se.avg: Calculate approximate standard errors for a fitted HFR model

In hfr: Estimate Hierarchical Feature Regression Models

Calculate approximate standard errors for a fitted HFR model

Description

This function computes the weighted average standard errors across levels using Burnham & Anderson (2004).

Usage

se.avg(object)

Arguments

object

Fitted hfr model.

Details

The HFR computes linear regressions over several levels of an estimated hierarchy. By averaging the standard errors across hierarchical levels, an indication can be obtained about the average significance of the variables.

Standard errors are understated, since the uncertainty in the hierarchy estimation is not reflected.

Value

A vector of standard errors.

Author(s)

Johann Pfitzinger

References

Pfitzinger, J. (2022). Cluster Regularization via a Hierarchical Feature Regression. arXiv 2107.04831[statML]

Burnham, K. P. and Anderson, D. R. (2004). Multimodel inference - understanding AIC and BIC in model selection. Sociological Methods & Research 33(2): 261-304.

See Also

hfr method

Examples

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit = hfr(x, y, kappa = 0.5)
se.avg(fit)

hfr documentation built on Jan. 22, 2023, 1:46 a.m.