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Mark an outer-grid setting as a default rather than a pin

Source: R/nested_laplace_auto_grid.R
auto_grid.Rd

Declares that a setting shaping the outer hyperparameter grid carries a default the caller computed, not a choice the user made. The auto-recenter pass (outer_grid_placement) leaves a user-pinned setting exactly as given, and re-centres (or, for a prior, engages its own regularizer over) a marked one when the fit rails against its ceiling.

Three kinds of setting take the mark:

  • a grid axis on a nested-Laplace prior block (sigma_grid, tau_grid, ...) – a numeric vector of nodes, or the [n_cells x k] matrix of pre-paired coordinates the families whose axis is a matrix take (mcar / miid's logchol_grid, tgmrf's theta_grid_built);

  • a scalar grid-construction knob in control, for a driver that builds its axes rather than taking them (fit_st_nested()'s n_grid_spatial, tau_upper, ...);

  • a prior_sigma hyperprior specification – a list, e.g. list("pc.prec", c(U = 3, alpha = 0.01)).

Wrapper packages are the intended caller: one that builds a default of its own – because it derives a second axis from it, hands the same vector to several blocks, or exposes its own argument with a default – would otherwise be indistinguishable from a user who pinned that setting deliberately. Mark it and the rescue stays live. A setting whose value is exactly the engine's own default is recognised without a mark; anything else needs one.

The mark is an attribute, so it is dropped by sort(), [, c() and as.numeric(): build the value first, mark it last.

Usage

auto_grid(x)

Arguments

x

Numeric vector or matrix of grid nodes, a numeric scalar knob, or a prior-specification list.

Value

x carrying the marker attribute. Numeric input is coerced to double IN PLACE, so everything else it carries – dim() and dimnames() above all – survives the mark; a list is returned unchanged apart from the attribute.

See also

is_auto_grid(), tulpa_nested_laplace_joint(), fit_st_nested()

Examples

prior <- list(type = "icar", sigma_grid = auto_grid(c(0.1, 0.5, 1, 2, 3)))
is_auto_grid(prior$sigma_grid)
is_auto_grid(auto_grid(list("pc.prec", c(U = 3, alpha = 0.01))))