testMod: Function to test different meteorological normalisation...

In davidcarslaw/deweather: Remove the influence of weather on air quality data

Function to test different meteorological normalisation models.

Description

Function to test different meteorological normalisation models.

Usage

testMod(
  input_data,
  vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp"),
  pollutant = "nox",
  train.frac = 0.8,
  n.trees = NA,
  shrinkage = 0.1,
  interaction.depth = 5,
  bag.fraction = 0.5,
  n.minobsinnode = 10,
  cv.folds = 5,
  seed = 123,
  n.core = 4,
  plot = TRUE
)

Arguments

input_data	Data frame to analyse. Must contain a POSIXct field called date.
vars	Explanatory variables to use. These variables will be used to build the gbm::gbm() model. Note that the model must include a trend component. Several variables can be automatically calculated (see prepData() for details).
pollutant	The name of the variable to apply meteorological normalisation to.
train.frac	Fraction of data to train a model on. The model is tested against the withheld 0.2 proportion.
n.trees	Number of trees to use. If n.trees = NA then the function will conduct cross-validation to calculate the optimum number.
shrinkage	A shrinkage parameter applied to each tree in the expansion. Also known as the learning rate or step-size reduction; 0.001 to 0.1 usually work, but a smaller learning rate typically requires more trees. Default is 0.1.
interaction.depth	Integer specifying the maximum depth of each tree (i.e., the highest level of variable interactions allowed). A value of 1 implies an additive model, a value of 2 implies a model with up to 2-way interactions, etc. Default is 5.
bag.fraction	The fraction of the training set observations randomly selected to propose the next tree in the expansion. This introduces randomness into the model fit. If bag.fraction < 1 then running the same model twice will result in similar but different.
n.minobsinnode	Integer specifying the minimum number of observations in the terminal nodes of the trees. Note that this is the actual number of observations, not the total weight.
cv.folds	Number of cross-validation folds to perform. If cv.folds > 1 then gbm::gbm(), in addition to the usual fit, will perform a cross-validation, calculate an estimate of generalization error returned in cv.error.
seed	Random number seed for reproducibility in returned model.
n.core	Number of cores to use for parallel processing.
plot	The default, TRUE, automatically prints a plot and two tables of statistics to review the model output. FALSE disables this behaviour.

Value

Returns to be added.

Author(s)

David Carslaw

See Also

buildMod() for fitting a final model

davidcarslaw/deweather documentation built on June 11, 2025, 10:25 p.m.