Function to apply meteorological normalisation

This is the main function to apply a gbm::gbm() model to a data set.

Usage

buildMod(
  input_data,
  vars = c("trend", "ws", "wd", "hour", "weekday", "air_temp"),
  pollutant = "nox",
  sam.size = nrow(input_data),
  n.trees = 200,
  shrinkage = 0.1,
  interaction.depth = 5,
  bag.fraction = 0.5,
  n.minobsinnode = 10,
  cv.folds = 0,
  simulate = FALSE,
  B = 100,
  n.core = 4,
  seed = 123
)

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.

sam.size

The number of random samples to extract from the data for model building. While it is possible to use the full data set, for data sets spanning years the model building can take a very long time to run. Additionally, there will be diminishing returns in terms of model accuracy. If sam.size is greater than the number of number of rows of data, the number of rows of data is used instead.

n.trees

Number of trees to fit.

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.

simulate

Should the original time series be randomly sampled with replacement? The default is FALSE. Setting simulate = TRUE can be useful for estimating model uncertainties. In which case models should be run multiple times with B = 1 and a different value of seed e.g. seed = runif(1).

B

Number of bootstrap simulations for partial dependence plots.

n.core

Number of cores to use for parallel processing.

seed

Random number seed for reproducibility in returned model.

Value

Returns a list including the model, influence data frame and partial dependence data frame.

See also

testMod() for testing models before they are built.

metSim() for using a built model with meteorological simulations.

plot2Way(), plotInfluence() and plotPD() for visualising built models.

Author

David Carslaw