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问题描述
我有一个结构如下的模型，我想在忽略随机效应的同时提取预测值.如?predict.gam 和此处所述，我正在使用 exclude 参数，但出现错误.我的错误在哪里?
I have a model structured as follows, and I would like to extract the predicted values while ignoring the random effect. As specified in ?predict.gam and here, I am using the exclude argument, but I am getting an error. Where is my mistake?
dt <- data.frame(n1 = runif(500, min=0, max=1),
n2 = rep(1:10,50),
n3 = runif(500, min=0, max=2),
n4 = runif(500, min=0, max=2),
c1 = factor(rep(c("X","Y"),250)),
c2 = factor(rep(c("a", "b", "c", "d", "e"), 100)))
mod = gam(n1 ~
s(n2, n3, n4, by=c1) +
s(c2, bs="re"),
data=dt)
newd=data.table(expand.grid(n1=seq(min(dt$n1), max(dt$n1), 0.5),
n2=1:10,
n3=seq(min(dt$n3), max(dt$n3), 0.5),
n4=seq(min(dt$n4), max(dt$n4), 0.5),
c1=c("X", "Y")))
newd$pred <- predict.gam(mod, newd, exclude = "s(c2)")
In predict.gam(mod, newd, exclude = "s(c2)"): not all required variables have been supplied in newdata!
推荐答案
exclude 与您假设的方式不同.您仍然需要在 newd 中为 predict.gam 提供所有变量.请参阅我的此答案，了解 predict.gam 背后的内容.
exclude does not work in the way as you assumed. You still need to provide all variables in your newd for predict.gam. See my this answer for what is behind predict.gam.
这是您需要做的:
## pad newd with an arbitrary value for variable c2
newd$c2 <- "a"
## termwise prediction
pt <- predict.gam(mod, newd, type = "terms", exclude = "s(c2)")
## linear predictor without random effect
lp_no_c2 <- rowSums(pt) + attr(pt, "constant")
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