rmw_calculate_model_errors

Function to calculate observed-predicted error statistics.

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_calculate_model_errors function

<dl><dt>df</dt>
<dd>Data frame with observed-predicted variables.</dd>
<dt>value_model</dt>
<dd>The modelled/predicted variable in <code>"df"</code>.</dd>
<dt>value_observed</dt>
<dd>The observed variable in <code>"df"</code>.</dd>
<dt>testing_only</dt>
<dd>Should only the testing set be used for the calculation 
of errors?</dd>
<dt>as_long</dt>
<dd>Should the returned tibble be in "long" format? This is useful
for plotting.</dd></dl>

Arguments

Author

Function to calculate observed-predicted error statistics. — rmw_calculate_model_errors

<dl>

<dt>df</dt>
<dd>Data frame with observed-predicted variables.</dd>


<dt>value_model</dt>
<dd>The modelled/predicted variable in <code>"df"</code>.</dd>


<dt>value_observed</dt>
<dd>The observed variable in <code>"df"</code>.</dd>


<dt>testing_only</dt>
<dd>Should only the testing set be used for the calculation 
of errors?</dd>


<dt>as_long</dt>
<dd>Should the returned tibble be in "long" format? This is useful
for plotting.</dd>

</dl>

rmw_calculate_model_errors: Function to calculate observed-predicted error statistics.

Description

Usage

Value

Arguments

Author