P-values for M-Estimator parameters

Computes two-sided Wald-type P-values from Z-scores. The Z-scores are compared to the standard normal distribution by default. When a finite_correction is set on the fit, they are compared instead to the t-distribution with \(n - p\) degrees of freedom.

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

Usage

p_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 P-values.

See also

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

Examples

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

p_values(fit)
#>  (Intercept)           wt           hp 
#> 3.701056e-82 3.967539e-10 1.746674e-06