rmw_plot_test_prediction

Function to plot the test set and predicted set after
<code>rmw_predict_the_test_set</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_test_prediction function

<dl><dt>df</dt>
<dd>Tibble created by <code>rmw_predict_the_test_set</code>.</dd>
<dt>bins</dt>
<dd>Numeric vector giving number of bins in both vertical and 
horizontal directions.</dd>
<dt>coord_equal</dt>
<dd>Should axes be forced to be equal?</dd></dl>

Arguments

Author

Function to plot the test set and predicted set after
rmw_predict_the_test_set. — rmw_plot_test_prediction

<dl>

<dt>df</dt>
<dd>Tibble created by <code>rmw_predict_the_test_set</code>.</dd>


<dt>bins</dt>
<dd>Numeric vector giving number of bins in both vertical and 
horizontal directions.</dd>


<dt>coord_equal</dt>
<dd>Should axes be forced to be equal?</dd>

</dl>

rmw_plot_test_prediction: Function to plot the test set and predicted set after `rmw_predict_the_test_set`.

Description

Usage

Value

Arguments

Author