sim_x

Simulate functional covariate observations.

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

sim_x function

<dl><dt>param</dt>
<dd>a list containing :<dl>
 <dt>grid</dt>
<dd>a numerical vector, the observation times.</dd></dl></dd> <dt>n</dt>
<dd>an integer, the sample size.</dd> <dt>p</dt>
<dd>an integer, the number of observation times.</dd> <dt>diagVar</dt>
<dd>a numerical vector (optional), the diagonal of the autocorrelation matrix.</dd> <dt>dim</dt>
<dd>a numerical value (optional), the dimension of the Fourier basis,
 if "shape" is "Fourier" or "Fourier2".</dd> <dt>ksi</dt>
<dd>a numerical value (optional) related to the observations correlation.</dd> <dt>x_shape</dt>
<dd>a character vector (optional), the shape of the observations.</dd>
</dl>

Arguments

sim_x — sim_x

<dl>

<dt>param</dt>
<dd>a list containing :<dl>
 <dt>grid</dt>
<dd>a numerical vector, the observation times.</dd>

 <dt>n</dt>
<dd>an integer, the sample size.</dd>

 <dt>p</dt>
<dd>an integer, the number of observation times.</dd>

 <dt>diagVar</dt>
<dd>a numerical vector (optional), the diagonal of the autocorrelation matrix.</dd>

 <dt>dim</dt>
<dd>a numerical value (optional), the dimension of the Fourier basis,
 if "shape" is "Fourier" or "Fourier2".</dd>

 <dt>ksi</dt>
<dd>a numerical value (optional) related to the observations correlation.</dd>

 <dt>x_shape</dt>
<dd>a character vector (optional), the shape of the observations.</dd>


</dl></dd>

</dl>

sim_x: sim_x

Description

Usage

Value

Arguments

Details

Examples