使用 betapart 和 ddecay 包的距离衰减错误

Question

我的目标是为物种数据与地理距离创建距离衰减曲线。但是，我运行陷入了错误。对于 betapart 包，这可能是由于缺少相对于行数的列。有办法解决这个问题吗？如果没有，是否有另一种方法来创建距离衰减曲线（并绘制它）？我也尝试了 ddecay 包，但运行也出现了错误。任何帮助深表感谢。数据结构如下。

# BETAPART -------------------------------------------------
library(betapart)
spat.dist<-dist(coords)
dissim.BCI<-beta.pair.abund(spec)$beta.bray.bal

plot(spat.dist, dissim.BCI, ylim=c(0,1), xlim=c(0, max(spat.dist)))

BCI.decay.exp<-decay.model(dissim.BCI, spat.dist, y.type="dissim", model.type="exp", perm=100)
#========================================================================================================

I also tried a few other packages --------------------------

# ddecay package -------------------------------------------
devtools::install_github("chihlinwei/ddecay")
the issue with this method is that it requires the use of a gradient however, I would like to avoid that if possible but I do not see a way around this. Also they do not include their example data in the package.

dd <- beta.decay(gradient=spat.dist, counts=decostand(spec, method="pa"),
             coords=coords, nboots=1000,
             dis.fun = "beta.pair", index.family = "sorensen", dis = 1, like.pairs=T)
x <- vegdist(coords, method = "euclidean")
y <- 1 - dist(decostand(spec, method="pa"), index.family = "sorensen")[[1]]
plot(x, y)
lines(dd$Predictions[, "x"], dd$Predictions[,"mean"], col="red", lwd=2)
#========================================================================================================

# DATA -----------------------------------------------------

    spec <- structure(list(Ccol = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Acol = c(0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0), NYcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), Mcol = c(0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0), AAcol = c(14, 0, 14, 3, 11, 1, 0, 2, 0, 
    3, 0, 4, 0, 1, 8, 2, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 7), 
        Ncol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1), ATBcol = c(0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 20, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 3), CVcol = c(0, 0, 0, 0, 0, 0, 1, 20, 
        0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 2, 0, 0, 
        0, 6), AZNcol = c(0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), GBcol = c(0, 
        0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0), KHAcol = c(0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
        0, 0, 0, 0), AFcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0), AFPcol = c(0, 
        0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 1), TIAcol = c(4, 1, 0, 2, 6, 0, 
        1, 1, 0, 2, 0, 0, 0, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 1, 0), AUcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AScol = c(0, 
        4, 0, 2, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 5, 0, 0), NSAcol = c(0, 0, 0, 0, 0, 0, 
        0, 0, 0, 7, 0, 0, 3, 0, 0, 0, 4, 0, 2, 0, 1, 0, 9, 5, 1, 
        0, 0, 2, 0), WZcol = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 10, 4, 
        0, 0, 0, 0, 0, 0, 1, 5, 0, 0, 0, 17, 4, 0, 0, 0, 0, 0), AJcol = c(0, 
        3, 6, 0, 0, 1, 0, 4, 0, 0, 0, 0, 39, 12, 0, 0, 0, 0, 0, 0, 
        0, 4, 5, 1, 12, 13, 16, 0, 5), EADcol = c(4, 1, 2, 1, 2, 
        0, 0, 0, 0, 4, 0, 2, 1, 1, 0, 0, 0, 0, 0, 10, 0, 0, 0, 0, 
        0, 0, 0, 0, 1), CAcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), Pcol = c(0, 
        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 60, 0, 0, 
        13, 0, 8, 1, 0, 0, 0, 0, 0), ASDcol = c(3, 5, 6, 17, 3, 5, 
        26, 2, 0, 17, 3, 10, 6, 3, 2, 4, 0, 0, 5, 25, 0, 0, 0, 2, 
        2, 9, 0, 2, 8), RMAcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
        OUcol = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), KAcol = c(0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 12, 
        0, 0, 0, 0, 0, 8, 1), PACcol = c(0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 2, 0, 37, 0, 24, 
        1, 0, 0), LAAcol = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0), GAcol = c(1, 
        0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 
        0, 0, 3, 0, 0, 0, 2, 0, 0), AAcol = c(1, 0, 1, 0, 0, 0, 0, 
        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 1, 0), EVAcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0), EAcol = c(0, 
        0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0), AKcol = c(0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
        0, 0, 0), Acol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 1, 0), QAcol = c(0, 
        0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0), YAcol = c(11, 24, 21, 63, 44, 
        95, 12, 43, 0, 5, 26, 22, 25, 48, 86, 2, 0, 0, 13, 0, 0, 
        2, 0, 0, 60, 6, 7, 0, 45), BANcol = c(0, 0, 0, 3, 0, 0, 0, 
        0, 0, 0, 0, 0, 24, 0, 6, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 
        9, 17, 17), VCcol = c(0, 38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Vcol = c(0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 1, 0, 0, 0, 0, 0, 0), Ocol = c(0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0), AVcol = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), JXcol = c(0, 
        3, 3, 0, 0, 0, 0, 0, 8, 0, 0, 10, 3, 0, 0, 5, 0, 0, 0, 1, 
        0, 0, 0, 2, 4, 1, 0, 0, 0)), class = "data.frame", row.names = c(NA, 
    -29L))

   coords <- structure(list(Lat.x = c(34.43363, 34.36784, 34.32587, 34.19891, 
    34.24217, 34.24863, 34.18137, 34.16838, 34.10961, 34.08329, 34.40571, 
    34.39591, 34.39292, 34.37466, 34.28948, 34.26146, 34.04687, 34.0409, 
    34.068339, 34.34679, 34.17161, 34.23308, 34.21544, 34.14922, 
    34.27539, 34.2323, 34.19057, 34.07042, 34.06289), Lon.x = c(-94.94494, 
    -94.92512, -94.94429, -94.84497, -94.8573, -94.85641, -94.887, 
    -94.91322, -94.92913, -94.93276, -95.02622, -95.04382, -94.96295, 
    -94.83733, -94.81071, -94.79161, -95.03968, -95.0608, -95.086986, 
    -95.03345, -95.23862, -95.25619, -95.1041, -95.02286, -95.02672, 
    -95.02626, -95.02941, -95.01746, -94.98786)), class = "data.frame", row.names = c(NA, 
    -29L))

Answer 1

如果您说出问题所在，您可以获得更多答案。例如，哪些功能失败以及错误消息是什么。我查看了 betapart::decay.model()，在那里我得到了这个错误信息：

Error in eval(family$initialize) : 
  cannot find valid starting values: please specify some

长话短说：您不能将此函数用于您的数据，因为您的数据中的相异度为 1，相异度变为相似度为 1-相异度，这使得这些值的相似度为零（即，这些成对的采样单元没有任何共同点，它们没有共同的物种）。函数 decay.model 使用 glm with gaussian family with log-link，并且 log-link 要求你给出起始值，如果你在y 变量。

我认为你有四种选择：

您不使用该方法，因为它不适合您的数据。
您修改 decay.model 函数，以便您可以指定起始值，如建议的错误消息。这意味着您将 mustart 添加到函数调用中，以便它读取，例如 glm(y ~ x, family=gaussian(link="log"), mustart=pmax(y, 0.01))。这将用 0.01 替换零作为起始值。
您将最大距离从 1 更改为更小的值，例如 0.99：dissim.BCI[dissim.BCI==1] <- 0.99。但是，这会更改数据，并且还会更改您使用备选方案 2 获得的结果（仅更改起始值，但数据未修改）。但是，效果不是很大，任何贝叶斯主义者都会声称相异度 1 只是频率论者的愚蠢行为（您只是没有看到与这些采样单元共有的情况）。
您将最大距离更改为缺失值。这将比替代方案 3 更能改变数据。它消除了最大的差异，这些差异不再影响衰减曲线。效果与审查最大差异相同。结果比备选方案 3 变化更多。

使用 betapart 和 ddecay 包的距离衰减错误

Errors with distance-decay using betapart and ddecay packages

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