Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data

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Documentation for package ‘rmweather’ version 0.2.6

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%>% Pseudo-function to re-export *magrittr*'s pipe.
base functions Pseudo-function to re-export functions from the *stats* package.
data_london Example observational data for the *rmweather* package.
data_london_normalised Example of meteorologically normalised data for the *rmweather* package.
dplyr functions Pseudo-function to re-export *dplyr*'s common functions.
model_london Example *ranger* random forest model for the *rmweather* package.
rmw_calculate_model_errors Function to calculate observed-predicted error statistics.
rmw_clip Function to "clip" the edges of a normalised time series after being produced with 'rmw_normalise'.
rmw_do_all Function to train a random forest model to predict (usually) pollutant concentrations using meteorological and time variables and then immediately normalise a variable for "average" meteorological conditions.
rmw_find_breakpoints Function to detect breakpoints in a data frame using a linear regression based approach.
rmw_model_importance Functions to extract model statistics from a model calculated with 'rmw_calculate_model'.
rmw_model_nested_sets Function to train random forest models using a nested tibble.
rmw_model_statistics Functions to extract model statistics from a model calculated with 'rmw_calculate_model'.
rmw_nest_for_modelling Function to nest observational data before modelling with *rmweather*.
rmw_normalise Function to normalise a variable for "average" meteorological conditions.
rmw_normalise_nested_sets Function to normalise a variable for "average" meteorological conditions in a nested tibble.
rmw_partial_dependencies Function to calculate partial dependencies after training with *rmweather*.
rmw_plot_importance Function to plot random forest variable importances after training by 'rmw_train_model'.
rmw_plot_normalised Function to plot the meteorologically normalised time series after 'rmw_normalise'.
rmw_plot_partial_dependencies Function to plot partial dependencies after calculation by 'rmw_partial_dependencies'.
rmw_plot_test_prediction Function to plot the test set and predicted set after 'rmw_predict_the_test_set'.
rmw_predict Function to predict using a *ranger* random forest.
rmw_predict_nested_partial_dependencies Function to calculate partial dependencies from a random forest models using a nested tibble.
rmw_predict_nested_sets Function to make predictions from a random forest models using a nested tibble.
rmw_predict_nested_sets_by_year Function to make predictions by meteorological year from a random forest models using a nested tibble.
rmw_predict_the_test_set Functions to use a model to predict the observations within a test set after 'rmw_calculate_model'.
rmw_prepare_data Function to prepare a data frame for modelling with *rmweather*.
rmw_train_model Function to train a random forest model to predict (usually) pollutant concentrations using meteorological and time variables.
system_cpu_core_count Function to return the system's number of CPU cores.
wday_monday Function to get weekday number from a date where '1' is Monday and '7' is Sunday.
zzz Squash the global variable notes when building a package.