R语言 用变元法求解带交互作用的广义相对论

r7xajy2e  于 2023-03-27  发布在  其他
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我有一个GAM模型(用mgcv拟合),它在by参数中有两个因子的交互作用

gam(y ~ s(percent, by = source:treatment), data = dat)

如果我试图从那个模型中提取emmeans,它会给我一个错误

Error in `[.data.frame`(tbl, , vars, drop = FALSE) : 
  undefined columns selected

这里有一个完全可复制的例子

library(tidyverse)
library(mgcv)
library(emmeans)

# Simulate Data -----------------------------------------------------------
# similar to ?emmeans::pigs
mycurve <- function(x, h = 4, s = 2){
  30-h + (2*h)/(1+exp((9-x)/s))
}
d_source <- tibble(source = factor(c("fish", "soy", "skim")),
                   hi = 4:6)
d_treatment <- tibble(treatment = factor(c("control", "treatment")),
                      si = c(2, 0.4))
dat <- expand_grid(d_source, d_treatment,
                   percent = seq(9, 18, by = 0.5))
set.seed(1)
dat <- dat %>% 
  rowwise() %>% 
  mutate(mu = mycurve(x = percent, h = hi, s = si),
         y = mu + rnorm(n = 1, mean = 0, sd = 0.1))
dat %>% 
  ggplot(aes(x = percent, y = y, color = source, shape = treatment,
             group = interaction(treatment, source))) +
  geom_point() +
  geom_line()

# Fit model ---------------------------------------------------------------
fit <- gam(y ~ s(percent, by = source:treatment), data = dat)
plot(fit, pages = 1)

# Extract EMM -------------------------------------------------------------
emmeans(fit, specs = "percent", by = c("treatment", "source"),
        at = list(percent = 9:18))

你知道为什么这不起作用吗?或者是emmeans::recover_data.gam()函数中的一个bug?
一个简单的解决方法是定义一个新的因素,但这不便于以后进行具体的对比。

dat <- dat %>% 
  mutate(st = interaction(source, treatment))
fit2 <- gam(y ~ s(percent, by = st), data = dat)
emmeans(fit2, specs = "percent", by = "st",
        at = list(percent = 9:18))
c9qzyr3d

c9qzyr3d1#

我不确定,但如果你使用emmeans::ref_grid(fit, at = list(percent = 9:18)),它会显示从你拟合的模型中获得的参考网格的摘要,包括你可能合法使用的变量的名称。也许gam已经为source:treatment创建了一些变量,你可以将其用作by变量。
顺便说一句,所有的tibles和管道的东西都让你很难看到你在做什么。你发现的问题是关于emmeans()的,如果你保留它的其余部分,我会很感激。

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