M_inf_sigc | R Documentation |
Computes the parameter value M=M_{inf}
of the SIGC(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
SIGC(M_{inf}
) prior.
M_inf_sigc(rlmc, df, alpha=0.5, truncation=5*10^6)
rlmc |
target RLMC value. Real number in |
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 |
See the Supplementary Material of Ott et al. (2021), Section 2.3.2,
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 SIGC(M
) prior. Real number > 1.
Occasionally, the formula for M_{inf}
given in the Supplementary Material of
Ott et al. (2021, Section 2.3.2)
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. (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")}
m_inf_sgc
# extreme RLMC target value close to 1 used in Ott et al. (2021)
# 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)
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