Calculate approximate standard errors for a fitted HFR model

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

Usage

se.avg(object)

Arguments

object: Fitted hfr model.

Value

A vector of standard errors.

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.

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.

Author

Johann Pfitzinger

Examples

x = matrix(rnorm(100 * 20), 100, 20)
y = rnorm(100)
fit = hfr(x, y, kappa = 0.5)
se.avg(fit)
#>         V1         V2         V3         V4         V5         V6         V7 
#> 0.07909191 0.06357181 0.08818077 0.08520213 0.08108539 0.06336660 0.07035675 
#>         V8         V9        V10        V11        V12        V13        V14 
#> 0.06557754 0.06292404 0.08090008 0.05005583 0.08259609 0.06261372 0.07183307 
#>        V15        V16        V17        V18        V19        V20 
#> 0.07254165 0.07191043 0.04829758 0.06499330 0.07046749 0.07734053