make_agfh_sampler() to instantiate a sampler. Doing so
requires supplying the observed response (as an Small area statistics concerns estimation techniques for
sub-populations when direct estimation would be unreliable. The
agfh package implements the Agnostic Fay-Herriot model
(AGFH), an extension of the traditional small area model. In place of
normal sampling errors, the sampling error distribution is modified by a
Gaussian process to accommodate a broader class of distributions.
This flexibility is most useful in the presence of bounded, bi-modal, or heavily skewed sampling errors. Practitioners should consider the AGFH model when they have evidence of such departures from the traditional methods
Install the official version from CRAN:
install.packages('agfh')Next, consult the accompanying paper for a thorough background (under review), or the vignette within this package for an end-to-end illustration of the package.
The AGFH model is implemented as a Metropolis-within-Gibbs sampler;
use make_agfh_sampler() to instantiate a sampler. Doing so
requires supplying the observed response (as an -vector of univariate values), accompanying
covariates (as an
matrix of values), and sampling
error precision (again an
-vector of univariate values). Additionally, prior
hyperparameters can be supplied.
make_agfh_sampler() creates a sampler function; calling
it will produce MCMC samples targeting the posterior. It requires
starting values for the Gibbs components as well as the desired number
of steps and thinning rate. Note, n.mcmc=100 and
n.thin=10 would make 1000 MCMC steps and return every
tenth.
The sampler returns a list of relevant samples and summary values.
Typically, the contents of param.samples.list are most
interesting; these are the posterior samples from the AGFH model. The
convenience method map_from_density() may be used to get a
maximum a posteriori point estimate.
Parallel analysis with the traditional Fay-Herriot model is also
possible with agfh, as detailed in the vignette. In
particular, make_gibbs_sampler() returns a Gibbs sampler of
the traditional model that can be used in the same manner as
make_agfh_sampler().