h_choice_all: The choice of the numerical 'h' step value for RLMC...
In si4bayesmeta: Sensitivity and Identification for the Bayesian Meta-Analysis

Description Usage Arguments Details Value References See Also Examples

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).

1	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

h2Nmuh1N01

# 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