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 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.

Details

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.

Value

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

References

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.

See Also

bayesmeta in the package bayesmeta, fit_models_RA, post_RA_fits, pri_RA

Examples

# 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)

[Package ra4bayesmeta version 0.1-2 Index]