m_j_sgc {ra4bayesmeta} | R Documentation |
Numerically determines the parameter value m=m_J of the SGC(m) prior, such that the Hellinger distance between the marginal posteriors for the heterogeneity standard deviation τ induced by the SGC(m_J) and Jeffreys' (improper) reference prior is minimal.
m_j_sgc(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 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 Ott et al. (2020, Section 2.7) for details.
Parameter value m=m_J of the SGC(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.
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.
# for aurigular acupuncture (AA) data set # warning: it takes ca. 2 min. to run this function data(aa) m_j_sgc(df=aa, digits=1)