Description Usage Arguments Details Value Warning References See Also Examples
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 τ induced by the SIGC(M_J) prior and Jeffreys (improper) reference prior is minimal.
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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 M. Real number in (1,∞). |
digits |
specifies the desired precision of the parameter value M=M_J, i.e. to how many digits this value should be determined. Possible values are 1,2,3. Defaults to 2. |
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. Supplementary Material: How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Revised for Statistics in Medicine. 2021.
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