compute_chains_info

Compute summaries of Gibbs Sampler chains.

A method for the Bayesian functional linear regression model (scalar-on-function),
including two estimators of the coefficient function and an estimator of its support.
A representation of the posterior distribution is also available. Grollemund P-M., Abraham C.,
Baragatti M., Pudlo P. (2019) <doi:10.1214/18-BA1095>.

Paul-Marie Grollemund

bliss

Bayesian Functional Linear Regression with Sparse Step Functions

Isabelle Sanchez

Meili Baragatti

compute_chains_info function

<dl><dt>chain</dt>
<dd>a list given by the <code>Bliss_Gibbs_Sampler</code> function.</dd>
<dt>param</dt>
<dd>a list containing:<dl>
<dt>K</dt>
<dd>a vector of integers, corresponding to the numbers of intervals for
 each covariate.</dd></dl></dd><dt>grids</dt>
<dd>a numerical vector, the observation time points.</dd><dt>basis</dt>
<dd>a vector of characters (optional) among : "uniform" (default),
 "epanechnikov", "gauss" and "triangular" which correspond to
 different basis functions to expand the coefficient function and the
 functional covariates.</dd>
</dl>

Arguments

compute_chains_info — compute_chains_info

<dl>

<dt>chain</dt>
<dd>a list given by the <code>Bliss_Gibbs_Sampler</code> function.</dd>


<dt>param</dt>
<dd>a list containing:<dl>
<dt>K</dt>
<dd>a vector of integers, corresponding to the numbers of intervals for
 each covariate.</dd>

<dt>grids</dt>
<dd>a numerical vector, the observation time points.</dd>

<dt>basis</dt>
<dd>a vector of characters (optional) among : "uniform" (default),
 "epanechnikov", "gauss" and "triangular" which correspond to
 different basis functions to expand the coefficient function and the
 functional covariates.</dd>


</dl></dd>

</dl>

compute_chains_info: compute_chains_info

Description

Usage

Value

Arguments

Examples