rmw_plot_importance

Function to plot random forest variable importances after training by 
<code>rmw_train_model</code>.

An integrated set of tools to allow data users to conduct
meteorological normalisation and counterfactual modelling for air quality
data. The meteorological normalisation technique uses predictive random
forest models to remove variation of pollutant concentrations so trends and
interventions can be explored in a robust way. For examples, see
Grange et al. (2018) <doi:10.5194/acp-18-6223-2018> and
Grange and Carslaw (2019) <doi:10.1016/j.scitotenv.2018.10.344>. The random
forest models can also be used for counterfactual or business as usual (BAU)
modelling by using the models to predict, from the model's perspective, the
future. For an example, see Grange et al. (2021) <doi:10.5194/acp-2020-1171>.

Stuart K. Grange

rmweather

Tools to Conduct Meteorological Normalisation and Counterfactual
Modelling for Air Quality Data

rmw_plot_importance function

<dl><dt>df</dt>
<dd>Data frame created by <code>rmw_model_importance</code>.</dd>
<dt>colour</dt>
<dd>Colour of point and segment geometries.</dd></dl>

Arguments

Author

Function to plot random forest variable importances after training by 
rmw_train_model. — rmw_plot_importance

<dl>

<dt>df</dt>
<dd>Data frame created by <code>rmw_model_importance</code>.</dd>


<dt>colour</dt>
<dd>Colour of point and segment geometries.</dd>

</dl>

rmw_plot_importance: Function to plot random forest variable importances after training by `rmw_train_model`.

Description

Usage

Value

Arguments

Author

See Also