6L, 3L, 4L, 6L), .Label = c("BR",
"HBY", "MBB", "MCB", "MEB", "MEL", "MPD", "MPE"), class = "factor"),
SampleMonth = structure(c(1L, 1L, 5L, 3L, 2L, 1L, 1L, 4L,
2L, 4L, 4L, 2L, 4L, 1L, 5L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 5L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 3L, 1L, 5L, 3L, 4L, 5L,
1L, 5L, 5L, 1L, 5L, 5L, 5L, 3L, 1L, 1L, 1L, 1L, 1L, 5L, 4L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 5L, 4L,
3L, 1L, 5L, 2L, 5L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 5L, 1L,
1L, 2L, 1L, 3L, 1L, 1L, 4L, 4L, 5L, 1L, 5L, 3L, 5L, 1L, 1L,
3L, 4L, 2L, 1L, 4L, 2L, 4L, 1L, 1L, 2L, 5L, 4L, 1L, 2L, 2L,
1L, 3L, 1L, 1L, 5L, 2L, 4L, 5L, 1L, 1L, 5L, 3L, 2L, 1L, 2L,
4L, 3L, 5L, 1L, 1L, 3L, 5L, 2L, 5L, 3L, 4L, 1L, 2L, 4L, 4L,
5L, 5L, 5L, 5L, 3L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 4L, 2L, 5L,
4L, 4L, 2L, 2L, 5L, 1L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 3L, 5L,
4L, 3L, 2L, 3L, 4L, 2L, 4L, 5L, 5L, 5L, 1L, 3L, 3L, 3L, 3L,
4L, 3L, 2L, 1L, 2L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 3L,
2L, 4L, 4L, 5L, 3L, 2L, 1L, 3L, 4L, 5L, 5L, 1L, 1L, 4L, 2L,
5L, 4L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 4L,
3L, 3L, 5L, 4L, 4L, 2L, 2L, 2L, 5L, 5L, 5L, 2L, 2L, 3L, 2L,
4L, 4L, 5L, 4L, 5L, 5L, 4L, 4L, 3L, 4L, 5L, 3L, 2L, 5L, 4L,
5L, 5L, 3L, 3L, 5L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 2L,
5L, 5L, 2L, 3L, 4L, 5L, 2L, 4L, 5L, 2L, 2L, 3L, 3L, 4L, 4L,
2L, 3L, 2L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 4L, 4L, 3L, 2L, 2L,
3L, 4L, 2L, 5L, 2L, 2L, 3L, 5L, 2L, 2L, 3L, 2L, 4L, 3L, 5L,
2L, 2L, 4L, 3L, 5L, 5L, 4L, 3L, 3L, 2L, 4L, 3L, 5L, 4L, 3L,
2L, 2L, 3L, 2L, 2L, 3L, 4L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 4L,
4L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 3L, 5L, 4L, 5L, 4L, 5L, 4L,
3L, 4L, 4L, 3L, 3L, 3L, 5L, 5L, 3L, 5L, 2L, 4L, 3L, 4L, 4L,
3L, 4L), .Label = c("06", "07", "08", "09", "10"), class = "factor"),
Biomass = c(0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.99, 19.99, 36.99, 39.99, 50, 50, 51, 60,
60, 69.99, 80, 81, 84, 86.99, 89.99, 100, 100, 100, 100.99,
117, 119.99, 119.99, 119.99, 125, 129.99, 130, 139.99, 140,
150, 150, 152.99, 159.99, 169.99, 170, 179.99, 184.99, 189.99,
189.99, 192.99, 199.99, 199.99, 210, 219.99, 219.99, 230,
230, 235, 248, 250, 269.99, 269.99, 269.99, 276, 279.99,
280, 282, 289.99, 289.99, 300, 300, 300, 304, 310, 310, 319.99,
319.99, 326, 329.99, 330, 335.99, 339.99, 339.99, 340, 350,
350, 360, 362, 369.99, 369.99, 369.99, 375, 379.99, 380,
383, 384, 390, 390, 399.99, 400, 419.99, 420, 423.99, 429.99,
430, 430, 430, 435.99, 440, 450, 459.99, 459.99, 465, 469.99,
479.99, 480, 480, 482, 484, 490, 490, 499.99, 500, 509.99,
519.99, 529.99, 530, 544.99, 556.99, 559.99, 560, 569.99,
569.99, 573, 579, 590, 590, 597, 609.99, 609.99, 609.99,
610, 610, 630, 630, 640, 650, 659.99, 659.99, 659.99, 667,
670, 680, 680, 680, 690, 700, 702, 709.99, 709.99, 709.99,
719.99, 719.99, 729.99, 730, 739.99, 740, 744, 745.99, 749.99,
750, 752, 756.99, 758, 759.99, 759.99, 760, 769.99, 769.99,
769.99, 780, 789.99, 789.99, 789.99, 790, 794, 809.99, 810,
810, 810, 819.99, 829.99, 829.99, 830, 840, 840, 850, 869,
870, 870, 880, 880, 890, 890, 890, 898.99, 900, 900, 909.99,
910, 919.99, 920, 930, 939.99, 940, 949.99, 950, 950, 959.99,
960, 970, 979.99, 979.99, 980, 980.99, 989.99, 990, 997.99,
1000, 1019.99, 1020, 1020, 1029.99, 1030, 1030, 1040, 1049.99,
1050, 1050, 1059.99, 1069.99, 1080, 1100, 1100, 1120, 1130,
1130.99, 1139.99, 1153, 1158, 1170, 1170, 1177, 1180, 1180,
1180, 1180, 1189.99, 1190, 1200, 1219.99, 1220, 1240, 1259.99,
1259.99, 1279, 1279.99, 1280.99, 1300, 1309.99, 1310, 1316,
1320, 1330, 1339.99, 1339.99, 1340, 1345, 1359.99, 1360,
1369, 1370, 1379.99, 1400, 1410, 1420, 1429.99, 1430.99,
1440, 1440, 1449, 1450, 1459.99, 1468.99, 1470, 1474, 1479.99,
1490, 1490, 1490, 1499.99, 1510, 1520, 1529.99, 1540, 1560,
1560, 1570, 1570, 1579.99, 1580, 1580, 1590, 1590, 1594,
1620, 1630, 1630, 1639.99, 1650, 1650, 1680, 1715, 1719.99,
1720, 1729.99, 1733, 1770, 1779.99, 1808, 1819.99, 1837,
1840, 1860, 1900, 1909.99, 1910, 1929.99, 1940, 1954, 2020,
2040, 2050, 2069.99, 2090, 2100, 2102.99, 2159.99, 2179.99,
2249.99, 2279.99, 2289.99, 2290, 2334, 2366, 2380, 2399.99,
2400, 2420, 2450, 2500, 2520, 2550, 2589.99, 2589.99, 2599.99,
2600, 2620, 2660, 2713, 2739.99, 2830, 2920, 2959.99, 3010,
3029.99, 3129.99, 3150, 3190, 3250, 3310, 3440, 3490, 3650,
3740, 3809.99, 3850, 3920, 5799.99, 5898.72, 6680, 6970)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -400L))
首先，对于 lm，您得到 NA，因为您没有指定主要术语：
fit = lm(Biomass ~ SampleMonth*Location,data=biomass)
table(is.na(coefficients(fit)))
FALSE
40
当你做双向重复方差分析时，你有一个重复测量的实验或研究，就像许多患者在一段时间内接受治疗等。在你的情况下，如果没有 "repeated measure"，或者接受相同治疗的普通个体，那么你可以使用常规方差分析。
如果您对时间段和地点的影响感兴趣：
anova_test(Biomass ~ SampleMonth+Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 SampleMonth 4 388 18.712 4.45e-14 * 0.162
2 Location 7 388 2.969 5.00e-03 * 0.051
以上假定每个月或每个位置的共同均值（或效果）。您可以有一个更复杂的模型来测试不同地点的月份效应是否存在差异：
anova_test(Biomass ~ SampleMonth*Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 SampleMonth 4 360 19.507 1.53e-14 * 0.178
2 Location 7 360 3.095 4.00e-03 * 0.057
3 SampleMonth:Location 28 360 1.588 3.20e-02 * 0.110
我正在尝试根据 here 中的描述使用 R Statix 包 运行 双向重复测量方差分析，但是我 运行 正在进入主题错误我的数据集。数据包括 5 个月内在 8 个位置的每个位置记录的 10 个重复生物量测量值（10 个重复 x 8 个位置 x 5 个月 = 400 个观察值）。
head(biomass)
然而，当我运行这个：
library(rstatix)
res.aov <- anova_test(
data = biomass, dv = Biomass, wid = ID,
within = c(Location, SampleMonth)
)
我收到错误：
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases
我看过类似的 posts here and
在上面第一个post的基础上，我尝试运行:
lm(Biomass~Location:SampleMonth, data =biomass)
我注意到一个 Location:SampleMonth 值显示 NA 系数 (LocationMPE:SampleMonth10)。但是，如果我从此位置删除所有数据并重试，所发生的只是另一个 Location:SampleMonth 系数变为 NA。这似乎发生在任何组合（甚至只有 2 个）位置。
我是不是做错了什么？
下面是我整个数据集的 dput()：
biomass <- structure(list(ID = structure(1:400, .Label = c("1", "2", "3",
"4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37",
"38", "39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59",
"60", "61", "62", "63", "64", "65", "66", "67", "68", "69", "70",
"71", "72", "73", "74", "75", "76", "77", "78", "79", "80", "81",
"82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92",
"93", "94", "95", "96", "97", "98", "99", "100", "101", "102",
"103", "104", "105", "106", "107", "108", "109", "110", "111",
"112", "113", "114", "115", "116", "117", "118", "119", "120",
"121", "122", "123", "124", "125", "126", "127", "128", "129",
"130", "131", "132", "133", "134", "135", "136", "137", "138",
"139", "140", "141", "142", "143", "144", "145", "146", "147",
"148", "149", "150", "151", "152", "153", "154", "155", "156",
"157", "158", "159", "160", "161", "162", "163", "164", "165",
"166", "167", "168", "169", "170", "171", "172", "173", "174",
"175", "176", "177", "178", "179", "180", "181", "182", "183",
"184", "185", "186", "187", "188", "189", "190", "191", "192",
"193", "194", "195", "196", "197", "198", "199", "200", "201",
"202", "203", "204", "205", "206", "207", "208", "209", "210",
"211", "212", "213", "214", "215", "216", "217", "218", "219",
"220", "221", "222", "223", "224", "225", "226", "227", "228",
"229", "230", "231", "232", "233", "234", "235", "236", "237",
"238", "239", "240", "241", "242", "243", "244", "245", "246",
"247", "248", "249", "250", "251", "252", "253", "254", "255",
"256", "257", "258", "259", "260", "261", "262", "263", "264",
"265", "266", "267", "268", "269", "270", "271", "272", "273",
"274", "275", "276", "277", "278", "279", "280", "281", "282",
"283", "284", "285", "286", "287", "288", "289", "290", "291",
"292", "293", "294", "295", "296", "297", "298", "299", "300",
"301", "302", "303", "304", "305", "306", "307", "308", "309",
"310", "311", "312", "313", "314", "315", "316", "317", "318",
"319", "320", "321", "322", "323", "324", "325", "326", "327",
"328", "329", "330", "331", "332", "333", "334", "335", "336",
"337", "338", "339", "340", "341", "342", "343", "344", "345",
"346", "347", "348", "349", "350", "351", "352", "353", "354",
"355", "356", "357", "358", "359", "360", "361", "362", "363",
"364", "365", "366", "367", "368", "369", "370", "371", "372",
"373", "374", "375", "376", "377", "378", "379", "380", "381",
"382", "383", "384", "385", "386", "387", "388", "389", "390",
"391", "392", "393", "394", "395", "396", "397", "398", "399",
"400"), class = "factor"), Location = structure(c(6L, 7L, 5L,
8L, 6L, 6L, 8L, 5L, 3L, 8L, 7L, 7L, 8L, 6L, 8L, 1L, 8L, 4L, 8L,
8L, 1L, 3L, 6L, 3L, 7L, 3L, 2L, 8L, 6L, 6L, 4L, 3L, 4L, 3L, 6L,
2L, 2L, 3L, 3L, 7L, 2L, 1L, 4L, 7L, 8L, 6L, 4L, 8L, 7L, 4L, 4L,
8L, 3L, 5L, 4L, 8L, 5L, 4L, 8L, 3L, 3L, 2L, 7L, 2L, 7L, 4L, 5L,
3L, 2L, 8L, 4L, 4L, 5L, 1L, 4L, 7L, 7L, 6L, 2L, 7L, 3L, 3L, 4L,
5L, 7L, 4L, 4L, 1L, 6L, 1L, 3L, 1L, 1L, 1L, 2L, 6L, 1L, 8L, 2L,
5L, 6L, 5L, 3L, 4L, 4L, 8L, 1L, 1L, 8L, 1L, 5L, 5L, 8L, 8L, 8L,
6L, 8L, 7L, 8L, 7L, 3L, 5L, 4L, 7L, 5L, 4L, 5L, 1L, 5L, 1L, 6L,
2L, 8L, 4L, 5L, 4L, 3L, 7L, 8L, 4L, 7L, 7L, 6L, 2L, 2L, 5L, 8L,
5L, 1L, 2L, 7L, 8L, 7L, 8L, 8L, 1L, 4L, 6L, 4L, 2L, 1L, 7L, 2L,
8L, 5L, 6L, 1L, 4L, 1L, 8L, 1L, 7L, 3L, 1L, 2L, 2L, 2L, 1L, 6L,
5L, 2L, 3L, 1L, 5L, 4L, 4L, 4L, 2L, 1L, 5L, 7L, 3L, 7L, 5L, 8L,
8L, 7L, 2L, 1L, 3L, 5L, 1L, 2L, 3L, 8L, 2L, 1L, 3L, 7L, 7L, 7L,
7L, 1L, 2L, 4L, 7L, 3L, 8L, 4L, 7L, 2L, 7L, 1L, 8L, 5L, 4L, 7L,
6L, 8L, 5L, 5L, 3L, 5L, 3L, 1L, 6L, 4L, 6L, 8L, 7L, 6L, 1L, 4L,
7L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 6L, 2L, 3L, 2L, 7L, 5L, 4L, 3L,
3L, 2L, 1L, 1L, 2L, 8L, 6L, 1L, 1L, 7L, 4L, 5L, 5L, 5L, 7L, 6L,
5L, 8L, 3L, 7L, 5L, 5L, 2L, 1L, 6L, 3L, 6L, 1L, 4L, 2L, 8L, 1L,
2L, 7L, 7L, 6L, 5L, 2L, 7L, 4L, 7L, 4L, 5L, 5L, 5L, 6L, 5L, 3L,
1L, 8L, 4L, 1L, 5L, 2L, 4L, 6L, 3L, 3L, 4L, 2L, 5L, 7L, 8L, 5L,
6L, 1L, 8L, 2L, 1L, 7L, 1L, 5L, 4L, 1L, 6L, 6L, 3L, 3L, 4L, 3L,
2L, 8L, 5L, 2L, 3L, 6L, 6L, 5L, 2L, 5L, 7L, 6L, 6L, 3L, 8L, 6L,
7L, 3L, 4L, 2L, 2L, 6L, 5L, 4L, 3L, 3L, 1L, 6L, 1L, 4L, 6L, 2L,
6L, 4L, 6L, 2L, 7L, 8L, 5L, 4L, 3L, 5L, 1L, 2L, 3L, 6L, 2L, 3L,
8L, 7L, 6L, 3L, 8L, 6L, 3L, 8L, 6L, 6L, 3L, 4L, 6L), .Label = c("BR",
"HBY", "MBB", "MCB", "MEB", "MEL", "MPD", "MPE"), class = "factor"),
SampleMonth = structure(c(1L, 1L, 5L, 3L, 2L, 1L, 1L, 4L,
2L, 4L, 4L, 2L, 4L, 1L, 5L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 5L, 3L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L,
1L, 3L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 3L, 1L, 5L, 3L, 4L, 5L,
1L, 5L, 5L, 1L, 5L, 5L, 5L, 3L, 1L, 1L, 1L, 1L, 1L, 5L, 4L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 5L, 4L,
3L, 1L, 5L, 2L, 5L, 2L, 1L, 3L, 1L, 1L, 1L, 3L, 2L, 5L, 1L,
1L, 2L, 1L, 3L, 1L, 1L, 4L, 4L, 5L, 1L, 5L, 3L, 5L, 1L, 1L,
3L, 4L, 2L, 1L, 4L, 2L, 4L, 1L, 1L, 2L, 5L, 4L, 1L, 2L, 2L,
1L, 3L, 1L, 1L, 5L, 2L, 4L, 5L, 1L, 1L, 5L, 3L, 2L, 1L, 2L,
4L, 3L, 5L, 1L, 1L, 3L, 5L, 2L, 5L, 3L, 4L, 1L, 2L, 4L, 4L,
5L, 5L, 5L, 5L, 3L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 4L, 2L, 5L,
4L, 4L, 2L, 2L, 5L, 1L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 3L, 5L,
4L, 3L, 2L, 3L, 4L, 2L, 4L, 5L, 5L, 5L, 1L, 3L, 3L, 3L, 3L,
4L, 3L, 2L, 1L, 2L, 3L, 3L, 4L, 4L, 4L, 2L, 2L, 4L, 4L, 3L,
2L, 4L, 4L, 5L, 3L, 2L, 1L, 3L, 4L, 5L, 5L, 1L, 1L, 4L, 2L,
5L, 4L, 2L, 3L, 1L, 2L, 3L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 4L,
3L, 3L, 5L, 4L, 4L, 2L, 2L, 2L, 5L, 5L, 5L, 2L, 2L, 3L, 2L,
4L, 4L, 5L, 4L, 5L, 5L, 4L, 4L, 3L, 4L, 5L, 3L, 2L, 5L, 4L,
5L, 5L, 3L, 3L, 5L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 2L,
5L, 5L, 2L, 3L, 4L, 5L, 2L, 4L, 5L, 2L, 2L, 3L, 3L, 4L, 4L,
2L, 3L, 2L, 3L, 3L, 3L, 5L, 4L, 3L, 3L, 4L, 4L, 3L, 2L, 2L,
3L, 4L, 2L, 5L, 2L, 2L, 3L, 5L, 2L, 2L, 3L, 2L, 4L, 3L, 5L,
2L, 2L, 4L, 3L, 5L, 5L, 4L, 3L, 3L, 2L, 4L, 3L, 5L, 4L, 3L,
2L, 2L, 3L, 2L, 2L, 3L, 4L, 5L, 5L, 5L, 5L, 2L, 5L, 5L, 4L,
4L, 4L, 5L, 4L, 4L, 4L, 3L, 5L, 3L, 5L, 4L, 5L, 4L, 5L, 4L,
3L, 4L, 4L, 3L, 3L, 3L, 5L, 5L, 3L, 5L, 2L, 4L, 3L, 4L, 4L,
3L, 4L), .Label = c("06", "07", "08", "09", "10"), class = "factor"),
Biomass = c(0.1, 0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 9.99, 19.99, 36.99, 39.99, 50, 50, 51, 60,
60, 69.99, 80, 81, 84, 86.99, 89.99, 100, 100, 100, 100.99,
117, 119.99, 119.99, 119.99, 125, 129.99, 130, 139.99, 140,
150, 150, 152.99, 159.99, 169.99, 170, 179.99, 184.99, 189.99,
189.99, 192.99, 199.99, 199.99, 210, 219.99, 219.99, 230,
230, 235, 248, 250, 269.99, 269.99, 269.99, 276, 279.99,
280, 282, 289.99, 289.99, 300, 300, 300, 304, 310, 310, 319.99,
319.99, 326, 329.99, 330, 335.99, 339.99, 339.99, 340, 350,
350, 360, 362, 369.99, 369.99, 369.99, 375, 379.99, 380,
383, 384, 390, 390, 399.99, 400, 419.99, 420, 423.99, 429.99,
430, 430, 430, 435.99, 440, 450, 459.99, 459.99, 465, 469.99,
479.99, 480, 480, 482, 484, 490, 490, 499.99, 500, 509.99,
519.99, 529.99, 530, 544.99, 556.99, 559.99, 560, 569.99,
569.99, 573, 579, 590, 590, 597, 609.99, 609.99, 609.99,
610, 610, 630, 630, 640, 650, 659.99, 659.99, 659.99, 667,
670, 680, 680, 680, 690, 700, 702, 709.99, 709.99, 709.99,
719.99, 719.99, 729.99, 730, 739.99, 740, 744, 745.99, 749.99,
750, 752, 756.99, 758, 759.99, 759.99, 760, 769.99, 769.99,
769.99, 780, 789.99, 789.99, 789.99, 790, 794, 809.99, 810,
810, 810, 819.99, 829.99, 829.99, 830, 840, 840, 850, 869,
870, 870, 880, 880, 890, 890, 890, 898.99, 900, 900, 909.99,
910, 919.99, 920, 930, 939.99, 940, 949.99, 950, 950, 959.99,
960, 970, 979.99, 979.99, 980, 980.99, 989.99, 990, 997.99,
1000, 1019.99, 1020, 1020, 1029.99, 1030, 1030, 1040, 1049.99,
1050, 1050, 1059.99, 1069.99, 1080, 1100, 1100, 1120, 1130,
1130.99, 1139.99, 1153, 1158, 1170, 1170, 1177, 1180, 1180,
1180, 1180, 1189.99, 1190, 1200, 1219.99, 1220, 1240, 1259.99,
1259.99, 1279, 1279.99, 1280.99, 1300, 1309.99, 1310, 1316,
1320, 1330, 1339.99, 1339.99, 1340, 1345, 1359.99, 1360,
1369, 1370, 1379.99, 1400, 1410, 1420, 1429.99, 1430.99,
1440, 1440, 1449, 1450, 1459.99, 1468.99, 1470, 1474, 1479.99,
1490, 1490, 1490, 1499.99, 1510, 1520, 1529.99, 1540, 1560,
1560, 1570, 1570, 1579.99, 1580, 1580, 1590, 1590, 1594,
1620, 1630, 1630, 1639.99, 1650, 1650, 1680, 1715, 1719.99,
1720, 1729.99, 1733, 1770, 1779.99, 1808, 1819.99, 1837,
1840, 1860, 1900, 1909.99, 1910, 1929.99, 1940, 1954, 2020,
2040, 2050, 2069.99, 2090, 2100, 2102.99, 2159.99, 2179.99,
2249.99, 2279.99, 2289.99, 2290, 2334, 2366, 2380, 2399.99,
2400, 2420, 2450, 2500, 2520, 2550, 2589.99, 2589.99, 2599.99,
2600, 2620, 2660, 2713, 2739.99, 2830, 2920, 2959.99, 3010,
3029.99, 3129.99, 3150, 3190, 3250, 3310, 3440, 3490, 3650,
3740, 3809.99, 3850, 3920, 5799.99, 5898.72, 6680, 6970)), class = c("spec_tbl_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -400L))
首先，对于 lm，您得到 NA，因为您没有指定主要术语：
fit = lm(Biomass ~ SampleMonth*Location,data=biomass)
table(is.na(coefficients(fit)))
FALSE
40
当你做双向重复方差分析时，你有一个重复测量的实验或研究，就像许多患者在一段时间内接受治疗等。在你的情况下，如果没有 "repeated measure"，或者接受相同治疗的普通个体，那么你可以使用常规方差分析。
如果您对时间段和地点的影响感兴趣：
anova_test(Biomass ~ SampleMonth+Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 SampleMonth 4 388 18.712 4.45e-14 * 0.162
2 Location 7 388 2.969 5.00e-03 * 0.051
以上假定每个月或每个位置的共同均值（或效果）。您可以有一个更复杂的模型来测试不同地点的月份效应是否存在差异：
anova_test(Biomass ~ SampleMonth*Location,data=biomass)
Coefficient covariances computed by hccm()
ANOVA Table (type II tests)
Effect DFn DFd F p p<.05 ges
1 SampleMonth 4 360 19.507 1.53e-14 * 0.178
2 Location 7 360 3.095 4.00e-03 * 0.057
3 SampleMonth:Location 28 360 1.588 3.20e-02 * 0.110