pri_RA_fits {ra4bayesmeta} | R Documentation |
Prior reference analysis based on bayesmeta fits
Description
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.
Usage
pri_RA_fits(fits.actual, fits.bm)
Arguments
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 |
Details
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.
Value
A matrix of Hellinger distance estimates between actual and benchmark
heterogeneity priors
with columns and
rows,
where
=length(
fit.bms
) is the number of benchmark fits specified and
=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 priorH(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
References
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. 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. doi:10.1002/sim.9076
See Also
bayesmeta
in the package bayesmeta,
fit_models_RA_5bm
,
post_RA_fits
,
pri_RA_5bm
Examples
# 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)