post_RA {ra4bayesmeta} | R Documentation |
Computes a table of Hellinger distances between marginal posterior distributions
for different parameters in the NNHM
induced by the actual heterogeneity priors specified in tau.prior
and
posterior benchmarks proposed in Ott et al. (2020).
post_RA(df, tau.prior=list(function(x) dhalfnormal(x, scale=1)), m_inf=NA, M_inf=NA, rlmc0=0.0001, rlmc1=0.9999, mu.mean=0, mu.sd=4)
df |
data frame with one column "y" containing the (transformed) effect estimates for the individual studies and one column "sigma" containing the standard errors of these estimates. |
tau.prior |
list of prior specifications, which are either functions returning the probability densities of the actual priors of interest for the heterogeneity parameter tau or character strings specifying priors implemented in the |
m_inf |
parameter value m=m_{inf} of the SGC(m) prior,
such that the median relative latent model complexity (RLMC) is close to 0.
If set to |
M_inf |
parameter value M=M_{inf} of the SIGC(M) prior,
such that the median relative latent model complexity (RLMC) is close to 1.
If set to |
rlmc0 |
RLMC target value for the SGC(m_{inf}) benchmark prior (typically close to 0).
Is required only if |
rlmc1 |
RLMC target value for the SIGC(M_{inf}) benchmark prior (typically close to 1).
Is required only if |
mu.mean |
mean of the normal prior for the effect mu. |
mu.sd |
standard deviation of the normal prior for the effect mu. |
The three posterior benchmarks used are introduced in Ott et al. (2020, Sections 2.5 and 2.6), where they are denoted by po_{m_{inf}}(Ψ), po_{J}(Ψ) and po_{M_{inf}}(Ψ). Here, Ψ \in \{ μ, τ, θ_1, ..., θ_k, θ_{new} \} denotes the parameter of interest in the NNHM, where θ_{i} is the random effect in the ith study and θ_{new} the predicted effect for a new study.
Note that Jeffreys' reference posterior po_{J} is proper if there are at least two studies in the meta-analysis data set. It is based on the improper Jeffreys' reference prior, which is minimally informative given the data.
The default values for mu.mean
and mu.sd
are suitable for effects mu on the log odds (ratio) scale.
A list with two elements:
The first element named "table" is a matrix containing the Hellinger distance estimates and the
second element called "par" is a named vector giving the parameter values
of the benchmark priors.
The vector "par" has the following three components:
m_inf
, M_inf
and C
.
The matrix "table" contains the Hellinger distance estimates between marginal posteriors
and has 3 columns and n*(k+3) rows,
where n=length(tau.prior
) is the number of actual heterogeneity priors specified
and k is the number of studies in the meta-analysis data set
(so that there are k+3 parameters of interest).
The columns of the matrix give the following Hellinger distance estimates between two marginal posteriors (for the parameter of interest Ψ varying with rows) induced by the following two heterogeneity priors (from left to right):
H(po_m_inf, po_act) |
benchmark prior SGC(m_inf) and actual prior |
H(po_J, po_act) |
Jeffreys' reference prior π_J and actual prior |
H(po_M_inf, po_act) |
benchmark prior SIGC(M_inf) and actual prior |
The actual heterogenity prior and the parameter of interest Ψ vary with the rows in the following order:
mu, pri_act_1 |
Ψ=μ and first actual prior in |
mu, pri_act_2 |
Ψ=μ and second actual prior in |
... |
|
mu, pri_act_n |
Ψ=μ and nth actual prior in |
tau, pri_act_1 |
Ψ=τ and first actual prior in |
... |
|
tau, pri_act_n |
Ψ=τ and nth actual prior |
theta_1, pri_act_1 |
Ψ=θ_1 and first actual prior |
... |
|
theta_k, pri_act_n |
Ψ=θ_k and nth actual prior |
theta_new, pri_act_1 |
Ψ=θ_{new} and first actual prior |
... |
|
theta_new, pri_act_n |
Ψ=θ_{new} and nth actual prior |
A warning message will be displayed if one of the parameters m_inf
or M_inf
has a value larger than 5*10^6, since this may lead to numerical problems
in the bayesmeta
function
used for computation of the marginal posteriors.
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.
# for aurigular acupuncture (AA) data set with two # actual half-normal heterogeneity priors data(aa) post_RA(df=aa, tau.prior=list(function(t)dhalfnormal(t, scale=0.5), function(t)dhalfnormal(t, scale=1)))