我尝试使用方法described in this vignette通过metaGAM运行模拟数据，但每次尝试将GAM模型传递到metagam时都会出现奇怪的错误。以下是模拟数据和拟合：

#### Libraries ####
library(tidyverse)
library(mgcv)
library(metagam)
set.seed(1)

#### Sim Data ####
n <- 100
x <- seq(0, 1, length.out = n)
fx1 <- sin(2 * pi * x)
fx2 <- sin(3 * pi * x)
fx3 <- sin(2.4 * pi * x)
y1 <- fx1 + rnorm(n, sd = 0.5)
y2 <- fx2 + rnorm(n, sd = .3)
y3 <- fx3 + rnorm(n, sd = .4)

#### Plot ####
par(mfrow=c(1,3))
plot(x, y1, main = "Simulated Data 1")  
lines(x, fx1, lwd = 2) 
plot(x, y2, main = "Simulated Data 2")
lines(x, fx2, lwd = 2) 
plot(x, y3, main = "Simulated Data 3") 
lines(x, fx3, lwd = 2) 

#### Assign to Dataframe ####
df <- data.frame(x,y1,y2,y3) %>% 
  as_tibble()
df

#### Fit Data ####
fit1 <- gam(y1 ~ s(x), data = df)
fit2 <- gam(y2 ~ s(x), data = df)
fit3 <- gam(y3 ~ s(x), data = df)

#### Combine ####
models <- list(cohort1 = fit1, 
               cohort2 = fit2,
               cohort3 = fit3)

当我尝试使用main命令运行它时：

#### Fit into MetaGAM ####
metafit <- metagam(models, 
                   terms = "s(x)")

它只是给了我这个错误，我假设这意味着它无法找到我的模型中的样条项或数据：

Error in FUN(X[[i]], ...) : Unknown term  requested

我运行models来检查是否不包含s(x)，但输出似乎表明它编码正确：

$cohort1

Family: gaussian 
Link function: identity 

Formula:
y1 ~ s(x)

Estimated degrees of freedom:
4.77  total = 5.77 

GCV score: 0.2317925     

$cohort2

Family: gaussian 
Link function: identity 

Formula:
y2 ~ s(x)

Estimated degrees of freedom:
6.64  total = 7.64 

GCV score: 0.1065703     

$cohort3

Family: gaussian 
Link function: identity 

Formula:
y3 ~ s(x)

Estimated degrees of freedom:
5.37  total = 6.37 

GCV score: 0.1722314

修改样条以显式地包括基础项（例如，s(x, bs = "cr)对于CR样条）似乎没有帮助。我该怎么解决？