pri_RA_fits {ra4bayesmeta} | 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 |
Suggestions for prior benchmarks are provided
in Ott et al. (2020, Sections 2.5 and 2.7) and they
can be computed using the function fit_models_RA
.
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 |
H(pri_bm_2, pri_act) |
second benchmark prior bm_2 inducing the fit |
... |
... |
H(pri_bm_n.bm, pri_act) |
last benchmark prior bm_n.bm inducing the fit |
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 |
pri_act_2 |
second actual prior in |
... |
... |
pri_act_n |
nth (last) actual prior in |
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.
bayesmeta
in the package bayesmeta,
fit_models_RA
,
post_RA_fits
,
pri_RA
# for aurigular acupuncture (AA) data set data(aa) # compute the model fits # two actual half-normal heterogeneity priors fits <- fit_models_RA(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)] pri_RA_fits(fits.actual=fits.actual, fits.bm=fits.bm.pri)