mgcv: 如何在 predict.gam 中使用 'exclude' 参数?
mgcv: How to use 'exclude' argument in predict.gam?
我有一个结构如下的模型,我想在忽略随机效应的情况下提取预测值。正如 ?predict.gam
和 here 中指定的那样,我正在使用 exclude
参数,但出现错误。我的错误在哪里?
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$pred <- predict.gam(mod, newd, exclude = "s(c2)")
In predict.gam(mod, newd, exclude = "s(c2)"): not all required variables have been supplied in newdata!
exclude
并不像您假设的那样工作。您仍然需要在 newd
中为 predict.gam
提供所有变量。请参阅我的 了解 predict.gam
背后的内容。
这是您需要做的:
## pad newd with an arbitrary value for variable c2
newd$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")
我有一个结构如下的模型,我想在忽略随机效应的情况下提取预测值。正如 ?predict.gam
和 here 中指定的那样,我正在使用 exclude
参数,但出现错误。我的错误在哪里?
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$pred <- predict.gam(mod, newd, exclude = "s(c2)")
In predict.gam(mod, newd, exclude = "s(c2)"): not all required variables have been supplied in newdata!
exclude
并不像您假设的那样工作。您仍然需要在 newd
中为 predict.gam
提供所有变量。请参阅我的 predict.gam
背后的内容。
这是您需要做的:
## pad newd with an arbitrary value for variable c2
newd$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")