Extract temporal effects from a fitted model

Extract posterior distributions of temporal effects from a fitted tulpa model with temporal specification.

Usage

temporal(
  object,
  component = "all",
  summary = FALSE,
  probs = c(0.025, 0.5, 0.975),
  ...
)

# S3 method for class 'tulpa_fit'
temporal(
  object,
  component = "all",
  summary = FALSE,
  probs = c(0.025, 0.5, 0.975),
  ...
)

Arguments

object: A tulpa_fit object fitted with temporal argument
component: Which component to extract for multi-scale models: "all" (default), "trend", "seasonal", or "short_term".
summary: Logical; if TRUE, return summary statistics instead of full posterior draws.
probs: Quantiles to compute if summary = TRUE.
...: Ignored

Value

A tulpa_temporal_posterior object

Details

temporal() is overloaded. Given a fitted model it is the accessor described here. Given a one-sided formula (or a named formula = / structure = argument) it is instead the inline varying-coefficient field constructor used in a tulpa() model formula, the temporal mirror of spatial(): temporal(formula = ~ 1 + x || time, structure = "rw1") declares a smooth temporal level (the intercept column) plus a temporally varying slope on each covariate column. structure is one of "rw1" (default), "rw2", or "ar1"; only the double bar || (independent fields) is supported.

Examples

# \donttest{
set.seed(131)
df <- data.frame(year = 1:40, x = rnorm(40))
df$count <- rpois(40, exp(1 + 0.2 * df$x))

fit <- tulpa(
  count ~ x,
  data = df,
  family = "poisson",
  temporal = temporal_multiscale("year", trend = "rw2", seasonal = 12),
  mode = "exact",
  control = list(n_iter = 200L, n_warmup = 100L, seed = 1L)
)

# Extract all temporal effects
temp_post <- temporal(fit)
summary(temp_post)
# }