predict_intervals

Computes CI from posterior, and PI for Tweedie and scat gams.

Implement a GAM-based (Generalized Additive Models) spatial surplus
production model (spatial SPM), aimed at modeling northern shrimp population
in Atlantic Canada but potentially to any stock in any location. The package
is opinionated in its implementation of SPMs as it internally makes the choice
to use penalized spatial gams with time lags. However, it also aims to provide
options for the user to customize their model. The methods are described in
Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).

Valentin Lucet

sspm

Spatial Surplus Production Model Framework for Northern Shrimp
Populations

Eric Pedersen

predict_intervals function

<dl><dt>object_fit</dt>
<dd>[gam OR bam] The fit to use for predictions.</dd>
<dt>new_data</dt>
<dd>[data.frame] The data to predict onto.</dd>
<dt>n</dt>
<dd>[numeric] The number of simulations to run for parameters.</dd>
<dt>CI</dt>
<dd>[logical] Whether to compute the CI.</dd>
<dt>PI</dt>
<dd>[logical] Whether to compute the PI.</dd>
<dt>...</dt>
<dd>further arguments passed to the quantile function.</dd></dl>

Arguments

GAM confidence and prediction intervals — predict_intervals

<dl>

<dt>object_fit</dt>
<dd>[gam OR bam] The fit to use for predictions.</dd>


<dt>new_data</dt>
<dd>[data.frame] The data to predict onto.</dd>


<dt>n</dt>
<dd>[numeric] The number of simulations to run for parameters.</dd>


<dt>CI</dt>
<dd>[logical] Whether to compute the CI.</dd>


<dt>PI</dt>
<dd>[logical] Whether to compute the PI.</dd>


<dt>...</dt>
<dd>further arguments passed to the quantile function.</dd>

</dl>

predict_intervals: GAM confidence and prediction intervals

Description

Usage

Value

Arguments

Examples