rmw_predict_nested_sets_by_year

Function to make predictions by meteorological year 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_sets_by_year function

<dl><dt>df_nest</dt>
<dd>Nested tibble created by <code>rmw_model_nested_sets</code>.</dd>
<dt>variables</dt>
<dd>Variables to randomly sample. Default is all variables used
for training the model with the exception of <code>date_unix</code>, the trend term.</dd>
<dt>n_samples</dt>
<dd>Number of times to sample the observations from each 
meteorological year and then predict.</dd>
<dt>aggregate</dt>
<dd>Should all the <code>n_samples</code> predictions be aggregated?</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></dl>

Arguments

Author

Function to make predictions by meteorological year from a random forest 
models using a nested tibble. — rmw_predict_nested_sets_by_year

<dl>

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


<dt>variables</dt>
<dd>Variables to randomly sample. Default is all variables used
for training the model with the exception of <code>date_unix</code>, the trend term.</dd>


<dt>n_samples</dt>
<dd>Number of times to sample the observations from each 
meteorological year and then predict.</dd>


<dt>aggregate</dt>
<dd>Should all the <code>n_samples</code> predictions be aggregated?</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>

</dl>

rmw_predict_nested_sets_by_year: Function to make predictions by meteorological year from a random forest models using a nested tibble.

Description

Usage

Value

Arguments

Author

See Also