M_j_sigc | R Documentation |
Numerically determines the parameter value M=M_J
of the SIGC(M
) prior,
such that the Hellinger distance between the marginal posteriors for the heterogeneity
standard deviation \tau
induced by the SIGC(M_J
) prior and Jeffreys (improper) reference prior
is minimal.
M_j_sigc(df, upper=3, digits=2, mu.mean=0, mu.sd=4)
df |
data frame with one column "y" containing the (transformed) effect estimates for the individual studies and one column "sigma" containing the standard errors of these estimates. |
upper |
upper bound for parameter |
digits |
specifies the desired precision of the parameter value |
mu.mean |
mean of the normal prior for the effect mu. |
mu.sd |
standard deviation of the normal prior for the effect mu. |
See the Supplementary Material of Ott et al. (2021, Section 2.6) for details.
Parameter value M=M_J
of the SIGC(M) prior. Real number > 1.
This function takes several minutes to run if the desired precision
is digits=2
and even longer for higher precision.
For some data sets, the optimal parameter value M=M_J
is very large
(e.g. of order 9*10^5).
If this function returns M_J
=upper
, then
the optimal parameter value may be larger than upper
.
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_j_sgc
# for aurigular acupuncture (AA) data set
data(aa)
# warning: it takes ca. 2 min. to run this function
M_j_sigc(df=aa, digits=1)
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