rmw_clip

<code>rmw_clip</code> helps if the random forest model behaves strangely at the 
beginning and end of the time series during prediction.

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_clip function

<dl><dt>df</dt>
<dd>Data frame from <code>rmw_normalise</code>.</dd>
<dt>seconds</dt>
<dd>Number of seconds to clip from start and end of time-series. 
The default is half a year.</dd></dl>

Arguments

Author

Function to "clip" the edges of a normalised time series after being 
produced with rmw_normalise. — rmw_clip

<dl>

<dt>df</dt>
<dd>Data frame from <code>rmw_normalise</code>.</dd>


<dt>seconds</dt>
<dd>Number of seconds to clip from start and end of time-series. 
The default is half a year.</dd>

</dl>

rmw_clip: Function to "clip" the edges of a normalised time series after being produced with `rmw_normalise`.

Description

Usage

Value

Arguments

Author

See Also

Examples