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Confidence intervals for M-Estimator parameters

Source: R/inference.R
confidence_intervals.Rd

Computes two-sided Wald-type \((1 - \alpha) \times 100\%\) confidence intervals using the point estimates and sandwich variance: \(\hat{\theta} \pm c_{\alpha/2} \times \widehat{SE}(\hat{\theta})\). The critical value \(c_{\alpha/2}\) comes from the standard normal distribution by default. When a finite_correction is set on the fit, it comes instead from the t-distribution with \(n - p\) degrees of freedom, matching the finite-sample adjustment of the variance.

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

Usage

confidence_intervals(object, alpha = 0.05, ...)

Arguments

object

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

alpha

Numeric significance level, between 0 and 1. Default 0.05 for 95% confidence intervals.

...

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 p-by-2 matrix with columns "lower" and "upper".

See also

confint(), the standard R accessor for the same intervals. It returns identical values but is parameterized by level = 0.95 where this function takes alpha = 0.05.

Examples

psi <- function(theta) {
  y <- c(1, 2, 3, 4, 5)
  matrix(y - theta[1], nrow = 1)
}
m <- m_estimate(stacked_equations = psi, init = 0)
confidence_intervals(m)
#>           lower   upper
#> theta_1 1.76041 4.23959