rescale_alpha: Rescale Significance Threshold for the Stack / M Correction

In ctreeMI: Conditional Inference Trees with Stacked Multiple Imputation

Rescale Significance Threshold for the Stack / M Correction

Description

Computes the adjusted significance threshold alpha / M used in the Stack / M correction of Sherlock et al. (2026). Dividing the nominal alpha by the number of imputations M counteracts the inflated test statistics that arise from stacking M copies of the data.

Usage

rescale_alpha(alpha = 0.05, m)

Arguments

alpha	Numeric. Nominal significance level (default 0.05). Must be strictly between 0 and 1.
m	A single positive integer. Number of imputations.

Value

A single numeric value: alpha / m.

References

Sherlock, P., Mansolf, M., Hofheimer, J., Hockett, C. W., O'Connor, T. G., Roubinov, D., Graff, J. C., Lai, J.-S., Bush, N. R., Wright, R. J., & Chiu, Y.-H. M. (2026). Beyond linear risk: A machine learning approach to understanding perinatal depression in context. Multivariate Behavioral Research, 1–16. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00273171.2026.2661244")}

See Also

ctree_stacked, stack_imputations

Examples

rescale_alpha(0.05, 30)   # 0.001666...
rescale_alpha(0.05, 10)   # 0.005
rescale_alpha(0.01, 5)    # 0.002

ctreeMI documentation built on July 26, 2026, 1:06 a.m.