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我先上传表格。该表包含 9 行，其中 6 行是因子，左侧 3 行是 152 个人 (n01,n02,n03) 增长率的离散度量。然后我指定因素：

`r$feed <- factor (r$feed)`
`r$ph <- factor (r$ph)`
`r$aq <- factor (r$aq)`
`r$ind <- factor (r$ind)`
`r$wc <- factor (r$wc)`
`r$p0<- factor (r$p0)`

接下来，我将数据框融合到一个新表“r2”中，其中包含我感兴趣的因素，并使用 na.omit 函数删除 NA 值。

`r2 <- data.table::melt(r,id.vars=c("feed","ph","aq","wc"),
                       measure=c("n01","n12","n23"),
                       variable.name="time",value.name="G")`

`r2<-na.omit(r2)`

r2 看起来像这样：

data.frame(
           G = c(0.184, 0.087, 1.747, 0.11, 0.39, 0.062, 0.08, 0.189, 0.068,
                 0.262, 0.048, 0.029, 0, 0.229, 0.175),
        feed = as.factor(c("HF", "HF", "HF", "HF", "HF", "HF", "HF", "HF",
                           "HF", "HF", "HF", "HF", "HF", "HF", "HF")),
          ph = as.factor(c("8.1", "8.1", "8.1", "8.1", "8.1", "8.1", "8.1",
                           "8.1", "8.1", "8.1", "8.1", "8.1", "8.1", "8.1",
                           "8.1")),
          aq = as.factor(c("1", "1", "1", "1", "1", "1", "2", "2", "2", "2",
                           "2", "2", "2", "3", "3")),
          wc = as.factor(c("3", "3", "2", "3", "2", "4", "3", "4", "2", "2",
                           "3", "3", "1", "4", "3")),
        time = as.factor(c("n01", "n01", "n01", "n01", "n01", "n01", "n01",
                           "n01", "n01", "n01", "n01", "n01", "n01", "n01",
                           "n01"))
)

之后，我设置了固定方差并应用并执行了 2 个 gls 模型，如下所示：

`vfix3 <- varIdent(form=~1|time*factor(aq))
    mix1 <- gls(G ~ ph+feed, weights=vfix3,data=r2)
    mix3 <- gls(G ~ ph+feed+wc+time, weights=vfix3,data=r2)`

这些模型似乎工作正常，因为我可以获得它们的摘要和方差分析。然后，我尝试使用包 emmeans 中的 lsmeans 函数运行事后成对比较，如下所示：

    print(lsmeans(mix1, list(pairwise~ph|feed), adjust="tukey"))

lsmeans 似乎适用于 2 因子模型 mix1。但是，在模型 mix3 上执行 lsmeans 时会弹出此错误：

crossprod(x, y) 中的错误：需要数字/复数矩阵/向量参数

我试图将模型转换为矩阵，但对于lsmeans函数它不是正确的对象。我也尝试过不设置因子并将列保留为数字，但会弹出相同的错误。在阅读 lsmeans 函数时，我找不到任何与之相关的 crossprod 函数。
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