Cannot fit data into metaGAM

I'm trying to run simulated data through a metaGAM using the method described in this vignette, but I keep getting a weird error every time I try to pass the GAM models into metagam. Below is the simulated data and fits:

#### 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)

When I try running it with the main command:

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

It just gives me this error, which I assume means it can't find either the spline term or the data in my models:

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

I ran models to check if s(x) was somehow not included, but the output seems to indicate it is coded correctly:

$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 

Modifying the splines to explicitly include basis terms (e.g. s(x, bs = "cr) for CR splines) does not seem to help. How do I fix this?

I figured it out. For some reason this package requires that you always use the strip_rawdata function before running GAMs through metagam. It also appears you cannot use the default splines when fitting each GAM, so I changed the basis to bs="cr". This immediately fixed the problem by using this code:

#### Fit Data ####
fit1 <- gam(y1 ~ s(x, bs = "cr"), data = df)
fit2 <- gam(y2 ~ s(x, bs = "cr"), data = df)
fit3 <- gam(y3 ~ s(x, bs = "cr"), data = df) # changes each basis

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

#### Fit and Plot ####
fit <- metagam(models)
plot(fit) # plots fit

Which gives me what I want:

The use of smoothers for the metagams default prediction type "iterms" is not supported:

> Note that type="terms" is not supported, since it may result in estimated zero
> standard deviation for smooth terms.

(from ?metagam)

So you'd need to specify another prediction type ("response" or "link"), e. g.:

metafit <- metagam(models, 
                   terms = "s(x)",
                   type = "response"
                   ) 

However, that might not be what you're after?