关于物种丰度和性状数据的函数 manyglm()
Function manyglm() on species abundance and trait data
我正在尝试 运行 使用以下代码进行 glm 分析：
ftP <- manyglm(AbundVec ~ as.matrix(traitVec)*trV, family = "poisson")
我目前收到以下错误消息：
错误 [[<-.data.frame(*tmp*, i, value = c(54L, 54L, 54L, 54L, 54L, :
替换有 60326 行，数据有 8618
作为第一步，我根据以下内容为丰度数据创建了一个物种 x 绘图矩阵：
AbundMat <- acast(Abund, plot~species, value.var="occurrence", fun.aggregate=length)
输出：class(AbundMat)=矩阵，typeof(AbundMat)=整数，storage.mode(AbundMat)=整数，长度(AbundMat)=8618，属性(AbundMat)= 139(地块）62（种）
然后我导入文件：
1) 特征：data.frame 62obs。和 7 个变量（包括 Factors 和 num）；包括 ; typeof(Traits)=list 和 class(Traits)=df
2) 治疗：data.frame 139obs。和 2 个变量（$plot 和 $site 作为因素）； typeof(Treatment)=list and class(Treatment)=df
然后我按照论文附录中描述的示例对丰度数据进行向量化：CATS 回归 - 一种基于模型的方法来研究基于特征的社区组装 (http://onlinelibrary.wiley.com/store/10.1111/2041-210X.12280/asset/supinfo/mee312280-sup-0003-AppendixS3.pdf?v=1&s=0037b03799e63903d896ba208fb2a6cc1b5605d0)：
AbundVec <- as.vector(AbundMat)
n.rows <- NROW(AbundMat)
n.vars <- NCOL(AbundMat)
sppVec <- rep(row.names(Traits), each=n.rows)
traitVec <- Traits[rep(1:n.vars, each = n.rows),]
siteVec <- rep(row.names(AbundMat), n.vars)
Treatment <- as.factor(Treatment)
levels(Treatment) <- c("A", "B", "C")
trV <- rep(Treatment, n.vars)
最后，我执行 glm（需要包 mvabund）：
ftP <- manyglm(AbundVec ~ as.matrix(traitVec)*trV, family = "poisson")
A这个阶段我得到上面提到的错误信息：
错误 [[<-.data.frame(*tmp*, i, value = c(54L, 54L, 54L, 54L, 54L, :
替换有 60326 行，数据有 8618
请注意8618*7=60326（我有7个特征变量）
当我将我的数据文件与 CATS 回归论文中描述的丰度示例数据文件进行比较时，我注意到示例数据文件具有以下 str() 输出：
str(GrazedPlants$abundMat)
num [1:32, 1:68] 0 210 195 1 617 0 1 75 1 0 ...
- attr(, "dimnames")=List of 2
..$ : chr [1:32] "FC01" "FC02" "FC03" "FC04" ...
..$ : atomic [1:68] CAPSBURS CREPVESI.HAE GALICORR MUSCNEGL ...
.. ..- attr(, "levels")= chr [1:83] "ALYSALYS" "ANTHVULN" "APHYMONS" "ARENSERP" ...
→ typeof = double, class = matrix
当我对我的数据 运行 str() 时，我反而得到：
str(AbundMat)
int [1:139, 1:62] 0 0 0 0 0 0 0 0 0 1 ...
- attr(*, "dimnames")=List of 2
..$ : chr [1:139] "A01" "A02" "A03" "A04" ...
..$ : chr [1:62] "sp01" "sp02" "sp03" "sp04" ...
→ typeof = 整数，class = 矩阵
我的 Treatment 和 Traits 数据格式似乎与示例数据集的格式相匹配，只是我的大部分特征都是因素（而在示例中它们都是数字）。
所以，我想知道错误消息是否是由于 'AbundMat' 不是所需的形式 class=double（而是整数）？
此外，根据 str() 输出，'AbundMat' 中似乎缺少属性（级别）。
是否可以生成双精度类型的矩阵（类似于示例数据矩阵）？
============ 补充说明：==========================
作为附加说明，此代码可用于强制数据与函数 manyglm() 一起使用。它只使用一行数据（一个图）。我想知道如何针对多个站点调整此代码，即我数据集中的 139 个地块？
y2 = c(t(grazedPlants$abundMat[1,])) # coercing the first row into a vector
y2
singleSite2 = data.frame(y2, grazedPlants$traits, check.names = F) # make a data frame from site abundances and traits
singleSite2
ft5 = manyglm(y2 ~ ., data = singleSite2, family = "negative.binomial")
ft5
summary(ft5, nBoot=1) # find which traits are important
plot(ft5)
============================================= =======
非常感谢您的任何建议。
这些是我的数据集：
1) 物种 x 地块丰度（计数）数据（文件名：Abund）
structure(list(plot = structure(c(47L, 47L, 48L, 51L, 53L, 54L), .Label = c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "A22", "A23", "A24", "A25", "A26", "A27", "A28", "A29", "A30", "A31", "A32", "A33", "A34", "A35", "A36", "A37", "A38", "A39", "A40", "A41", "A42", "A43", "A44", "A45", "A46", "A47", "A48", "A49", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "B10", "B11", "B12", "B13", "B14", "B15", "B16", "B17", "B18", "B19", "B20", "B21", "B22", "B23", "B24", "B25", "B26", "B27", "B28", "B29", "B30", "B31", "B32", "B33", "B34", "B35", "B36", "B37", "B38", "B39", "B40", "B41", "B42", "B43", "B44", "B45", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14", "C15","C16", "C17", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "C42", "C43", "C44", "C45"), class = "factor"),
species = structure(c(46L, 47L, 47L, 47L, 47L, 47L), .Label = c("sp1",
"sp10", "sp11", "sp12", "sp13", "sp14", "sp15", "sp16", "sp17",
"sp18", "sp19", "sp2", "sp20", "sp21", "sp22", "sp23", "sp24",
"sp25", "sp26", "sp27", "sp28", "sp29", "sp3", "sp30", "sp31",
"sp32", "sp33", "sp34", "sp35", "sp36", "sp37", "sp38", "sp39",
"sp4", "sp40", "sp41", "sp42", "sp43", "sp44", "sp45", "sp46",
"sp47", "sp48", "sp49", "sp5", "sp50", "sp51", "sp52", "sp53",
"sp54", "sp55", "sp56", "sp57", "sp58", "sp59", "sp6", "sp60",
"sp61", "sp62", "sp7", "sp8", "sp9"), class = "factor"),
occurrence = c(1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("plot", "species", "occurrence"), row.names = c(NA, 6L), class = "data.frame")
2) 物种 x 性状数据（文件名：Traits）
structure(list(species = structure(c(1L, 12L, 23L, 34L, 45L, 56L, 60L, 61L, 62L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 57L, 58L, 59L), .Label = c("sp1", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15", "sp16", "sp17", "sp18", "sp19", "sp2", "sp20", "sp21", "sp22", "sp23", "sp24", "sp25", "sp26", "sp27", "sp28", "sp29", "sp3", "sp30", "sp31", "sp32", "sp33", "sp34", "sp35", "sp36", "sp37", "sp38", "sp39", "sp4", "sp40", "sp41", "sp42", "sp43", "sp44", "sp45", "sp46", "sp47", "sp48", "sp49", "sp5", "sp50", "sp51", "sp52", "sp53", "sp54", "sp55", "sp56", "sp57", "sp58", "sp59", "sp6", "sp60", "sp61", "sp62", "sp7", "sp8", "sp9"), class = "factor"), trait1 = structure(c(3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L), .Label = c("<NA>", "a", "b"), class = "factor"), trait2 = structure(c(6L, 6L, 4L, 3L, 8L, 4L, 4L, 4L, 3L, 1L, 1L, 6L, 6L, 6L, 6L, 1L, 2L, 2L, 7L, 7L, 6L, 3L, 6L, 6L, 2L, 1L, 2L, 4L, 4L, 3L, 5L, 4L, 4L, 1L, 4L, 2L, 4L, 6L, 3L, 8L, 2L, 4L, 4L, 1L, 1L, 4L, 6L, 1L, 4L, 6L, 6L, 6L, 6L, 5L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 6L), .Label = c("<NA>", "c", "d", "e", "f", "g", "h", "i"), class = "factor"), trait3 = structure(c(4L, 4L, 4L, 6L, 4L, 1L, 6L, 2L, 2L, 2L, 2L, 1L, 6L, 4L, 4L, 6L, 3L, 6L, 3L, 3L, 6L, 2L, 2L, 2L, 6L, 2L, 6L, 4L, 4L, 4L, 6L, 2L, 6L, 2L, 6L, 2L, 2L, 2L, 6L, 6L, 6L, 2L, 6L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 6L, 4L, 5L, 4L, 4L, 2L, 2L, 2L, 6L, 2L, 6L, 6L), .Label = c("", "<NA>", "j", "k", "l", "m"), class = "factor"), trait4 = structure(c(4L, 4L, 2L, 2L, 5L, 3L, 2L, 3L, 1L, 5L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 4L, 2L, 2L, 1L, 5L, 1L, 3L, 3L, 1L, 5L, 3L, 1L, 5L, 5L, 2L, 2L, 1L, 1L, 3L, 1L, 3L, 5L, 2L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 5L, 3L, 2L, 2L, 2L, 3L, 5L, 5L, 4L, 3L, 1L, 2L, 1L, 2L, 4L), .Label = c("<NA>", "1", "2", "3", "4"), class = "factor"), trait5 = structure(c(12L, 14L, 9L, 2L, 21L, 9L, 1L, 14L, 9L, 22L, 2L, 12L, 12L, 4L, 12L, 14L, 6L, 14L, 12L, 11L, 12L, 3L, 1L, 1L, 3L, 1L, 2L, 12L, 5L, 12L, 2L, 17L, 2L, 1L, 16L, 19L, 15L, 19L, 12L, 10L, 3L, 12L, 18L, 12L, 19L, 8L, 12L, 20L, 16L, 12L, 7L, 12L, 13L, 9L, 11L, 5L, 2L, 1L, 14L, 11L, 12L, 16L), .Label = c("<NA>", "10", "12", "14", "15", "16", "17", "18", "20", "23", "25", "30", "32", "35", "4", "40", "50", "6", "7", "8", "9", "small"), class = "factor"),
trait6 = structure(c(3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L), .Label = c("<NA>", "p",
"q"), class = "factor"), trait7 = c(0.61, 0.64, 0.61, 0.62,
0.64, 0.61, 0.39, 0.13, 0.73, 0.75, 0.51, 0.77, 0.75, 0.6,
0.88, 0.55, 0.61, 0.72, 0.64, 0.72, 0.65, 0.7, 0.85, 0.74,
0.55, 0.76, 0.76, 0.43, 0.67, 0.48, 0.73, 0.88, 0.4, 0.46,
0.48, 0.67, 0.67, 0.88, 0.53, 0.48, 0.93, 0.64, 0.62, 0.47,
0.64, 0.64, 0.7, 0.64, 0.9, 0.85, 0.7, 0.86, 0.72, 0.43,
0.33, 0.74, 0.52, 0.69, 0.75, 0.45, 0.88, 0.68)), .Names = c("species", "trait1", "trait2", "trait3", "trait4", "trait5", "trait6", "trait7"), class = "data.frame", row.names = c(NA, -62L))
3）治疗数据（文件名：Treatment）
structure(list(plot = structure(1:139, .Label = c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "A22", "A23", "A24", "A25", "A26", "A27", "A28", "A29", "A30", "A31", "A32", "A33", "A34", "A35", "A36", "A37", "A38", "A39", "A40", "A41", "A42", "A43", "A44", "A45", "A46", "A47", "A48", "A49", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "B10", "B11", "B12", "B13", "B14", "B15", "B16", "B17", "B18", "B19", "B20", "B21", "B22", "B23", "B24", "B25", "B26", "B27", "B28", "B29", "B30", "B31", "B32", "B33", "B34", "B35", "B36", "B37", "B38", "B39", "B40", "B41", "B42", "B43", "B44", "B45", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "C42", "C43", "C44", "C45"), class = "factor"), site = 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("A", "B", "C"), class = "factor")), .Names = c("plot", "site"), class = "data.frame", row.names = c(NA, -139L))
此外，这些是 'grazedPlants.RData' 文件的子集：
dput(头(grazedPlants$abundMat))
structure(list(X = structure(1:6, .Label = c("FC01", "FC02",
"FC03", "FC04", "FC05", "FC06", "FC07", "FC08", "NC01", "NC02",
"NC03", "NC04", "NC05", "NC06", "NC07", "NC08", "ND01", "ND02",
"ND03", "ND04", "ND05", "ND06", "ND07", "ND08", "TD01", "TD02",
"TD03", "TD04", "TD05", "TD06", "TD07", "TD08"), class = "factor"),
CAPSBURS = c(0L, 210L, 195L, 1L, 617L, 0L), CREPVESI.HAE = c(0L,
0L, 0L, 0L, 0L, 0L), GALICORR = c(0L, 0L, 0L, 0L, 0L, 0L),
MUSCNEGL = c(0L, 0L, 0L, 0L, 0L, 0L), BROMEREC = c(1L, 0L,
0L, 0L, 0L, 0L), ARENSERP = c(0L, 0L, 1L, 0L, 4L, 0L), VULPMYUR = c(0L,
28L, 3L, 0L, 0L, 8L), CERAPUMI = c(0L, 2L, 0L, 0L, 0L, 0L
), ORNICOLL = c(0L, 1L, 0L, 0L, 0L, 1L), VEROARVE = c(0L,
0L, 0L, 0L, 0L, 0L), GERAROTU = c(0L, 0L, 0L, 80L, 0L, 41L
), ERODCICU = c(0L, 0L, 0L, 1L, 0L, 3L), BROMHORD = c(0L,
735L, 0L, 72L, 0L, 4L), POABULB = c(0L, 209L, 110L, 2L, 0L,
0L), HORDMURI = c(5L, 11L, 0L, 0L, 0L, 0L), TARALAEV = c(0L,
0L, 0L, 0L, 0L, 0L), SANGMINO = c(0L, 0L, 0L, 0L, 1L, 0L),
TRIFSCAB = c(0L, 1L, 25L, 0L, 0L, 0L), CREPSANC.SAN = c(0L,
0L, 0L, 0L, 0L, 0L), ERYNCAMP = c(1L, 0L, 0L, 0L, 0L, 0L),
BROMDIAN = c(0L, 0L, 0L, 0L, 0L, 1L), STELMEDI = c(0L, 0L,
0L, 0L, 0L, 0L), ALYSALYS = c(0L, 0L, 0L, 0L, 0L, 0L), CERAGLOM = c(17L,
5L, 2L, 0L, 21L, 0L), EROPVERN = c(0L, 0L, 0L, 0L, 0L, 0L
), FESTRUBR = c(30L, 0L, 14L, 0L, 69L, 0L), FILAPYRA = c(0L,
0L, 9L, 0L, 0L, 0L), SCLEANNU = c(0L, 0L, 0L, 0L, 0L, 0L),
CAREHALL = c(80L, 21L, 0L, 2L, 0L, 368L), POTENEUM = c(2L,
1L, 0L, 0L, 0L, 0L), VICITETR.GRA = c(0L, 0L, 0L, 1L, 0L,
0L), SHERARVE = c(0L, 0L, 0L, 0L, 0L, 0L), POLYCALC = c(0L,
0L, 0L, 2L, 0L, 0L), VICISATI.SAT = c(0L, 0L, 0L, 0L, 0L,
0L), FILIVULG = c(0L, 0L, 0L, 126L, 16L, 0L), MYOSRAMO.RAM = c(0L,
56L, 0L, 0L, 0L, 27L), CAREHUMI = c(0L, 3L, 9L, 0L, 0L, 7L
), TARAOFFI = c(2L, 0L, 0L, 0L, 0L, 0L), HIPPCOMO = c(1L,
1L, 0L, 3L, 1L, 0L), FESTCHRI = c(0L, 0L, 0L, 0L, 13L, 0L
), KOELVALL = c(0L, 0L, 0L, 0L, 0L, 0L), ONONSTRI = c(0L,
0L, 0L, 0L, 0L, 0L), GENIHISP = c(0L, 0L, 0L, 0L, 0L, 0L),
CARTMITI = c(0L, 0L, 0L, 0L, 0L, 0L), ANTHVULN = c(0L, 0L,
0L, 0L, 0L, 0L), CENTPECT.SUP = c(0L, 0L, 0L, 0L, 0L, 0L),
LOTUCORN = c(0L, 0L, 0L, 0L, 0L, 1L), PLANLANC = c(0L, 0L,
0L, 1L, 0L, 0L), RANUBULB = c(0L, 0L, 0L, 0L, 15L, 0L), CHAMSAGI = c(0L,
0L, 0L, 0L, 0L, 0L), TEUCPOLI = c(0L, 0L, 0L, 0L, 0L, 0L),
THYMSERP = c(0L, 0L, 0L, 0L, 0L, 0L), COROMINI = c(0L, 0L,
0L, 0L, 0L, 0L), SALVPRAT = c(0L, 0L, 0L, 2L, 0L, 0L), ASTRMONS = c(0L,
0L, 0L, 0L, 0L, 0L), HIERPILO = c(1L, 0L, 0L, 0L, 0L, 0L),
LINUTENU.TEN = c(0L, 0L, 0L, 0L, 0L, 0L), APHYMONS = c(0L,
0L, 2L, 0L, 0L, 0L), TEUCCHAM = c(0L, 0L, 1L, 0L, 0L, 0L),
HELICANU = c(0L, 0L, 1L, 0L, 16L, 6L), HELINUMM = c(2L, 0L,
0L, 0L, 0L, 0L), CAREFLAC = c(18L, 0L, 0L, 57L, 0L, 8L),
STIPPENN = c(125L, 26L, 0L, 0L, 0L, 0L), INULMONT = c(0L,
0L, 1L, 0L, 0L, 0L), TEUCMONT = c(0L, 0L, 0L, 0L, 0L, 0L),
GLOBVULG = c(0L, 0L, 0L, 0L, 1L, 0L), HELIAPEN = c(0L, 1L,
0L, 0L, 0L, 0L), RANUGRAM = c(0L, 0L, 0L, 2L, 0L, 0L)), .Names = c("X",
"CAPSBURS", "CREPVESI.HAE", "GALICORR", "MUSCNEGL", "BROMEREC",
"ARENSERP", "VULPMYUR", "CERAPUMI", "ORNICOLL", "VEROARVE", "GERAROTU",
"ERODCICU", "BROMHORD", "POABULB", "HORDMURI", "TARALAEV", "SANGMINO",
"TRIFSCAB", "CREPSANC.SAN", "ERYNCAMP", "BROMDIAN", "STELMEDI",
"ALYSALYS", "CERAGLOM", "EROPVERN", "FESTRUBR", "FILAPYRA", "SCLEANNU",
"CAREHALL", "POTENEUM", "VICITETR.GRA", "SHERARVE", "POLYCALC",
"VICISATI.SAT", "FILIVULG", "MYOSRAMO.RAM", "CAREHUMI", "TARAOFFI",
"HIPPCOMO", "FESTCHRI", "KOELVALL", "ONONSTRI", "GENIHISP", "CARTMITI",
"ANTHVULN", "CENTPECT.SUP", "LOTUCORN", "PLANLANC", "RANUBULB",
"CHAMSAGI", "TEUCPOLI", "THYMSERP", "COROMINI", "SALVPRAT", "ASTRMONS",
"HIERPILO", "LINUTENU.TEN", "APHYMONS", "TEUCCHAM", "HELICANU",
"HELINUMM", "CAREFLAC", "STIPPENN", "INULMONT", "TEUCMONT", "GLOBVULG",
"HELIAPEN", "RANUGRAM"), row.names = c(NA, 6L), class = "data.frame")
dput(头(grazedPlants$traits))
structure(list(X = structure(c(9L, 20L, 28L, 42L, 7L, 4L), .Label = c("ALYSALYS", "ANTHVULN", "APHYMONS", "ARENSERP", "ASTRMONS", "BROMDIAN", "BROMEREC", "BROMHORD", "CAPSBURS", "CAREFLAC", "CAREHALL", "CAREHUMI", "CARTMITI", "CENTPECT.SUP", "CERAGLOM", "CERAPUMI", "CHAMSAGI", "COROMINI", "CREPSANC.SAN", "CREPVESI.HAE", "ERODCICU", "EROPVERN", "ERYNCAMP", "FESTCHRI", "FESTRUBR", "FILAPYRA", "FILIVULG", "GALICORR", "GENIHISP", "GERAROTU", "GLOBVULG", "HELIAPEN", "HELICANU", "HELINUMM", "HIERPILO", "HIPPCOMO", "HORDMURI", "INULMONT", "KOELVALL", "LINUTENU.TEN", "LOTUCORN", "MUSCNEGL", "MYOSRAMO.RAM", "ONONSTRI", "ORNICOLL", "PLANLANC", "POABULB", "POLYCALC", "POTENEUM", "RANUBULB", "RANUGRAM", "SALVPRAT", "SANGMINO", "SCLEANNU", "SHERARVE", "STELMEDI", "STIPPENN", "TARALAEV", "TARAOFFI", "TEUCCHAM", "TEUCMONT", "TEUCPOLI", "THYMSERP", "TRIFSCAB", "VEROARVE", "VICISATI.SAT", "VICITETR.GRA", "VULPMYUR"), class = "factor"), SM = c(0.587, -0.81, 0.446, 0.195, 0.357, -0.128), OFL = c(136.1359, 131.9466, 148.8626, 148.5717, 156.9126, 149.2011), RPH = c(1.4092797, 1.0612845, 0.729711, 0.7335391, 1.7941586, 1.4624131), VPH = c(1.12952674, 0.86161103, 0.54418607, 0.44006594, 1.49088504, 1.01091915), SLA = c(1.0997531, 1.0612576, 1.4866071, 1.0666007, 0.9769541, 1.1077584), LDMC = c(2.491384, 2.446196, 2.227827, 2.476604, 2.717145, 2.347065), LA = c(0.58658981, 0.29217792, 0.53604022, 0.07529993, 0.28781845, 0.68114674),
LNC = c(1.46497, 1.31874, 1.417831, 1.277482, 1.302021, 1.363778
), LCC = c(2.632749, 2.621413, 2.6114, 2.653969, 2.637665,
2.630081), LPC = c(0.40060595, 0.305709643, 0.543668171,
0.225178098, 0.135509475, 0.354213077)), .Names = c("X", "SM", "OFL", "RPH", "VPH", "SLA", "LDMC", "LA", "LNC", "LCC", "LPC"), row.names = c(NA, 6L), class = "data.frame"))
dput(头(grazedPlants$treatment))
structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Names = c("FC01", "FC02",
"FC03", "FC04", "FC05", "FC06"), .Label = c("Fertilised (Limestone)",
"Pasture (Limestone)", "Pasture (Dolomite)", "Ungrazed (Dolomite)"
), class = "factor")
在这里使用您最近发布的对象（丰富、特质和治疗）和将它们与发布的 link 对象（abund、traits 和 treatment）进行比较，您的根本问题是 Traits 不包含数字列。其他问题包括 rownames() 和 $ 赋值。
作者的特征（数字列）
head(traits)
# SM OFL RPH VPH SLA LDMC LA LNC LCC LPC
# CAPSBURS 0.5872967 136.1359 1.4092797 1.1295267 1.0997531 2.491384 0.58658981 1.464970 2.632749 0.4006060
# CREPVESI.HAE -0.8103945 131.9466 1.0612845 0.8616110 1.0612576 2.446196 0.29217792 1.318740 2.621413 0.3057096
# GALICORR 0.4463238 148.8626 0.7297110 0.5441861 1.4866071 2.227827 0.53604022 1.417831 2.611400 0.5436682
# MUSCNEGL 0.1951452 148.5717 0.7335391 0.4400659 1.0666007 2.476604 0.07529993 1.277482 2.653969 0.2251781
# BROMEREC 0.3571080 156.9126 1.7941586 1.4908850 0.9769541 2.717145 0.28781845 1.302021 2.637665 0.1355095
# ARENSERP -0.1283229 149.2011 1.4624131 1.0109192 1.1077584 2.347065 0.68114674 1.363778 2.630081 0.3542131
你的特征（因子列 + 应该是 rownames 的物种）
head(Traits)
# species trait1 trait2 trait3 trait4 trait5 trait6 trait7
# 1 sp1 b g k 3 30 q 0.61
# 2 sp2 b g k 3 35 q 0.64
# 3 sp3 a e k 1 20 q 0.61
# 4 sp4 a d m 1 10 p 0.62
# 5 sp5 a i k 4 9 q 0.64
# 6 sp6 a e 2 20 q 0.61
调整 rownames 并将 factor 类型转换为 numeric，然后您应该可以调用 manyglm().如果此处使用的这种转换没有意义，请务必调整 Traits 以反映数字。
row.names(Traits) <- Traits$species # ASSIGNING ROW NAMES TO SPECIES
Traits <- Traits[-1] # REMOVING FIRST COLUMN
# CONVERT COLUMNS TO NUMERIC
Traits[, c(1:ncol(Traits))] <- sapply(Traits[, c(1:ncol(Traits))], as.numeric)
AbundVec <- as.vector(as.numeric(AbundMat))
N.rows <- NROW(AbundMat)
N.vars <- NCOL(AbundMat)
SppVec <- rep(row.names(Traits), each=N.rows)
TraitVec <- Traits[rep(1:N.vars, each=N.rows),]
SiteVec <- rep(row.names(AbundMat), N.vars)
Treatment = as.factor(Treatment$site) # SELECT SPECIFIC COLUMN
levels(Treatment) <- c("A", "B", "C")
TrV <- rep(Treatment, N.vars)
FtP <- manyglm(AbundVec ~ as.matrix(TraitVec)*TrV, family = "poisson")
我正在尝试 运行 使用以下代码进行 glm 分析：
ftP <- manyglm(AbundVec ~ as.matrix(traitVec)*trV, family = "poisson")
我目前收到以下错误消息：
错误 [[<-.data.frame(*tmp*, i, value = c(54L, 54L, 54L, 54L, 54L, :
替换有 60326 行，数据有 8618
作为第一步，我根据以下内容为丰度数据创建了一个物种 x 绘图矩阵：
AbundMat <- acast(Abund, plot~species, value.var="occurrence", fun.aggregate=length)
输出：class(AbundMat)=矩阵，typeof(AbundMat)=整数，storage.mode(AbundMat)=整数，长度(AbundMat)=8618，属性(AbundMat)= 139(地块）62（种）
然后我导入文件：
1) 特征：data.frame 62obs。和 7 个变量（包括 Factors 和 num）；包括 ; typeof(Traits)=list 和 class(Traits)=df
2) 治疗：data.frame 139obs。和 2 个变量（$plot 和 $site 作为因素）； typeof(Treatment)=list and class(Treatment)=df
然后我按照论文附录中描述的示例对丰度数据进行向量化：CATS 回归 - 一种基于模型的方法来研究基于特征的社区组装 (http://onlinelibrary.wiley.com/store/10.1111/2041-210X.12280/asset/supinfo/mee312280-sup-0003-AppendixS3.pdf?v=1&s=0037b03799e63903d896ba208fb2a6cc1b5605d0)：
AbundVec <- as.vector(AbundMat)
n.rows <- NROW(AbundMat)
n.vars <- NCOL(AbundMat)
sppVec <- rep(row.names(Traits), each=n.rows)
traitVec <- Traits[rep(1:n.vars, each = n.rows),]
siteVec <- rep(row.names(AbundMat), n.vars)
Treatment <- as.factor(Treatment)
levels(Treatment) <- c("A", "B", "C")
trV <- rep(Treatment, n.vars)
最后，我执行 glm（需要包 mvabund）：
ftP <- manyglm(AbundVec ~ as.matrix(traitVec)*trV, family = "poisson")
A这个阶段我得到上面提到的错误信息：
错误 [[<-.data.frame(*tmp*, i, value = c(54L, 54L, 54L, 54L, 54L, :
替换有 60326 行，数据有 8618
请注意8618*7=60326（我有7个特征变量）
当我将我的数据文件与 CATS 回归论文中描述的丰度示例数据文件进行比较时，我注意到示例数据文件具有以下 str() 输出：
str(GrazedPlants$abundMat) num [1:32, 1:68] 0 210 195 1 617 0 1 75 1 0 ... - attr(, "dimnames")=List of 2 ..$ : chr [1:32] "FC01" "FC02" "FC03" "FC04" ... ..$ : atomic [1:68] CAPSBURS CREPVESI.HAE GALICORR MUSCNEGL ... .. ..- attr(, "levels")= chr [1:83] "ALYSALYS" "ANTHVULN" "APHYMONS" "ARENSERP" ...
→ typeof = double, class = matrix
当我对我的数据 运行 str() 时，我反而得到：
str(AbundMat) int [1:139, 1:62] 0 0 0 0 0 0 0 0 0 1 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:139] "A01" "A02" "A03" "A04" ... ..$ : chr [1:62] "sp01" "sp02" "sp03" "sp04" ...
→ typeof = 整数，class = 矩阵
我的 Treatment 和 Traits 数据格式似乎与示例数据集的格式相匹配，只是我的大部分特征都是因素（而在示例中它们都是数字）。
所以，我想知道错误消息是否是由于 'AbundMat' 不是所需的形式 class=double（而是整数）？
此外，根据 str() 输出，'AbundMat' 中似乎缺少属性（级别）。
是否可以生成双精度类型的矩阵（类似于示例数据矩阵）？
============ 补充说明：==========================
作为附加说明，此代码可用于强制数据与函数 manyglm() 一起使用。它只使用一行数据（一个图）。我想知道如何针对多个站点调整此代码，即我数据集中的 139 个地块？
y2 = c(t(grazedPlants$abundMat[1,])) # coercing the first row into a vector
y2
singleSite2 = data.frame(y2, grazedPlants$traits, check.names = F) # make a data frame from site abundances and traits
singleSite2
ft5 = manyglm(y2 ~ ., data = singleSite2, family = "negative.binomial")
ft5
summary(ft5, nBoot=1) # find which traits are important
plot(ft5)
============================================= =======
非常感谢您的任何建议。
这些是我的数据集：
1) 物种 x 地块丰度（计数）数据（文件名：Abund）
structure(list(plot = structure(c(47L, 47L, 48L, 51L, 53L, 54L), .Label = c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "A22", "A23", "A24", "A25", "A26", "A27", "A28", "A29", "A30", "A31", "A32", "A33", "A34", "A35", "A36", "A37", "A38", "A39", "A40", "A41", "A42", "A43", "A44", "A45", "A46", "A47", "A48", "A49", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "B10", "B11", "B12", "B13", "B14", "B15", "B16", "B17", "B18", "B19", "B20", "B21", "B22", "B23", "B24", "B25", "B26", "B27", "B28", "B29", "B30", "B31", "B32", "B33", "B34", "B35", "B36", "B37", "B38", "B39", "B40", "B41", "B42", "B43", "B44", "B45", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14", "C15","C16", "C17", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "C42", "C43", "C44", "C45"), class = "factor"),
species = structure(c(46L, 47L, 47L, 47L, 47L, 47L), .Label = c("sp1",
"sp10", "sp11", "sp12", "sp13", "sp14", "sp15", "sp16", "sp17",
"sp18", "sp19", "sp2", "sp20", "sp21", "sp22", "sp23", "sp24",
"sp25", "sp26", "sp27", "sp28", "sp29", "sp3", "sp30", "sp31",
"sp32", "sp33", "sp34", "sp35", "sp36", "sp37", "sp38", "sp39",
"sp4", "sp40", "sp41", "sp42", "sp43", "sp44", "sp45", "sp46",
"sp47", "sp48", "sp49", "sp5", "sp50", "sp51", "sp52", "sp53",
"sp54", "sp55", "sp56", "sp57", "sp58", "sp59", "sp6", "sp60",
"sp61", "sp62", "sp7", "sp8", "sp9"), class = "factor"),
occurrence = c(1L, 1L, 1L, 1L, 1L, 1L)), .Names = c("plot", "species", "occurrence"), row.names = c(NA, 6L), class = "data.frame")
2) 物种 x 性状数据（文件名：Traits）
structure(list(species = structure(c(1L, 12L, 23L, 34L, 45L, 56L, 60L, 61L, 62L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 57L, 58L, 59L), .Label = c("sp1", "sp10", "sp11", "sp12", "sp13", "sp14", "sp15", "sp16", "sp17", "sp18", "sp19", "sp2", "sp20", "sp21", "sp22", "sp23", "sp24", "sp25", "sp26", "sp27", "sp28", "sp29", "sp3", "sp30", "sp31", "sp32", "sp33", "sp34", "sp35", "sp36", "sp37", "sp38", "sp39", "sp4", "sp40", "sp41", "sp42", "sp43", "sp44", "sp45", "sp46", "sp47", "sp48", "sp49", "sp5", "sp50", "sp51", "sp52", "sp53", "sp54", "sp55", "sp56", "sp57", "sp58", "sp59", "sp6", "sp60", "sp61", "sp62", "sp7", "sp8", "sp9"), class = "factor"), trait1 = structure(c(3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 3L, 2L, 3L), .Label = c("<NA>", "a", "b"), class = "factor"), trait2 = structure(c(6L, 6L, 4L, 3L, 8L, 4L, 4L, 4L, 3L, 1L, 1L, 6L, 6L, 6L, 6L, 1L, 2L, 2L, 7L, 7L, 6L, 3L, 6L, 6L, 2L, 1L, 2L, 4L, 4L, 3L, 5L, 4L, 4L, 1L, 4L, 2L, 4L, 6L, 3L, 8L, 2L, 4L, 4L, 1L, 1L, 4L, 6L, 1L, 4L, 6L, 6L, 6L, 6L, 5L, 3L, 2L, 4L, 1L, 4L, 1L, 4L, 6L), .Label = c("<NA>", "c", "d", "e", "f", "g", "h", "i"), class = "factor"), trait3 = structure(c(4L, 4L, 4L, 6L, 4L, 1L, 6L, 2L, 2L, 2L, 2L, 1L, 6L, 4L, 4L, 6L, 3L, 6L, 3L, 3L, 6L, 2L, 2L, 2L, 6L, 2L, 6L, 4L, 4L, 4L, 6L, 2L, 6L, 2L, 6L, 2L, 2L, 2L, 6L, 6L, 6L, 2L, 6L, 6L, 2L, 4L, 6L, 2L, 4L, 6L, 6L, 4L, 5L, 4L, 4L, 2L, 2L, 2L, 6L, 2L, 6L, 6L), .Label = c("", "<NA>", "j", "k", "l", "m"), class = "factor"), trait4 = structure(c(4L, 4L, 2L, 2L, 5L, 3L, 2L, 3L, 1L, 5L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 4L, 2L, 2L, 1L, 5L, 1L, 3L, 3L, 1L, 5L, 3L, 1L, 5L, 5L, 2L, 2L, 1L, 1L, 3L, 1L, 3L, 5L, 2L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 5L, 3L, 2L, 2L, 2L, 3L, 5L, 5L, 4L, 3L, 1L, 2L, 1L, 2L, 4L), .Label = c("<NA>", "1", "2", "3", "4"), class = "factor"), trait5 = structure(c(12L, 14L, 9L, 2L, 21L, 9L, 1L, 14L, 9L, 22L, 2L, 12L, 12L, 4L, 12L, 14L, 6L, 14L, 12L, 11L, 12L, 3L, 1L, 1L, 3L, 1L, 2L, 12L, 5L, 12L, 2L, 17L, 2L, 1L, 16L, 19L, 15L, 19L, 12L, 10L, 3L, 12L, 18L, 12L, 19L, 8L, 12L, 20L, 16L, 12L, 7L, 12L, 13L, 9L, 11L, 5L, 2L, 1L, 14L, 11L, 12L, 16L), .Label = c("<NA>", "10", "12", "14", "15", "16", "17", "18", "20", "23", "25", "30", "32", "35", "4", "40", "50", "6", "7", "8", "9", "small"), class = "factor"),
trait6 = structure(c(3L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L,
1L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L,
3L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L,
2L, 2L, 3L, 2L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L), .Label = c("<NA>", "p",
"q"), class = "factor"), trait7 = c(0.61, 0.64, 0.61, 0.62,
0.64, 0.61, 0.39, 0.13, 0.73, 0.75, 0.51, 0.77, 0.75, 0.6,
0.88, 0.55, 0.61, 0.72, 0.64, 0.72, 0.65, 0.7, 0.85, 0.74,
0.55, 0.76, 0.76, 0.43, 0.67, 0.48, 0.73, 0.88, 0.4, 0.46,
0.48, 0.67, 0.67, 0.88, 0.53, 0.48, 0.93, 0.64, 0.62, 0.47,
0.64, 0.64, 0.7, 0.64, 0.9, 0.85, 0.7, 0.86, 0.72, 0.43,
0.33, 0.74, 0.52, 0.69, 0.75, 0.45, 0.88, 0.68)), .Names = c("species", "trait1", "trait2", "trait3", "trait4", "trait5", "trait6", "trait7"), class = "data.frame", row.names = c(NA, -62L))
3）治疗数据（文件名：Treatment）
structure(list(plot = structure(1:139, .Label = c("A01", "A02", "A03", "A04", "A05", "A06", "A07", "A08", "A09", "A10", "A11", "A12", "A13", "A14", "A15", "A16", "A17", "A18", "A19", "A20", "A21", "A22", "A23", "A24", "A25", "A26", "A27", "A28", "A29", "A30", "A31", "A32", "A33", "A34", "A35", "A36", "A37", "A38", "A39", "A40", "A41", "A42", "A43", "A44", "A45", "A46", "A47", "A48", "A49", "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B09", "B10", "B11", "B12", "B13", "B14", "B15", "B16", "B17", "B18", "B19", "B20", "B21", "B22", "B23", "B24", "B25", "B26", "B27", "B28", "B29", "B30", "B31", "B32", "B33", "B34", "B35", "B36", "B37", "B38", "B39", "B40", "B41", "B42", "B43", "B44", "B45", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14", "C15", "C16", "C17", "C18", "C19", "C20", "C21", "C22", "C23", "C24", "C25", "C26", "C27", "C28", "C29", "C30", "C31", "C32", "C33", "C34", "C35", "C36", "C37", "C38", "C39", "C40", "C41", "C42", "C43", "C44", "C45"), class = "factor"), site = 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("A", "B", "C"), class = "factor")), .Names = c("plot", "site"), class = "data.frame", row.names = c(NA, -139L))
此外，这些是 'grazedPlants.RData' 文件的子集：
dput(头(grazedPlants$abundMat))
structure(list(X = structure(1:6, .Label = c("FC01", "FC02",
"FC03", "FC04", "FC05", "FC06", "FC07", "FC08", "NC01", "NC02",
"NC03", "NC04", "NC05", "NC06", "NC07", "NC08", "ND01", "ND02",
"ND03", "ND04", "ND05", "ND06", "ND07", "ND08", "TD01", "TD02",
"TD03", "TD04", "TD05", "TD06", "TD07", "TD08"), class = "factor"),
CAPSBURS = c(0L, 210L, 195L, 1L, 617L, 0L), CREPVESI.HAE = c(0L,
0L, 0L, 0L, 0L, 0L), GALICORR = c(0L, 0L, 0L, 0L, 0L, 0L),
MUSCNEGL = c(0L, 0L, 0L, 0L, 0L, 0L), BROMEREC = c(1L, 0L,
0L, 0L, 0L, 0L), ARENSERP = c(0L, 0L, 1L, 0L, 4L, 0L), VULPMYUR = c(0L,
28L, 3L, 0L, 0L, 8L), CERAPUMI = c(0L, 2L, 0L, 0L, 0L, 0L
), ORNICOLL = c(0L, 1L, 0L, 0L, 0L, 1L), VEROARVE = c(0L,
0L, 0L, 0L, 0L, 0L), GERAROTU = c(0L, 0L, 0L, 80L, 0L, 41L
), ERODCICU = c(0L, 0L, 0L, 1L, 0L, 3L), BROMHORD = c(0L,
735L, 0L, 72L, 0L, 4L), POABULB = c(0L, 209L, 110L, 2L, 0L,
0L), HORDMURI = c(5L, 11L, 0L, 0L, 0L, 0L), TARALAEV = c(0L,
0L, 0L, 0L, 0L, 0L), SANGMINO = c(0L, 0L, 0L, 0L, 1L, 0L),
TRIFSCAB = c(0L, 1L, 25L, 0L, 0L, 0L), CREPSANC.SAN = c(0L,
0L, 0L, 0L, 0L, 0L), ERYNCAMP = c(1L, 0L, 0L, 0L, 0L, 0L),
BROMDIAN = c(0L, 0L, 0L, 0L, 0L, 1L), STELMEDI = c(0L, 0L,
0L, 0L, 0L, 0L), ALYSALYS = c(0L, 0L, 0L, 0L, 0L, 0L), CERAGLOM = c(17L,
5L, 2L, 0L, 21L, 0L), EROPVERN = c(0L, 0L, 0L, 0L, 0L, 0L
), FESTRUBR = c(30L, 0L, 14L, 0L, 69L, 0L), FILAPYRA = c(0L,
0L, 9L, 0L, 0L, 0L), SCLEANNU = c(0L, 0L, 0L, 0L, 0L, 0L),
CAREHALL = c(80L, 21L, 0L, 2L, 0L, 368L), POTENEUM = c(2L,
1L, 0L, 0L, 0L, 0L), VICITETR.GRA = c(0L, 0L, 0L, 1L, 0L,
0L), SHERARVE = c(0L, 0L, 0L, 0L, 0L, 0L), POLYCALC = c(0L,
0L, 0L, 2L, 0L, 0L), VICISATI.SAT = c(0L, 0L, 0L, 0L, 0L,
0L), FILIVULG = c(0L, 0L, 0L, 126L, 16L, 0L), MYOSRAMO.RAM = c(0L,
56L, 0L, 0L, 0L, 27L), CAREHUMI = c(0L, 3L, 9L, 0L, 0L, 7L
), TARAOFFI = c(2L, 0L, 0L, 0L, 0L, 0L), HIPPCOMO = c(1L,
1L, 0L, 3L, 1L, 0L), FESTCHRI = c(0L, 0L, 0L, 0L, 13L, 0L
), KOELVALL = c(0L, 0L, 0L, 0L, 0L, 0L), ONONSTRI = c(0L,
0L, 0L, 0L, 0L, 0L), GENIHISP = c(0L, 0L, 0L, 0L, 0L, 0L),
CARTMITI = c(0L, 0L, 0L, 0L, 0L, 0L), ANTHVULN = c(0L, 0L,
0L, 0L, 0L, 0L), CENTPECT.SUP = c(0L, 0L, 0L, 0L, 0L, 0L),
LOTUCORN = c(0L, 0L, 0L, 0L, 0L, 1L), PLANLANC = c(0L, 0L,
0L, 1L, 0L, 0L), RANUBULB = c(0L, 0L, 0L, 0L, 15L, 0L), CHAMSAGI = c(0L,
0L, 0L, 0L, 0L, 0L), TEUCPOLI = c(0L, 0L, 0L, 0L, 0L, 0L),
THYMSERP = c(0L, 0L, 0L, 0L, 0L, 0L), COROMINI = c(0L, 0L,
0L, 0L, 0L, 0L), SALVPRAT = c(0L, 0L, 0L, 2L, 0L, 0L), ASTRMONS = c(0L,
0L, 0L, 0L, 0L, 0L), HIERPILO = c(1L, 0L, 0L, 0L, 0L, 0L),
LINUTENU.TEN = c(0L, 0L, 0L, 0L, 0L, 0L), APHYMONS = c(0L,
0L, 2L, 0L, 0L, 0L), TEUCCHAM = c(0L, 0L, 1L, 0L, 0L, 0L),
HELICANU = c(0L, 0L, 1L, 0L, 16L, 6L), HELINUMM = c(2L, 0L,
0L, 0L, 0L, 0L), CAREFLAC = c(18L, 0L, 0L, 57L, 0L, 8L),
STIPPENN = c(125L, 26L, 0L, 0L, 0L, 0L), INULMONT = c(0L,
0L, 1L, 0L, 0L, 0L), TEUCMONT = c(0L, 0L, 0L, 0L, 0L, 0L),
GLOBVULG = c(0L, 0L, 0L, 0L, 1L, 0L), HELIAPEN = c(0L, 1L,
0L, 0L, 0L, 0L), RANUGRAM = c(0L, 0L, 0L, 2L, 0L, 0L)), .Names = c("X",
"CAPSBURS", "CREPVESI.HAE", "GALICORR", "MUSCNEGL", "BROMEREC",
"ARENSERP", "VULPMYUR", "CERAPUMI", "ORNICOLL", "VEROARVE", "GERAROTU",
"ERODCICU", "BROMHORD", "POABULB", "HORDMURI", "TARALAEV", "SANGMINO",
"TRIFSCAB", "CREPSANC.SAN", "ERYNCAMP", "BROMDIAN", "STELMEDI",
"ALYSALYS", "CERAGLOM", "EROPVERN", "FESTRUBR", "FILAPYRA", "SCLEANNU",
"CAREHALL", "POTENEUM", "VICITETR.GRA", "SHERARVE", "POLYCALC",
"VICISATI.SAT", "FILIVULG", "MYOSRAMO.RAM", "CAREHUMI", "TARAOFFI",
"HIPPCOMO", "FESTCHRI", "KOELVALL", "ONONSTRI", "GENIHISP", "CARTMITI",
"ANTHVULN", "CENTPECT.SUP", "LOTUCORN", "PLANLANC", "RANUBULB",
"CHAMSAGI", "TEUCPOLI", "THYMSERP", "COROMINI", "SALVPRAT", "ASTRMONS",
"HIERPILO", "LINUTENU.TEN", "APHYMONS", "TEUCCHAM", "HELICANU",
"HELINUMM", "CAREFLAC", "STIPPENN", "INULMONT", "TEUCMONT", "GLOBVULG",
"HELIAPEN", "RANUGRAM"), row.names = c(NA, 6L), class = "data.frame")
dput(头(grazedPlants$traits))
structure(list(X = structure(c(9L, 20L, 28L, 42L, 7L, 4L), .Label = c("ALYSALYS", "ANTHVULN", "APHYMONS", "ARENSERP", "ASTRMONS", "BROMDIAN", "BROMEREC", "BROMHORD", "CAPSBURS", "CAREFLAC", "CAREHALL", "CAREHUMI", "CARTMITI", "CENTPECT.SUP", "CERAGLOM", "CERAPUMI", "CHAMSAGI", "COROMINI", "CREPSANC.SAN", "CREPVESI.HAE", "ERODCICU", "EROPVERN", "ERYNCAMP", "FESTCHRI", "FESTRUBR", "FILAPYRA", "FILIVULG", "GALICORR", "GENIHISP", "GERAROTU", "GLOBVULG", "HELIAPEN", "HELICANU", "HELINUMM", "HIERPILO", "HIPPCOMO", "HORDMURI", "INULMONT", "KOELVALL", "LINUTENU.TEN", "LOTUCORN", "MUSCNEGL", "MYOSRAMO.RAM", "ONONSTRI", "ORNICOLL", "PLANLANC", "POABULB", "POLYCALC", "POTENEUM", "RANUBULB", "RANUGRAM", "SALVPRAT", "SANGMINO", "SCLEANNU", "SHERARVE", "STELMEDI", "STIPPENN", "TARALAEV", "TARAOFFI", "TEUCCHAM", "TEUCMONT", "TEUCPOLI", "THYMSERP", "TRIFSCAB", "VEROARVE", "VICISATI.SAT", "VICITETR.GRA", "VULPMYUR"), class = "factor"), SM = c(0.587, -0.81, 0.446, 0.195, 0.357, -0.128), OFL = c(136.1359, 131.9466, 148.8626, 148.5717, 156.9126, 149.2011), RPH = c(1.4092797, 1.0612845, 0.729711, 0.7335391, 1.7941586, 1.4624131), VPH = c(1.12952674, 0.86161103, 0.54418607, 0.44006594, 1.49088504, 1.01091915), SLA = c(1.0997531, 1.0612576, 1.4866071, 1.0666007, 0.9769541, 1.1077584), LDMC = c(2.491384, 2.446196, 2.227827, 2.476604, 2.717145, 2.347065), LA = c(0.58658981, 0.29217792, 0.53604022, 0.07529993, 0.28781845, 0.68114674),
LNC = c(1.46497, 1.31874, 1.417831, 1.277482, 1.302021, 1.363778
), LCC = c(2.632749, 2.621413, 2.6114, 2.653969, 2.637665,
2.630081), LPC = c(0.40060595, 0.305709643, 0.543668171,
0.225178098, 0.135509475, 0.354213077)), .Names = c("X", "SM", "OFL", "RPH", "VPH", "SLA", "LDMC", "LA", "LNC", "LCC", "LPC"), row.names = c(NA, 6L), class = "data.frame"))
dput(头(grazedPlants$treatment))
structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Names = c("FC01", "FC02",
"FC03", "FC04", "FC05", "FC06"), .Label = c("Fertilised (Limestone)",
"Pasture (Limestone)", "Pasture (Dolomite)", "Ungrazed (Dolomite)"
), class = "factor")
在这里使用您最近发布的对象（丰富、特质和治疗）和将它们与发布的 link 对象（abund、traits 和 treatment）进行比较，您的根本问题是 Traits 不包含数字列。其他问题包括 rownames() 和 $ 赋值。
作者的特征（数字列）
head(traits)
# SM OFL RPH VPH SLA LDMC LA LNC LCC LPC
# CAPSBURS 0.5872967 136.1359 1.4092797 1.1295267 1.0997531 2.491384 0.58658981 1.464970 2.632749 0.4006060
# CREPVESI.HAE -0.8103945 131.9466 1.0612845 0.8616110 1.0612576 2.446196 0.29217792 1.318740 2.621413 0.3057096
# GALICORR 0.4463238 148.8626 0.7297110 0.5441861 1.4866071 2.227827 0.53604022 1.417831 2.611400 0.5436682
# MUSCNEGL 0.1951452 148.5717 0.7335391 0.4400659 1.0666007 2.476604 0.07529993 1.277482 2.653969 0.2251781
# BROMEREC 0.3571080 156.9126 1.7941586 1.4908850 0.9769541 2.717145 0.28781845 1.302021 2.637665 0.1355095
# ARENSERP -0.1283229 149.2011 1.4624131 1.0109192 1.1077584 2.347065 0.68114674 1.363778 2.630081 0.3542131
你的特征（因子列 + 应该是 rownames 的物种）
head(Traits)
# species trait1 trait2 trait3 trait4 trait5 trait6 trait7
# 1 sp1 b g k 3 30 q 0.61
# 2 sp2 b g k 3 35 q 0.64
# 3 sp3 a e k 1 20 q 0.61
# 4 sp4 a d m 1 10 p 0.62
# 5 sp5 a i k 4 9 q 0.64
# 6 sp6 a e 2 20 q 0.61
调整 rownames 并将 factor 类型转换为 numeric，然后您应该可以调用 manyglm().如果此处使用的这种转换没有意义，请务必调整 Traits 以反映数字。
row.names(Traits) <- Traits$species # ASSIGNING ROW NAMES TO SPECIES
Traits <- Traits[-1] # REMOVING FIRST COLUMN
# CONVERT COLUMNS TO NUMERIC
Traits[, c(1:ncol(Traits))] <- sapply(Traits[, c(1:ncol(Traits))], as.numeric)
AbundVec <- as.vector(as.numeric(AbundMat))
N.rows <- NROW(AbundMat)
N.vars <- NCOL(AbundMat)
SppVec <- rep(row.names(Traits), each=N.rows)
TraitVec <- Traits[rep(1:N.vars, each=N.rows),]
SiteVec <- rep(row.names(AbundMat), N.vars)
Treatment = as.factor(Treatment$site) # SELECT SPECIFIC COLUMN
levels(Treatment) <- c("A", "B", "C")
TrV <- rep(Treatment, N.vars)
FtP <- manyglm(AbundVec ~ as.matrix(TraitVec)*TrV, family = "poisson")