rmw_normalise_nested_sets

Function to normalise a variable for "average" meteorological conditions in
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_normalise_nested_sets 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 <code>df</code> and then predict?</dd>
<dt>replace</dt>
<dd>Should <code>variables</code> be sampled with replacement?</dd>
<dt>se</dt>
<dd>Should the standard error of the predictions be calculated too? 
The standard error method is the "infinitesimal jackknife for bagging" and 
will slow down the predictions significantly.</dd>
<dt>aggregate</dt>
<dd>Should all the <code>n_samples</code> predictions be aggregated?</dd>
<dt>keep_samples</dt>
<dd>When <code>aggregate</code> is <code>FALSE</code>, should the 
sampled/shuffled observations be kept?</dd>
<dt>n_cores</dt>
<dd>Number of CPU cores to use for the model predictions. Default
is system's total minus one.</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 normalise a variable for "average" meteorological conditions in
a nested tibble. — rmw_normalise_nested_sets

<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 <code>df</code> and then predict?</dd>


<dt>replace</dt>
<dd>Should <code>variables</code> be sampled with replacement?</dd>


<dt>se</dt>
<dd>Should the standard error of the predictions be calculated too? 
The standard error method is the "infinitesimal jackknife for bagging" and 
will slow down the predictions significantly.</dd>


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


<dt>keep_samples</dt>
<dd>When <code>aggregate</code> is <code>FALSE</code>, should the 
sampled/shuffled observations be kept?</dd>


<dt>n_cores</dt>
<dd>Number of CPU cores to use for the model predictions. Default
is system's total minus one.</dd>


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


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

</dl>

rmw_normalise_nested_sets: Function to normalise a variable for "average" meteorological conditions in a nested tibble.

Description

Usage

Value

Arguments

Author

See Also