rmw_normalise: Function to normalise a variable for "average" meteorological...

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

Function to normalise a variable for "average" meteorological conditions.

Description

Function to normalise a variable for "average" meteorological conditions.

Usage

rmw_normalise(
  model,
  df,
  variables = NA,
  n_samples = 300,
  replace = TRUE,
  se = FALSE,
  aggregate = TRUE,
  keep_samples = FALSE,
  n_cores = NA,
  verbose = FALSE
)

Arguments

model	A ranger model object from rmw_train_model.
df	Input data used to calculate model using rmw_prepare_data.
variables	Variables to randomly sample. Default is all variables used for training the model with the exception of date_unix, the trend term.
n_samples	Number of times to sample df and then predict?
replace	Should variables be sampled with replacement?
se	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.
aggregate	Should all the n_samples predictions be aggregated?
keep_samples	When aggregate is FALSE, should the sampled/shuffled observations be kept?
n_cores	Number of CPU cores to use for the model predictions. Default is system's total minus one.
verbose	Should the function give messages and display a progress bar?

Value

Tibble.

Author(s)

Stuart K. Grange

See Also

rmw_prepare_data, rmw_train_model

Examples

# Load package
library(dplyr)

# Keep things reproducible
set.seed(123)

# Prepare example data
data_london_prepared <- data_london %>% 
  filter(variable == "no2") %>% 
  rmw_prepare_data()

# Normalise the example no2 data
data_normalised <- rmw_normalise(
  model_london, 
  df = data_london_prepared, 
  n_samples = 300,
  verbose = TRUE
)

rmweather documentation built on June 22, 2024, 9:33 a.m.