Declare the canonical implementation of a test or model

baseline() declares the default implementation of a statistical procedure. It is always the default and is the only implementation reachable on the eager path.

Usage

baseline(fn, print = NULL, claim_parser = NULL)

Arguments

fn

A function whose first argument must be .proc, the processed model output from model_processor(). The keys available on .proc depend on the variable mapper <var_id> used:

• x_by: $x_data, $group_data

• rel: $x_data, $resp_data

• pairwise: $var_names, $pairs, $data

• formula: $data, $vars, $formula

Try run this to explore the structure: names(model_processor(<var_id>, <data>)).

Additional named arguments are user-supplied statistical parameters (e.g. .mu, .ci). See model_processor() for the full .proc schema per model type.

baseline(
    fn = function(.proc, .mu = 0, .ci = 0.95) {
        # ...
        <your-own-class>(...)   # return a class_stat_infer subclass
    }
)

When fn returns a class_stat_infer subclass, auto_tidy() and future auto_*() generics dispatch automatically on the result. Otherwise, register a tidy method via making_tidy().

print

A function with signature function(x, ...) for formatting the result. x is a cld_exec object — read your result from x@data. NULL falls back to print(x@data).

claim_parser

A map_claim() object that maps a null_claim to named arguments injected into fn alongside .proc. NULL (the default) if this implementation does not support state_null().

Value

A baseline S7 object.

See also

variant(), agendas(), stat_define(), model_processor(), map_claim(), class_stat_infer, auto_tidy()