Compute confidence bands from theta and covariance

Usage

compute_confidence_bands(
  theta,
  covariance,
  alpha = 0.05,
  method = "supt",
  n_draws = 100000L,
  seed = NULL
)

Arguments

theta: Numeric parameter vector.
covariance: Numeric covariance matrix.
alpha: Significance level. Default 0.05.
method: "supt" or "bonferroni". Default "supt".
n_draws: Number of MVN draws for sup-t. Default 1e5. See confidence_bands() for why this differs from Python's 1e6 estimator default and how to match it.
seed: RNG seed. Default NULL.

Value

A p-by-2 matrix with columns "lower" and "upper". Rows take their names from theta, when it has any.

Examples

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

# Bands from the estimates and covariance alone, without the fitted object
compute_confidence_bands(coef(fit), covariance = vcov(fit),
                         method = "supt", seed = 1)
#>                   lower       upper
#> (Intercept) 32.71991895 41.73462128
#> wt          -5.31895734 -2.43670414
#> hp          -0.04722284 -0.01632305