s: Defining smooths in 'brms' formulas

In brms: Bayesian Regression Models using 'Stan'

Defining smooths in brms formulas

Description

Functions used in definition of smooth terms within a model formulas. The function does not evaluate a (spline) smooth - it exists purely to help set up a model using spline based smooths.

Usage

s(...)

t2(...)

Arguments

...	Arguments passed to mgcv::s or mgcv::t2.

Details

The function defined here are just simple wrappers of the respective functions of the mgcv package. When using them, please cite the appropriate references obtained via citation("mgcv").

brms uses the "random effects" parameterization of smoothing splines as explained in mgcv::gamm. A nice tutorial on this topic can be found in Pedersen et al. (2019). The answers provided in this Stan discourse post may also be helpful.

References

Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: an introduction with mgcv. PeerJ.

See Also

brmsformula, mgcv::s, mgcv::t2

Examples

## Not run: 
# 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)

## End(Not run)

brms documentation built on Sept. 23, 2024, 5:08 p.m.