Predict distributional parameters in long format — predict

predict_parameters() returns predicted distributional parameters from a drmTMB fit in one long data frame. It is a compact data surface for interpretation tables and future plotting or marginalisation helpers: the same grid can hold location/mean, scale, shape, probability, and coscale quantities.

Usage

predict_parameters(object, ...)

# S3 method for class 'drmTMB'
predict_parameters(
  object,
  newdata = NULL,
  dpar = NULL,
  type = c("response", "link"),
  include_newdata = TRUE,
  conf.int = FALSE,
  conf.level = 0.95,
  ...
)

Arguments

object: A drmTMB fit.
...: Reserved for future options.
newdata: Optional data frame for prediction. If omitted, fitted rows are used.
dpar: Optional character vector of distributional parameters to predict, such as "mu", "sigma", "nu", "rho12", "sigma1", or "sigma2", plus fitted random-effect scale model names such as "sd(id)". NULL predicts all fitted distributional parameters.
type: Prediction scale: "response" or "link".
include_newdata: Logical; when TRUE and newdata is supplied, append the supplied covariate columns to the returned table.
conf.int: Logical; include Wald fixed-effect confidence intervals when available for the supplied prediction grid.
conf.level: Confidence level for Wald intervals when conf.int = TRUE.

Value

A data frame with columns row, row_label, dpar, component, type, estimate, conf.status, and interval_source. When conf.int = TRUE, std.error, conf.low, conf.high, and conf.level are also included. When include_newdata = TRUE, supplied newdata columns are appended after those core columns.

Details

The helper calls predict.drmTMB() for each requested distributional parameter. With newdata = NULL, predictions use the fitted rows. With newdata supplied, predictions are fixed-effect, population-level predictions for those rows, matching predict.drmTMB().

By default, the table includes interval provenance columns with conf.status = "not_requested" and interval_source = "not_available". When conf.int = TRUE and newdata is supplied for ordinary fixed-effect distributional parameters, the helper adds Wald fixed-effect intervals from the fitted coefficient covariance and records the requested confidence level. These are population-level intervals for the supplied grid; they do not include random-effect mode uncertainty, profile-likelihood uncertainty, or uncertainty for direct random-effect scale models.

Examples

dat <- data.frame(
  y = c(0.2, 0.5, 1.1, 1.4, 1.8, 2.2),
  x = c(-1, -0.5, 0, 0.5, 1, 1.5)
)
fit <- drmTMB(bf(y ~ x, sigma ~ x), data = dat)
grid <- data.frame(x = c(0, 1))
predict_parameters(
  fit,
  newdata = grid,
  dpar = c("mu", "sigma"),
  conf.int = TRUE
)
#>   row row_label  dpar            component     type     estimate std.error
#> 1   1         1    mu             location response 9.999961e-01        NA
#> 2   2         2    mu             location response 1.799999e+00        NA
#> 3   1         1 sigma distributional-scale response 9.135855e-02        NA
#> 4   2         2 sigma distributional-scale response 3.318231e-06        NA
#>   conf.low conf.high conf.level      conf.status interval_source x
#> 1       NA        NA       0.95 wald_unavailable   not_available 0
#> 2       NA        NA       0.95 wald_unavailable   not_available 1
#> 3       NA        NA       0.95 wald_unavailable   not_available 0
#> 4       NA        NA       0.95 wald_unavailable   not_available 1