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如何计算广义线性混合效应模型的中值绝对偏差（MAD）

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我知道我的问题与统计数据有关，但我正在寻找 R 的解决方案，所以我相信它适合SO .

我使用 R 中的 lme4 函数使用 glmer 函数构建了一个广义线性混合效应模型（GLMM），以使用Zuur等人基于显着的解释变量模拟水产养殖场周围的物种丰富度 . （2009）Mixed Effects Models and Extensions in Ecology with R . 该模型是：

Mod1 <- glmer(Richness ~ Distance + Depth + Substrate + Beggiatoa + 
        Distance*Beggiatoa + (1|Site/transect), family = poisson, data = mydata)

现在我有一个在不同站点收集的完整数据集，我想评估此模型在新数据集上的执行情况 .

在CV上的question后，有人建议在新数据集上查找中位数绝对偏差（mad） . 我在 R 中的 stats 包中尝试了 mad 函数，但是我收到以下错误消息：

Error in x[!is.na(x)] : object of type 'S4' is not subsettable
In addition: Warning messages:
1: In is.na(x) : is.na() applied to non-(list or vector) of type 'S4'
2: In is.na(x) : is.na() applied to non-(list or vector) of type 'S4'

Does anybody knows what's going wrong here? Is it that mad in stats can't be calculated for GLMMs? If so, is there another R package to calculate mad from GLMMs?

Edit:

为了让您了解我的数据，这里是 dput(head(mydata)) 的输出，同时请注意新数据集中没有"Substrate"类别，"S"指的是"Richness"：

structure(list(S = c(0, 1, 2, 3, 3, 2), Site = structure(c(1L, 
1L, 1L, 1L, 1L, 1L), .Label = c("BC", "BH", "GC", "IS", "Ref"
), class = "factor"), Transect = structure(c(4L, 4L, 4L, 4L, 
4L, 4L), .Label = c("10GC", "10IS", "10N", "10S", "11IS", "12IS", 
"13E", "1GC", "1N", "1W", "2E", "2GC", "2IS", "2N", "2W", "2WA", 
"3E", "3GC", "3IS", "3N", "3S", "4E", "4GC", "4IS", "4S", "4W", 
"5GC", "5IS", "5S", "6GC", "6IS", "6N", "6S", "6W", "7E", "7GC", 
"7IS", "8GC", "8IS", "8W", "9E", "9GC", "9IS", "9N", "RefBC1", 
"RefBC10", "RefBC11", "RefBC12", "RefBC2", "RefBC3", "RefBC4", 
"RefBC5", "RefBC6", "RefBC7", "RefBC8", "RefBC9", "X1", "X2"), class = "factor"), 
Distance = c(2, 20, 40, 80, 120, 160), Depth = c(40L, 40L, 
50L, 40L, 40L, 40L), Beggiatoa = c(2, 1, 1, 0, 0, 0)), .Names = c("S", 
"Site", "Transect", "Distance", "Depth", "Beggiatoa"), row.names = c(NA, 
6L), class = "data.frame")
r lme4 mixed-models

1 回答

  • 2

    对于样本内误差，中位绝对偏差计算就是这样

    mad(residuals(fitted_model))

    ...你可能想要 residuals(fitted_model,type="response") ，因为 residuals 默认会给你偏差残差（见 ?residuals.merMod ）

    如果您想查看样本外错误，可以执行以下操作：

    pred <- predict(fitted_model,
                newdata = newdf,
                type = "response",
                re.form=~0)
mad(pred, center=newdf$S)

    （ re.form=~0 指定您要省略预测中的随机效果，除非您还获得了训练数据，否则这是您唯一的选择）

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