我正在使用函数traitglm()inlibrary(mvabund)进行与 GLM 分析等效的第四角分析。
我使用以下代码：
TraitTGLM = traitglm(AbundanceSO, EnvironmentSO, TraitsSO, method="manyglm", family="negative.binomial",composition = T, col.intercepts = T)
当我运行时TraitTGLM$fourth，我注意到并非所有环境列和特征行都显示出来，即这适用于分类数据。我认为这是一个 GLM 问题，而不仅仅是一个特定的 TraitGLM 问题。
我正在关注一个示例，可以在此处找到：http ://rpubs.com/dwarton/68823在标题多个站点 - “第四角问题”下。在这个关于蚂蚁的例子中，没有一个不同特征的表达和一个环境因素在例子中丢失。然而，当我复制这个例子时，我对分类数据也有同样的问题。
这是我的 TraitTGLM$fourth 输出表：
for.L for.O la lo ele soKS soPS
disba 0.71070616 0.685757845 0.77616784 0.44377270 -0.273801917 0.0160309156 -0.32739666
diszo 0.05423857 -0.049717668 0.04232755 -0.07271644 0.172564727 -0.1752525381 -0.02645799
fruca -0.03913508 -0.008395801 -0.10166100 -0.12524280 0.074426535 0.0798772617 -0.02045041
frudr -0.19656983 -0.050675426 0.08987725 0.12030174 -0.143591329 0.1858635739 0.02522805
frufo -0.13801981 0.302674330 0.84252526 -0.96825886 0.062959979 -0.4234008882 0.32380440
frule -0.11621809 -0.052756892 -0.48521160 -0.42152312 0.137112189 0.0539778588 -0.02160870
frunu -0.25605149 -0.154229876 -0.08909875 -0.36192810 -0.009765994 0.1721867978 0.22116788
frusy -0.11173499 -0.050672028 -0.05063261 0.01643027 -0.009587756 0.0579839960 -0.07135602
pgt -0.01447752 0.003283760 0.37786743 0.35312457 -0.106385213 0.0009636494 -0.07629961
polb -0.30055435 -0.198174953 -0.59764188 -0.79383934 -0.057353371 0.3043540174 0.14883309
polf -0.08534353 -0.017679336 -0.02106968 -0.15447035 -0.029297745 0.1438625680 0.01861877
poli -0.35121801 -0.210595674 -0.32407205 -0.68408985 -0.235336244 0.2990251648 0.24455372
polw -0.22586240 -0.148508546 -0.24254181 -0.31744353 0.001497815 0.0464572048 -0.02604469
polx -0.09913082 -0.088810263 -0.30873822 -0.29903480 -0.033386877 0.0907425934 0.06796934
hei 0.07403575 0.021522093 -0.31065910 -0.18872436 0.079194573 -0.0351926077 0.12850278
see -0.14464361 -0.045753203 0.44451589 0.34468547 -0.001742632 -0.0366144001 -0.12092828
woo 0.01117763 0.007088976 0.68168824 0.43018105 -0.152649283 -0.1348766220 -0.02185598
有 7 列，但应该是 9，即缺少“for.H”和“soHS”。此外，显示了 17 个性状表达，但应该是 21 个。因此，那些具有超过 1 个可能表达的环境因素和性状，即 'dis' = 6 个表达 ('a','b','f','i ','w','x')，缺少其中一个表达式。
我的数据问题是否与拦截有关？我认为，参考水平应该是“0”而不是相应的类别条目之一？
任何有关如何解决此问题的建议将不胜感激。
请找到相关数据以重现以下分析：
丰富SO：
环境SO：
> dput(EnvironmentSO)
structure(list(for. = 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, 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, 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, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L), .Label = c("H", "L", "O"), class = "factor"),
la = c(43.54247057, 43.5453036, 43.54513539, 43.54554091,
43.54569291, 43.54531157, 43.54530949, 43.54578806, 43.54565608,
43.54618175, 43.53344226, 43.53254104, 43.53424692, 43.54350591,
43.53789473, 43.53323841, 43.5386833, 43.53226745, 43.54016387,
43.53381777, 43.53272343, 43.53181684, 43.53344226, 43.53592062,
43.53398407, 43.53394115, 43.53426301, 43.53376949, 43.5280242,
43.52869475, 43.52676892, 43.52626467, 43.52498794, 43.52710688,
43.52534199, 43.52731073, 43.52534199, 43.52087879, 43.52091634,
43.52123821, 43.52058375, 43.52073932, 43.52091634, 43.52135086,
43.52137768, 43.51324522, 43.5133847, 43.51285362, 43.51310575,
43.54387069, 43.54522789, 43.54504013, 43.54276562, 43.54308212,
43.54339325, 43.54300165, 43.54345763, 43.53729928, 43.53766942,
43.53825414, 43.53773379, 43.54011023, 43.5404321, 43.53999758,
43.53366756, 43.53403771, 43.52558875, 43.52663481, 43.52792227,
43.52819049, 43.52402234, 43.52828169, 43.5278579, 43.52444077,
43.52776134, 43.52562094, 43.52537417, 43.52614665, 43.52605546,
43.52589452, 43.52646852, 43.5265168, 43.526034, 43.52467144,
43.52430129, 43.52470362, 43.52629685, 43.52902734, 43.52505231,
43.52439249, 43.52471972, 43.51933384, 43.52024043, 43.52027261,
43.54300165, 43.54258859, 43.54426229, 43.54502404, 43.53538418,
43.54441004, 43.54473972, 43.54309285, 43.54456954, 43.53641415,
43.54307675, 43.54337716, 43.54282999, 43.53600109, 43.5367682,
43.53844726, 43.53772306, 43.53476727, 43.53788936, 43.53802347,
43.53584552, 43.5390588, 43.53507304, 43.53544855, 43.53504622,
43.53284144, 43.53368366, 43.53097999, 43.52645242, 43.53316331,
43.532262, 43.53343153, 43.52754676, 43.52731609, 43.52477336,
43.52390432, 43.52530444, 43.52433348, 43.51094925, 43.51146415,
43.51183438, 43.51266587, 43.51284826, 43.51244056, 43.51255858
), lo = c(122.9251818, 122.9266832, 122.9271353, 122.9272055,
122.9273281, 122.9274899, 122.9276568, 122.9276631, 122.9281064,
122.9286789, 122.928615, 122.9291568, 122.9292373, 122.9296074,
122.9295484, 122.9297844, 122.9298917, 122.9299668, 122.9302565,
122.9301814, 122.9305837, 122.9306696, 122.9308895, 122.9311631,
122.9314957, 122.9334859, 122.933502, 122.9341832, 122.9361144,
122.9365436, 122.9373375, 122.9374019, 122.9379652, 122.9384319,
122.9394726, 122.9399929, 122.9411302, 122.9490212, 122.9492304,
122.9492519, 122.9493806, 122.9495148, 122.9496381, 122.9498527,
122.95077, 122.9510329, 122.9514352, 122.951505, 122.9519717,
122.9225156, 122.9248492, 122.9251603, 122.9262707, 122.9268662,
122.9269627, 122.9273543, 122.9281268, 122.9285184, 122.9288671,
122.9289529, 122.9290173, 122.9293821, 122.9298112, 122.9299454,
122.9335878, 122.9340438, 122.9342744, 122.9343871, 122.9351596,
122.9351864, 122.935181, 122.9356316, 122.9357336, 122.9356906,
122.9357926, 122.935814, 122.9359911, 122.9364685, 122.9365275,
122.9368011, 122.9369298, 122.9371551, 122.9373161, 122.9373429,
122.9377828, 122.937874, 122.9379222, 122.9382066, 122.9384104,
122.938405, 122.9389361, 122.9465697, 122.9479483, 122.9487584,
122.9189161, 122.9236583, 122.9239479, 122.9243127, 122.9243127,
122.924474, 122.9249457, 122.925804, 122.9261636, 122.9261634,
122.9264048, 122.9266141, 122.9266838, 122.9272202, 122.9277138,
122.9278532, 122.9279391, 122.9280034, 122.9285184, 122.9285989,
122.9290334, 122.9292426, 122.9292587, 122.9303155, 122.9311631,
122.9322091, 122.9323272, 122.9330031, 122.9329441, 122.9336522,
122.933974, 122.9340974, 122.9348109, 122.9354332, 122.93627,
122.9367421, 122.9370049, 122.9382924, 122.9469667, 122.9486618,
122.9501853, 122.9504911, 122.9510168, 122.9510275, 122.951403
), ele = c(487, 476, 474, 474, 468, 470, 466, 464, 464, 464,
494, 499, 304, 474, 480, 497, 479, 300, 480, 304, 498, 496,
306, 494, 308, 488, 484, 487, 301, 496, 300, 301, 311, 497,
304, 490, 494, 481, 475, 474, 480, 475, 470, 468, 465, 465,
463, 461, 460, 303, 488, 487, 480, 480, 484, 480, 479, 491,
485, 483, 483, 484, 481, 483, 486, 484, 334, 345, 311, 304,
333, 303, 306, 334, 303, 343, 319, 315, 315, 309, 308, 305,
305, 318, 315, 314, 499, 489, 307, 308, 306, 300, 486, 481,
330, 497, 495, 490, 346, 490, 489, 483, 485, 315, 479, 484,
478, 487, 305, 495, 301, 486, 488, 485, 489, 490, 488, 490,
491, 493, 494, 303, 337, 494, 494, 486, 340, 313, 330, 344,
317, 311, 313, 300, 494, 477, 460, 470, 469), so = structure(c(1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 1L,
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 1L, 1L,
1L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("HS", "KS", "PS"), class = "factor")), .Names = c("for.",
"la", "lo", "ele", "so"), row.names = c("H01", "H02", "H03",
"H04", "H05", "H06", "H07", "H08", "H09", "H10", "H11", "H12",
"H13", "H14", "H15", "H16", "H17", "H18", "H19", "H20", "H21",
"H22", "H23", "H24", "H25", "H26", "H27", "H28", "H29", "H30",
"H31", "H32", "H33", "H34", "H35", "H36", "H37", "H38", "H39",
"H40", "H41", "H42", "H43", "H44", "H45", "H46", "H47", "H48",
"H49", "L01", "L02", "L03", "L04", "L05", "L06", "L07", "L08",
"L09", "L10", "L11", "L12", "L13", "L14", "L15", "L16", "L17",
"L18", "L19", "L20", "L21", "L22", "L23", "L24", "L25", "L26",
"L27", "L28", "L29", "L30", "L31", "L32", "L33", "L34", "L35",
"L36", "L37", "L38", "L39", "L40", "L41", "L42", "L43", "L44",
"L45", "O01", "O02", "O03", "O04", "O05", "O06", "O07", "O08",
"O09", "O10", "O11", "O12", "O13", "O14", "O15", "O16", "O17",
"O18", "O19", "O20", "O21", "O22", "O23", "O24", "O25", "O26",
"O27", "O28", "O29", "O30", "O31", "O32", "O33", "O34", "O35",
"O36", "O37", "O38", "O39", "O40", "O41", "O42", "O43", "O44",
"O45"), class = "data.frame")
特质SO：
> dput(TraitsSO)
structure(list(dis = structure(c(1L, 1L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 2L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 1L, 1L,
1L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 1L, 3L, 1L,
3L, 3L, 3L, 1L, 1L, 3L, 1L), .Label = c("an", "ba", "zo"), class = "factor"),
fru = structure(c(5L, 5L, 3L, 2L, 7L, 3L, 3L, 3L, 2L, 1L,
3L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 6L, 6L, 5L, 2L, 5L, 5L, 1L,
1L, 1L, 3L, 3L, 2L, 4L, 3L, 3L, 2L, 3L, 1L, 3L, 5L, 2L, 7L,
1L, 3L, 3L, 1L, 3L, 3L, 5L, 2L, 3L, 5L, 5L, 5L, 5L, 4L, 2L,
1L, 3L, 3L, 3L, 3L, 3L, 5L), .Label = c("be", "ca", "dr",
"fo", "le", "nu", "sy"), class = "factor"), pg = structure(c(2L,
2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L,
2L), .Label = c("s", "t"), class = "factor"), pol = structure(c(2L,
2L, 2L, 4L, 2L, 5L, 4L, 4L, 1L, 4L, 4L, 6L, 4L, 2L, 2L, 4L,
1L, 4L, 1L, 1L, 4L, 4L, 4L, 2L, 4L, 4L, 4L, 2L, 2L, 2L, 4L,
4L, 4L, 2L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 2L, 4L, 4L, 2L, 2L,
4L, 4L, 2L, 4L, 4L, 2L, 3L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L,
4L), .Label = c("a", "b", "f", "i", "w", "x"), class = "factor"),
hei = c(30, 35, 20, 10, 9, 20, 25, 35, 20, 15, 10, 30, 30,
14, 30, 35, 16, 35, 30, 25, 30, 12, 35, 26, 12, 10, 10, 30,
15, 30, 10, 50, 10, 17, 40, 7, 4, 7, 30, 10, 12, 30, 6, 30,
7, 18, 30, 8, 40, 30, 40, 30, 35, 20, 25, 15, 10, 7, 35,
40, 30, 40), see = c(3, 3, 1, 1, 4, 2, 1, 2, 3, 4, 2, 2,
2, 2, 2, 4, 3, 3, 1, 1, 4, 4, 3, 2, 2, 4, 4, 2, 2, 4, 4,
1, 1, 4, 1, 2, 2, 2, 4, 1, 3, 2, 2, 1, 3, 3, 2, 4, 2, 1,
1, 1, 2, 4, 4, 3, 2, 2, 1, 1, 1, 3), woo = c(1.61, 1.64,
1.61, 1.62, 1.64, 1.61, 1.39, 1.42, 1.73, 1.75, 1.51, 1.77,
1.75, 1.6, 1.88, 1.55, 1.61, 1.72, 1.64, 1.72, 1.65, 1.7,
1.85, 1.74, 1.55, 1.76, 1.76, 1.43, 1.67, 1.48, 1.73, 1.88,
1.4, 1.46, 1.48, 1.67, 1.67, 1.88, 1.53, 1.48, 1.74, 1.64,
1.62, 1.51, 1.64, 1.64, 1.7, 1.64, 1.9, 1.85, 1.7, 1.86,
1.72, 1.43, 1.57, 1.74, 1.52, 1.69, 1.75, 1.45, 1.88, 1.68
)), .Names = c("dis", "fru", "pg", "pol", "hei", "see", "woo"
), row.names = c("albleb", "albodo", "antgha", "apovil", "artlak",
"briret", "buclan", "cansub", "carsph", "catspa", "cropoi", "dalcul",
"dallan", "dalnig", "daloli", "dilobo", "dioehr", "diomal", "dipint",
"diptub", "elltom", "erican", "erysuc", "flesoo", "garcow", "garobt",
"garsoo", "gmearb", "greeri", "halcor", "holpub", "irvoli", "lancor",
"lopdup", "mancal", "memedu", "memscu", "milleu", "mitrot", "morcor",
"ochint", "parama", "pavtom", "permem", "phycol", "phyemb", "ptemac",
"rotwit", "schole", "shoobt", "shorox", "shosia", "sinsia", "stegut",
"steneu", "strnux", "symrac", "syz001", "terala", "tercal", "terche",
"xylxyl"), class = "data.frame")