pri_RA_fits | R Documentation |

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.

```
pri_RA_fits(fits.actual, fits.bm)
```

`fits.actual` |
a list of model fits of class bayesmeta, computed with
the |

`fits.bm` |
a list of model fits of class bayesmeta, computed with
the |

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. (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")}

Ott, M., Plummer, M., Roos, M. (2021). How vague is vague?
How informative is informative? Reference analysis for
Bayesian meta-analysis. *Statistics in Medicine* **40**, 4505–4521.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.9076")}

`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)
```

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