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Extract fitted covariate smooths

Source: R/smoother.R
smooth_effects.Rd

The grid-marginalized posterior mean of each s(x) term's latent values, one estimate per node (bin midpoint or unique covariate value). The latent block values are read from the fit's per-grid modes, weighted by the hyperparameter grid weights.

Usage

smooth_effects(object, term = 1L)

Arguments

object

A tulpa_fit from tulpa() with s(...) term(s) in the formula.

term

Which smoother: index or covariate name. Default 1.

Value

A data frame with columns x (node location) and estimate (the posterior-mean smooth at that node), with the covariate name as an attribute "var".

See also

field_effects() for a spatial field, ranef() for random effects.

Examples

# \donttest{
set.seed(1)
d <- data.frame(x = runif(300, -2, 2))
d$y <- rpois(300, exp(0.3 + sin(2 * d$x)))
fit <- tulpa(y ~ s(x), data = d, family = "poisson")
sm <- smooth_effects(fit)
plot(sm$x, sm$estimate, type = "l")
# }