I have a data frame with the following structure:
> t <- read.csv("combinedData.csv")[,1:7]
> str(t)
'data.frame': 699 obs. of 7 variables:
$ Awns : int 0 0 0 0 0 0 0 0 1 0 ...
$ Funnel : Factor w/ 213 levels "MEL001","MEL002",..: 1 1 2 2 2 3 4 4 4 4 ...
$ Plant : int 1 2 1 3 8 1 1 2 3 5 ...
$ Line : Factor w/ 8 levels "a","b","c","cA",..: 2 2 1 1 1 3 1 1 1 1 ...
$ X : int 1 2 3 4 7 8 9 10 11 12 ...
$ ID : Factor w/ 699 levels "MEL_001-1b","MEL_001-2b",..: 1 2 3 4 5 6 7 8 9 10 ...
$ BobWhite_c10082_241: int 2 2 2 2 2 2 0 2 2 0 ...
I want to construct a mixed effect model. I know in my data frame that the random effect I want to include (Funnel) is a factor, but it does not work:
> lmer(t$Awns ~ (1|t$Funnel) + t$BobWhite_c10082_241)
Error: couldn't evaluate grouping factor t$Funnel within model frame: try adding grouping factor to data frame explicitly if possible
In fact this happens whatever I want to include as a random effect e.g. Plant:
> lmer(t$Awns ~ (1|t$Plant) + t$BobWhite_c10082_241)
Error: couldn't evaluate grouping factor t$Plant within model frame: try adding grouping factor to data frame explicitly if possible
Why is R giving me this error? The only other answer I could google fu is that the random effect fed in wasn't a factor in the DF. But as str shows, df$Funnel certainly is.
答案 0 :(得分：3)
为建模功能提供方便的语法实际上并不容易，同时具有强大的实现。大多数软件包作者都假设您使用data参数，甚至可能会出现范围问题。因此，如果您使用DF$col语法指定变量，则可能会发生奇怪的事情，因为包作者很少花费大量精力使其正常工作，并且不包含大量的单元测试。
因此，如果模型函数提供data方法，强烈建议使用formula参数。如果您不遵循该实践（也可以使用其他模型函数，如lm），则会发生奇怪的事情。
在你的例子中：
lmer(Awns ~ (1|Funnel) + BobWhite_c10082_241, data = t)
这不仅有效，而且写作也更方便。