Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data
Pseudo-function to re-export functions from the stats package.
Pseudo-function to re-export dplyr 's common functions.
Pseudo-function to re-export magrittr 's pipe.
Function to calculate observed-predicted error statistics.
Function to "clip" the edges of a normalised time series after being p...
Function to train a random forest model to predict (usually) pollutant...
Function to detect breakpoints in a data frame using a linear regressi...
Function to train random forest models using a nested tibble.
Functions to extract model statistics from a model calculated with `rm...
Function to nest observational data before modelling with rmweather .
Function to normalise a variable for "average" meteorological conditio...
Function to normalise a variable for "average" meteorological conditio...
Function to calculate partial dependencies after training with rmweath...
Function to plot random forest variable importances after training by ...
Function to plot the meteorologically normalised time series after `rm...
Function to plot partial dependencies after calculation by `rmw_partia...
Function to plot the test set and predicted set after `rmw_predict_the...
Function to calculate partial dependencies from a random forest models...
Function to make predictions by meteorological year from a random fore...
Function to make predictions from a random forest models using a neste...
Functions to use a model to predict the observations within a test set...
Function to predict using a ranger random forest.
Function to prepare a data frame for modelling with rmweather .
Function to train a random forest model to predict (usually) pollutant...
Function to return the system's number of CPU cores.
Function to get weekday number from a date where 1 is Monday and 7...
Squash the global variable notes when building a package.
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>.