extract_names

This function allows the user to select variables from a data set by prefix
(using the <code>pattern</code> argument) or by exact name matching.
The return object is a character vector with the selected covariate names that can be used
as input for <code>cov_add</code>, <code>cov_uni</code>,
<code>cov_multi</code>, <code>cov_interact</code>, <code>cov_nl</code>,
and <code>cov_varying</code> functions.

Supports modeling health outcomes using Bayesian hierarchical
spatio-temporal models with complex covariate effects (e.g., linear,
non-linear, interactions, distributed lag linear and non-linear
models) in the 'INLA' framework. It is designed to help users identify key
drivers and predictors of disease risk by enabling streamlined model
exploration, comparison, and visualization of complex covariate effects.
See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021)
<doi:10.1016/S2542-5196(20)30292-8>.

Carles Milà

GHRmodel

Bayesian Hierarchical Modelling of Spatio-Temporal Health Data

Giovenale Moirano

Anna B. Kawiecki

Rachel Lowe

extract_names function

<dl><dt>data</dt>
<dd>A <code>data.frame</code> containing the variables.</dd>
<dt>pattern</dt>
<dd>A character vector specifying prefix(es) to match (e.g., "tmin" matches "tmin", "tmin.l1", etc.).</dd>
<dt>name</dt>
<dd>A character vector of exact variable name(s) to extract.</dd></dl>

Arguments

Extract Covariate Names — extract_names

<dl>

<dt>data</dt>
<dd>A <code>data.frame</code> containing the variables.</dd>


<dt>pattern</dt>
<dd>A character vector specifying prefix(es) to match (e.g., "tmin" matches "tmin", "tmin.l1", etc.).</dd>


<dt>name</dt>
<dd>A character vector of exact variable name(s) to extract.</dd>

</dl>

extract_names: Extract Covariate Names

Description

Usage

Value

Arguments

Examples