pri_RA_fits: Prior reference analysis based on bayesmeta fits
In ra4bayesmeta: Reference Analysis for Bayesian Meta-Analysis

Description Usage Arguments Details Value References See Also Examples

Computes a table of Hellinger distance estimates between the benchmark heterogeneity priors inducing the specified model fits in fits.bm and the actual heterogenity priors inducing the specified actual model fits in fits.actual. All fits should be based on the same data set.

1	pri_RA_fits(fits.actual, fits.bm)

`fits.actual`	a list of model fits of class bayesmeta, computed with the `bayesmeta` function in the package bayesmeta.
`fits.bm`	a list of model fits of class bayesmeta, computed with the `bayesmeta` function in the package bayesmeta. To be used as benchmarks.

Two alternative suggestions for benchmark priors are provided in Ott et al. (2021, Section 3.4) and its Supplementary Material (Sections 2.5 and 2.6). These benchmark priors and the corresponding fits can be computed using the functions fit_models_RA and fit_models_RA_5bm, respectively.

A matrix of Hellinger distance estimates between actual and benchmark heterogeneity priors with n.bm columns and n.act rows, where n.bm=length(fit.bms) is the number of benchmark fits specified and n.act=length(fit.actual) the number of actual fits specified.

The columns of the matrix give the following Hellinger distance estimates between two heterogeneity priors (from left to right):

`H(pri_bm_1, pri_act)`	first benchmark prior bm_1 inducing the fit `fits.bm[[1]]` and actual prior
`H(pri_bm_2, pri_act)`	second benchmark prior bm_2 inducing the fit `fits.bm[[2]]` and actual prior
`...`	...
`H(pri_bm_n.bm, pri_act)`	last benchmark prior bm_n.bm inducing the fit `fits.bm[[n.bm]]` and actual prior

Each row correspond to one actual heterogeneity prior inducing one of the fits in fits.actual, in the same order as in that list. Thus, the row names are:

`pri_act_1`	first actual prior in `tau.prior`
`pri_act_2`	second actual prior in `tau.prior`
`...`	...
`pri_act_n`	nth (last) actual prior in `tau.prior`

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.

Ott, M., Plummer, M., Roos, M. How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis. Manuscript revised for Statistics in Medicine. 2021.

bayesmeta in the package bayesmeta, fit_models_RA_5bm, post_RA_fits, pri_RA_5bm

# for aurigular acupuncture (AA) data set 
data(aa)
# compute the model fits  % function with long running time
# two actual half-normal heterogeneity priors
# warning: it takes ca. 7 min. to run this function
fits <- fit_models_RA_5bm(df=aa, tau.prior=
                                 list(function(t)dhalfnormal(t, scale=0.5),
                                      function(t)dhalfnormal(t, scale=1)))[[1]]
# benchmark fits under SGC(m_inf), SIGC(M_J), SGC(m_J) and SIGC(M_inf) priors
fits.bm.pri <- fits[1:4]
# actual fits under HN priors
fits.actual <- fits[c(6,7)]
# prior reference analysis
pri_RA_fits(fits.actual=fits.actual, fits.bm=fits.bm.pri)