m_inf_sgc: Optimization function for the SGC(m) prior: Adjust the prior...

In ra4bayesmeta: Reference Analysis for Bayesian Meta-Analysis

Optimization function for the SGC(m) prior: Adjust the prior to a target relative latent model complexity (RLMC)

Description

Computes the parameter value m=m_{inf} of the SGC(m) prior, such that the relative latent model complexity (RLMC) with respect to the reference threshold is approximately rlmc. The reference threshold is chosen as the (1-alpha)-quantile of the SGC(m_{inf}) prior.

Usage

m_inf_sgc(rlmc, alpha=0.5)

Arguments

rlmc	target RLMC value. Real number in (0,1).
alpha	determines the (1-alpha)-quantile of the SGC(m) prior, which is used as reference threshold. Defaults to 0.5 (i.e. the median).

Details

See the Supplementary Material of Ott et al. (2021), Section 2.3.1, for the formulas and explanations. Note that the parameter value m_{inf} does not depend on the data set considered.

Value

Parameter value m=m_{inf} of the SGC(m) prior. Real number > 1.

References

Ott, M., Plummer, M., Roos, M. (2021). Supplementary Material: How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Statistics in Medicine. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.9076")}

See Also

M_inf_sigc

Examples

# extreme RLMC target value close to 0 used in Ott et al. (2021)
m_inf_sgc(rlmc=0.0001)

# 25% quantile instead of the median as ref. threshold
m_inf_sgc(rlmc=0.0001, alpha=0.75)

ra4bayesmeta documentation built on Oct. 7, 2023, 1:07 a.m.