Computes hypothesis tests, contrasts, and small-sample corrections for
an estimate object. The arguments null, contrast, type, and
var.adj were previously available on estimate.default() and have
been moved here.
Usage
# S3 method for class 'estimate'
summary(
object,
contrast,
...,
null = 0,
level = 0.95,
type,
var.adj = 0.25,
df,
transform = NULL,
print = NULL
)Arguments
- object
an
estimateobject.- contrast
(optional) contrast matrix for the final Wald test. When supplied together with
null, tests \(H_0: B\theta = b_0\).- ...
additional arguments passed to contr.
- null
(optional) null hypothesis to test (default 0).
- level
level of confidence limits (default 0.95)
- type
type of small-sample correction. Requires the estimate to have been computed with
IC=TRUE(the default).- var.adj
variance adjustment parameter for small-sample correction. Requires the estimate to have been computed with
IC=TRUE(the default).- df
degrees of freedom for t-based inference (default:
NULLfor Gaussian approximation; when set, confidence intervals and p-values use the t-distribution withdfdegrees of freedom)- transform
(optional) function applied to the point estimates and confidence interval bounds after inference is performed on the original scale. Useful for variance-stabilizing transformations, e.g., compute CIs on the
atanh(Fisher z) scale and back-transform withtanh.(optional) custom print function for the resulting
summary.estimateobject
Details
types of small-sample corrections:
"robust"(default): no correction."df": applies \(n/(n-p)\) correction (Mancl & DeRouen, 2001)."mbn": Morel-Bokossa-Neerchal (2003) correction."hc3": leverage-adjusted HC3-type correction (blended withvar.adj)."hc4": Cribari-Neto (2004) leverage-adjusted correction.
The var.adj parameter controls the blending parameter for the HC3 leverage adjustment, by controls the weight between observation-level empirical leverage and the average leverage \(p/n\).