Function to test different meteorological normalisation models.

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.

See also

buildMod() for fitting a final model

Author

David Carslaw