extract_descriptives_inla: Extraction of descriptive statistics from an INLA object

In hunansona/ed4bhm: Empirical Determinacy for Bayesian Hierarchical Models

Extraction of descriptive statistics from an INLA object

Description

This function takes an INLA object as input and extracts the descriptive statistics (mean and standard deviation) of all the model parameters.

Usage

extract_descriptives_inla(inla.object, dz = 0.75, diff.logdens = 15)

Arguments

inla.object	character string, the name of the INLA object fitted in inla
dz	Step length in the standardized scale used in the construction of the grid, default 0.75.
diff.logdens	The difference of the log.density for the hyperpameters to stop numerical integration using int.strategy='grid'. Default 15.

Details

The returned matrix has two columns with names "mean" and "sd" for the mean and standard deviation of the marginal posterior distributions and has as many rows as there are parameters in the model.

Value

numerical matix

See Also

inla

Examples

data(eight_schools)
#prior settings
mean_mu<-0
prec_mu<-1/(4^2) 
prec_tau<-1/(5^2)

library(INLA)
HN.prior = "expression:
  tau0 =  1/(5^2);
  sigma = exp(-theta/2);
  log_dens = log(2) - 0.5 * log(2 * pi) + 0.5 * log(tau0);
  log_dens = log_dens - 0.5 * tau0 * sigma^2;
  log_dens = log_dens - log(2) - theta / 2;
  return(log_dens);
"

formula.8schools.HN <- y ~ 1+f(schooln, model="iid", 
                                      hyper = list(prec = list(prior = HN.prior)))

# INLA uses the centered parametrization of the 8 schools model by default
fit.inla.8schools <- inla(formula.8schools.HN,
                                data = eight_schools,
                                family = "gaussian",
                                scale = eight_schools$prec,
                                control.family = list(hyper=list(prec=list(initial = log(1), fixed=TRUE))),
                                control.fixed = list(mean.intercept=mean_mu, prec.intercept=prec_mu),
                                control.compute=list(hyperpar=TRUE),
                                num.threads=1)

descriptives_inla_8schools <- extract_descriptives_inla(inla.obj = fit.inla.8schools)

hunansona/ed4bhm documentation built on June 15, 2022, 6:42 p.m.