add_predictions

Given user-provided <code>newdata</code>, expand the object <code>tmb_data</code>
to include predictions corresponding to those new observations

Fits a wide variety of multivariate spatio-temporal models
with simultaneous and lagged interactions among variables (including
vector autoregressive spatio-temporal ('VAST') dynamics)
for areal, continuous, or network spatial domains.
It includes time-variable, space-variable, and space-time-variable
interactions using dynamic structural equation models ('DSEM')
as expressive interface, and the 'mgcv' package to specify splines
via the formula interface. See Thorson et al. (2024)
<doi:10.48550/arXiv.2401.10193> for more details.

James Thorson

tinyVAST

Multivariate Spatio-Temporal Models using Structural Equations

add_predictions function

<dl><dt>object</dt>
<dd>Output from <code>tinyVAST()</code>.</dd>
<dt>newdata</dt>
<dd>New data-frame of independent variables used to predict the response.</dd>
<dt>remove_origdata</dt>
<dd>Whether to remove original-data to allow faster evaluation.
<code>remove_origdata=TRUE</code> eliminates information about the distribution
for random effects, and cannot be combined with epsilon bias-correction.
WARNING: feature is experimental and subject to change.</dd></dl>

Arguments

Add predictions to data-list — add_predictions

<dl>

<dt>object</dt>
<dd>Output from <code>tinyVAST()</code>.</dd>


<dt>newdata</dt>
<dd>New data-frame of independent variables used to predict the response.</dd>


<dt>remove_origdata</dt>
<dd>Whether to remove original-data to allow faster evaluation.
<code>remove_origdata=TRUE</code> eliminates information about the distribution
for random effects, and cannot be combined with epsilon bias-correction.
WARNING: feature is experimental and subject to change.</dd>

</dl>

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add_predictions: Add predictions to data-list

Description

Usage

Value

Arguments