h_choice_all {si4bayesmeta} | R Documentation |
h
step value for RLMC perturbations
Function for the choice of the numerical h
step value for RLMC perturbations for actual data and for all 6 scenarios given two RLMC-adjusted heterogeneity priors HN and HC and three RLMC targets (0.25, 0.5, 0.75) in Roos et al. (2020).
h_choice_all(df)
df |
data frame in bayesmeta format |
See the motivation for the choice of the grid_epsilon value (grid_epsilon
=0.00354) in Roos et al. (2015)
h |
mean perturbation |
val |
all perturbations |
Roos, M., Hunanyan, S., Bakka, H., Rue, H. (2020). Sensitivity and identification quantification by a relative latent model complexity perturbation in the Bayesian meta-analysis. Manuscript submitted to Research Synthesis Methods.
Roos, M., Martins, T., Held, L., Rue, H. (2015). Sensitivity analysis for Bayesian hierarchical models. Bayesian Analysis 10(2), 321-349. https://projecteuclid.org/euclid.ba/1422884977
# Acute Graft rejection (AGR) data analyzed in Friede et al. (2017), # Sect. 3.2, URL: https://doi.org/10.1002/bimj.201500236 # First study: experimental group: 14 cases out of 61; # control group: 15 cases out of 20 # Second study: experimental group: 4 cases out of 36; # control group: 11 cases out of 36 rT<-c(14,4) nT<-c(61,36) rC<-c(15,11) nC<-c(20,36) df = data.frame(y = log((rT*(nC-rC))/(rC*(nT-rT))), sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), labels = c(1:2)) h_choice_all(df=df)$h # AGR: 0.0044249