if (FALSE) {
# simulate some data
dat <- mgcv::gamSim(1, n = 200, scale = 2)
# fit univariate smooths for all predictors
fit1 <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3),
data = dat, chains = 2)
summary(fit1)
plot(conditional_smooths(fit1), ask = FALSE)
# fit a more complicated smooth model
fit2 <- brm(y ~ t2(x0, x1) + s(x2, by = x3),
data = dat, chains = 2)
summary(fit2)
plot(conditional_smooths(fit2), ask = FALSE)
}
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