M_inf_sigc {ra4bayesmeta} | R Documentation |
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.
M_inf_sigc(rlmc, df, alpha=0.5, truncation=5*10^6)
rlmc |
target RLMC value. Real number in (0,1). |
df |
data frame with one column "sigma" containing the standard errors of the estimates for the individual studies. |
alpha |
determines the (1- |
truncation |
upper bound for the parameter value M. Defaults to the empirically determined value 5*10^6. |
See the Supplementary Material of Ott et al. (2020), Section 2.6, for the formulas and explanations. Note that the parameter value M_{inf} does depend on the data set considered.
Parameter value M=M_{inf} of the SGC(M) prior. Real number > 1.
Occasionally, the formula for M_{inf} given in the Supplementary Material of
Ott et al. (2020, Section 2.8)
yields values larger than 5*10^6. This can cause numerical problems in the
bayesmeta
function.
Therefore, we truncate the parameter value at the
empirically determined threshold 5*10^6 by default.
Ott, M., Plummer, M., Roos, M. How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Manuscript submitted to Statistics in Medicine. 2020.
# extreme RLMC target value close to 1 used in Ott et al. (2020) # for the aurigular acupuncture (AA) data set data(aa) M_inf_sigc(df=aa, rlmc=0.9999) # for the respiratory tract infections (RTI) data set data(rti) M_inf_sigc(df=rti, rlmc=0.9999) # 75% quantile instead of the median as ref. threshold M_inf_sigc(df=rti, rlmc=0.9999, alpha=0.25)