raw_estimates_HC {si4bayesmeta} | R Documentation |
Computes the raw estimates for a Bayesian NNHM with a RLMC-adjusted HC heterogeneity prior given a target RLMC according to Roos et al. (2020).
raw_estimates_HC(df, rlmc = 0.5, mu_mean = 0, mu_sd = 4)
df |
original base data frame in a |
rlmc |
the value of the target RLMC, usually set to 0.25, 0.5 or 0.75. Default value is 0.5. |
mu_mean |
mean of the normal prior for |
mu_sd |
standard deviation of the normal prior for |
A matrix with the raw estimates and shortest 95% CrI and its length for mu
, tau
, thetai
, i=1,..,k
and theta_new
.
Needs to have the package bayesmeta
loaded.
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.
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))), # log-odds-ratio sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), # SE(log-odds-ratio) labels = c(1:2)) # Set the normal prior parameter values for mu according to Roever 2018 bayesmeta # Suitable for logOR and log-odds scale with the unit information argument mu_mean<-0 mu_sd<-4 # Fix the value of the target RMC t_RLMC_025<-0.25 ####---- HC: raw parameter estimates for the base NNHM ----#### raw_est_rlmc025_HC<-raw_estimates_HC(df=df, rlmc=t_RLMC_025, mu_mean=mu_mean, mu_sd=mu_sd) raw_est_rlmc025_HC