check_wildfire_vif

Checks variance inflation factors of predictor variables using a
linear model of the predictor variables on the health outcome. Prints stats
if print_vif==TRUE. Raises a warning if VIF for a variables is &gt; 2.

internal

Tools for producing climate-health indicators and supporting
official statistics from health and climate data. Implements analytical
workflows for temperature-related mortality, wildfire smoke exposure,
air pollution, suicides related to extreme heat, malaria, and
diarrhoeal disease outcomes, with utilities for descriptive statistics, model
validation, attributable fraction and attributable number estimation,
relative risk estimation, minimum mortality temperature estimation,
and plotting for reporting. These six indicators are endorsed by
the United Nations Statistical Commission for inclusion in the
Global Set of Environment and Climate Change Statistics.
Implemented methods include distributed lag non-linear models (DLNM),
quasi-Poisson time-series regression, case-crossover analysis,
Bayesian spatio-temporal models using the Integrated Nested Laplace
Approximation ('INLA'), and multivariate meta-analysis for
sub-national estimates. The package is based on methods developed
in the Standards for Official Statistics on Climate-Health
Interactions (SOSCHI) project
<https://climate-health.officialstatistics.org>. For methodologies,
see Watkins et al. (2025) <doi:10.5281/zenodo.14865904>,
Brown et al. (2024) <doi:10.5281/zenodo.14052183>,
Pearce et al. (2024) <doi:10.5281/zenodo.14050224>,
Byukusenge et al. (2025) <doi:10.5281/zenodo.15585042>,
Dzakpa et al. (2025) <doi:10.5281/zenodo.14881886>, and
Dzakpa et al. (2025) <doi:10.5281/zenodo.14871506>.

Kenechi Omeke

climatehealth

Statistical Tools for Modelling Climate-Health Impacts

Charlie Browning

Etse Yawo Dzakpa

Gladin Jose

Matt Pearce

Ellie Watkins

Claire Hunt

Beatrice Byukusenge

Cassien Habyarimana

Venuste Nyagahakwa

Felix Scarbrough

Treesa Shaji

Bonnie Lewis

Maquines Odhiambo Sewe

Vijendra Ingole

Sean Lovell

Antony Brown

Euan Soutter

Gillian Flower

David Furley

Joe Panes

Charlotte Romaniuk

Milly Powell

Wellcome 

Office for National Statistics 

check_wildfire_vif function

<dl><dt>data</dt>
<dd>Dataframe containing a daily time series of climate and health
data.</dd>
<dt>predictors</dt>
<dd>Character vector with each of the predictors to include
in the model. Must contain at least 2 variables.</dd>
<dt>save_csv</dt>
<dd>Bool. Whether or not to save the VIF results to a CSV.</dd>
<dt>output_folder_path</dt>
<dd>String. Where to save the CSV file to (if save_csv == TRUE).</dd>
<dt>print_vif</dt>
<dd>Bool, whether or not to print VIF for each predictor.
Defaults to FALSE.</dd></dl>

Arguments

Check variance inflation factors of predictor variables using a linear model — check_wildfire_vif

<dl>

<dt>data</dt>
<dd>Dataframe containing a daily time series of climate and health
data.</dd>


<dt>predictors</dt>
<dd>Character vector with each of the predictors to include
in the model. Must contain at least 2 variables.</dd>


<dt>save_csv</dt>
<dd>Bool. Whether or not to save the VIF results to a CSV.</dd>


<dt>output_folder_path</dt>
<dd>String. Where to save the CSV file to (if save_csv == TRUE).</dd>


<dt>print_vif</dt>
<dd>Bool, whether or not to print VIF for each predictor.
Defaults to FALSE.</dd>

</dl>

Check variance inflation factors of predictor variables using a linear model

check_wildfire_vif: Check variance inflation factors of predictor variables using a linear model

Description

Usage

Value

Arguments