rmw_model_nested_sets

Function to train 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_model_nested_sets function

<dl><dt>df_nest</dt>
<dd>Nested tibble created by <code>rmw_nest_for_modelling</code>.</dd>
<dt>variables</dt>
<dd>Independent/explanatory variables used to predict <code>"value"</code>.</dd>
<dt>n_trees</dt>
<dd>Number of trees to grow to make up the forest.</dd>
<dt>mtry</dt>
<dd>Number of variables to possibly split at in each node. Default is
the (rounded down) square root of the number variables.</dd>
<dt>min_node_size</dt>
<dd>Minimal node size.</dd>
<dt>n_cores</dt>
<dd>Number of CPU cores to use for the model calculations.</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 train random forest models using a nested tibble. — rmw_model_nested_sets

<dl>

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


<dt>variables</dt>
<dd>Independent/explanatory variables used to predict <code>"value"</code>.</dd>


<dt>n_trees</dt>
<dd>Number of trees to grow to make up the forest.</dd>


<dt>mtry</dt>
<dd>Number of variables to possibly split at in each node. Default is
the (rounded down) square root of the number variables.</dd>


<dt>min_node_size</dt>
<dd>Minimal node size.</dd>


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


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


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

</dl>

rmw_model_nested_sets: Function to train random forest models using a nested tibble.

Description

Usage

Value

Arguments

Author

See Also