sim_marg_var

Estimate marginal variances of a (I)GMRF prior defined in terms
of a sparse precision matrix and possibly a set of equality constraints.
The marginal variances might be used to rescale the precision matrix
such that a default prior for a corresponding variance component is
more appropriate.

Fit multi-level models with possibly correlated
random effects using Markov Chain Monte Carlo simulation.
Such models allow smoothing over space and time and are useful in,
for example, small area estimation.

Harm Jan Boonstra

mcmcsae

Markov Chain Monte Carlo Small Area Estimation

Grzegorz Baltissen

sim_marg_var function

<dl><dt>D</dt>
<dd>factor of precision matrix Q such that Q=D'D.</dd>
<dt>Q</dt>
<dd>precision matrix.</dd>
<dt>R</dt>
<dd>equality restriction matrix.</dd>
<dt>r</dt>
<dd>rhs vector for equality constraints \(R'x = r\), where \(R'\) denotes the transpose of R.</dd>
<dt>eps1</dt>
<dd>passed to <code>create_cMVN_sampler</code>.</dd>
<dt>eps2</dt>
<dd>passed to <code>create_cMVN_sampler</code>.</dd>
<dt>nSim</dt>
<dd>number of Monte Carlo samples used to estimate the marginal variances.</dd></dl>

Arguments

Compute a Monte Carlo estimate of the marginal variances of a (I)GMRF — sim_marg_var

<dl>

<dt>D</dt>
<dd>factor of precision matrix Q such that Q=D'D.</dd>


<dt>Q</dt>
<dd>precision matrix.</dd>


<dt>R</dt>
<dd>equality restriction matrix.</dd>


<dt>r</dt>
<dd>rhs vector for equality constraints \(R'x = r\), where \(R'\) denotes the transpose of R.</dd>


<dt>eps1</dt>
<dd>passed to <code>create_cMVN_sampler</code>.</dd>


<dt>eps2</dt>
<dd>passed to <code>create_cMVN_sampler</code>.</dd>


<dt>nSim</dt>
<dd>number of Monte Carlo samples used to estimate the marginal variances.</dd>

</dl>

sim_marg_var: Compute a Monte Carlo estimate of the marginal variances of a (I)GMRF

Description

Usage

Value

Arguments

References