S-values (surprisal) for M-Estimator parameters

Computes Shannon Information values (S-values) as \(S = -\log_2(P)\), where \(P\) is the corresponding P-value.

This function mirrors m.s_values() in Python delicatessen, so code translated from Python can keep its shape.

Usage

s_values(object, null = 0, ...)

Arguments

object

A fitted MEstimator object (after calling estimate()).

null

Numeric null hypothesis value(s). Default 0.

...

Not used. Must be empty, so a name that is not one of the documented arguments is an error rather than silently ignored.

Value

A numeric vector of S-values.

Details

A P-value small enough to underflow to exactly zero has an S-value of Inf. That is the limit the surprisal is heading toward rather than a defect. The smallest P a double can hold is \(2^{-1074}\), so a P that arrives as zero stands for more than a thousand bits of surprisal, past the range a double can name. The infinity reports evidence beyond measurement, where any finite substitute would name a number the fit does not support.

See also

summary() and tidy(), which report the same S-values in table form alongside the other parameter-level results.

Examples

fit <- m_estimate(mpg ~ wt + hp, data = mtcars, .ee = ee_regression,
                  model = "linear")

s_values(fit)
#> (Intercept)          wt          hp 
#>   270.51017    31.23104    19.12696