rmw_predict_nested_partial_dependencies

Function to calculate partial dependencies from a random forest models using 
a nested tibble.

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_predict_nested_partial_dependencies function

<dl><dt>df_nest</dt>
<dd>Nested tibble created by <code>rmw_model_nested_sets</code>.</dd>
<dt>variables</dt>
<dd>Vector of variables to calculate partial dependencies for.</dd>
<dt>n_cores</dt>
<dd>Number of CPU cores to use for the model calculations.</dd>
<dt>training_only</dt>
<dd>Should only the training set be used for prediction?</dd>
<dt>rename</dt>
<dd>Within the <code>partial_dependencies</code> nested tibble, should 
the generic <code>"variable"</code> name be renamed to <code>"variable_model"</code>. 
This is useful when <code>"variable"</code> has been used as a pollutant identifier.</dd>
<dt>verbose</dt>
<dd>Should the function give messages?</dd>
<dt>progress</dt>
<dd>Should a progress bar be displayed?</dd></dl>

Arguments

Author

Function to calculate partial dependencies from a random forest models using 
a nested tibble. — rmw_predict_nested_partial_dependencies

<dl>

<dt>df_nest</dt>
<dd>Nested tibble created by <code>rmw_model_nested_sets</code>.</dd>


<dt>variables</dt>
<dd>Vector of variables to calculate partial dependencies for.</dd>


<dt>n_cores</dt>
<dd>Number of CPU cores to use for the model calculations.</dd>


<dt>training_only</dt>
<dd>Should only the training set be used for prediction?</dd>


<dt>rename</dt>
<dd>Within the <code>partial_dependencies</code> nested tibble, should 
the generic <code>"variable"</code> name be renamed to <code>"variable_model"</code>. 
This is useful when <code>"variable"</code> has been used as a pollutant identifier.</dd>


<dt>verbose</dt>
<dd>Should the function give messages?</dd>


<dt>progress</dt>
<dd>Should a progress bar be displayed?</dd>

</dl>

rmw_predict_nested_partial_dependencies: Function to calculate partial dependencies from a random forest models using a nested tibble.

Description

Usage

Value

Arguments

Author

See Also