R：在 lm() 函数中计算预测平均值

Question

我正在尝试计算具有分类变量和连续变量的给定简单线性回归模型的结果的 predicted/adjusted 平均值。一个快速示例类似于以下内容。

dat <- data.frame(value = c(5,8,41,25,23,56,58,54,51,52,56,59),
                  x = c("A","A","A","A","A","A", "B","B","B", "B","B","G"),
                  y=c("C","C","C","D","D","D", "D","D","D", "D","E","E"), 
                  z = c(34,56,25,35,54,67,43,73,52,78,15,38))
m <- lm(value ~ x + y + z, dat)

在给定 x = "A" 的情况下，我如何计算输出的调整后平均值以及该值的置信区间。特别是当模型中有另一个分类变量时。

谢谢！！

Answer 1

我猜这就是你要找的-

dat$predicted <- m$fitted.values
adj_mean <- aggregate(predicted ~ x, data = dat, FUN = mean)

Answer 2

我认为软件包 lsmeans 解决了这个问题。

R：在 lm() 函数中计算预测平均值

R: Calculate predicted mean in a lm() function

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