predict_functions

This is the generic predict_functions method. See the following functions
for the details about different data structures:

manip

Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters.  The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.

Terrance Savitsky

growfunctions

Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data

predict_functions function

<dl><dt>object</dt>
<dd>Object of class <code>gpdpgrow</code> or<code>gmrfdpgrow()</code>.</dd>
<dt>J</dt>
<dd>Scalar denoting number of draws to take from posterior predictive for each unit.
Defaults to <code>J = 500</code>.</dd>
<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd></dl>

Arguments

Use the model-estimated covariance parameters from gpdpgrow() or gmrdpgrow to predict the function at
future time points. — predict_functions

<dl>

<dt>object</dt>
<dd>Object of class <code>gpdpgrow</code> or<code>gmrfdpgrow()</code>.</dd>


<dt>J</dt>
<dd>Scalar denoting number of draws to take from posterior predictive for each unit.
Defaults to <code>J = 500</code>.</dd>


<dt>...</dt>
<dd>further arguments passed to or from other methods.</dd>

</dl>

predict_functions: Use the model-estimated covariance parameters from gpdpgrow() or gmrdpgrow to predict the function at future time points.

Description

Usage

Arguments

Details