Skip to contents

Build a diagnosands step from a set of named diagnosands

Source: R/set_diagnosands.R
select_diagnosands.Rd

Two ways to call this, told apart by whether the first argument is a design_step.

Usage

select_diagnosands(..., alpha = 0.05, subset = NULL, na.rm = FALSE)

Arguments

...

(required) Diagnosand names to keep, as strings, optionally preceded by a diagnosands design_step to subset.

alpha

(optional) Significance level used by power, type_s_rate, exaggeration_ratio, and prop_pos_sig. Defaults to 0.05.

subset

(optional) An expression evaluated on the simulations table; only the rows for which it is TRUE enter the diagnosands. NULL (the default) keeps every simulation.

na.rm

(optional) Passed to the library diagnosands. Defaults to FALSE.

Value

A diagnosands design_step, as declare_diagnosands() returns, holding the named diagnosands in the order given.

Details

Given names only, select_diagnosands("bias", "power") builds a fresh diagnosands step out of the built-in library, which is what DeclareDesign does. alpha, subset, and na.rm shape the expressions it writes.

Given a diagnosands step first, select_diagnosands(my_diagnosands, "bias") keeps those entries of that step and nothing else. The expressions are yours and are copied unchanged, so alpha, subset, and na.rm have nothing to act on and are refused rather than ignored: set them on the declare_diagnosands() call that made the set.

A consequence worth knowing: the same name can mean different things in the two forms. select_diagnosands("bias") writes mean(estimate - estimand, na.rm = FALSE), following DeclareDesign's default, while select_diagnosands(default_diagnosands(), "bias") returns the na.rm = TRUE version default_diagnosands() declares.

The library is a superset of default_diagnosands(): mean_estimand, mean_estimate, bias, sd_estimate, rmse, power, coverage, mean_se, type_s_rate, exaggeration_ratio, var_estimate, mean_var_hat, prop_pos_sig, and mean_ci_length.

See also

Other diagnosands: declare_diagnosands(), default_diagnosands(), format.diagnosis(), set_diagnosands()

Examples

select_diagnosands("sd_estimate", "mean_se")
#> <design_step: diagnosands [diagnosand]>

# subset a custom set instead of the library
select_diagnosands(default_diagnosands(), "bias", "rmse")
#> <design_step: diagnosands [diagnosand]>