autocartControl {autocart}

Create the object used for the controlling of the splits in the autocart model

Description

Create the object used for the controlling of the splits in the autocart model

Usage

autocartControl(
  minsplit = 20,
  minbucket = round(minsplit/3),
  maxdepth = 30,
  maxobsMtxCalc = NULL,
  distpower = 1,
  islonglat = TRUE,
  givePredAsFactor = TRUE,
  retainCoords = TRUE,
  useGearyC = FALSE,
  runParallel = TRUE,
  spatialWeightsType = "default",
  customSpatialWeights = NULL,
  spatialBandwidthProportion = 1,
  spatialBandwidth = NULL,
  asForest = FALSE
)

Arguments

minsplit

The minimum observations in a node before a split is attempted

minbucket

The minimum number of observations in a terminal node.

maxdepth

Set the maximum depth in the final tree. A root node is counted as a height of 0.

maxobsMtxCalc

Optional maximum number of observations in a node where computationally intensive matrix calculations like autocorrelation and compactness are performed.

distpower

The power of inverse distance to use when calculating spatial weights matrix.

islonglat

Are the coordinates longitude and latitude coordinates? If TRUE, then use great circle distance calculations

givePredAsFactor

In the returned autocart model, should the prediction vector also be returned as a factor?

retainCoords

After creating the autocart model, should the coordinates for each of the predictions be kept in the returned model?

useGearyC

Should autocart use Geary's C instead of Moran's I in the splitting function?

runParallel

Logical value indicating whether autocart should run using parallel processing.

spatialWeightsType

What type of spatial weighting should be used when calculating spatial autocorrelation? Options are "default" or "gaussian".

customSpatialWeights

Use this parameter to pass in an optional spatial weights matrix for use in autocorrelation calculations. Must have nrow and ncol equal to rows in training dataframe.

spatialBandwidthProportion

What percentage of the maximum pairwise distances should be considered the maximum distance for spatial influence? Cannot be simultaneously set with spatialBandwidth

spatialBandwidth

What is the maximum distance where spatial influence can be assumed? Cannot be simultaneously set with spatialBandwidthProportion.

asForest

A logical indicating if the tree should be created as a forest component with random subsetting of predictors at each node. Set this to true if you are using this tree inside an ensemble.

Value

An object passed in to the autocart function that controls the splitting.

Examples

# Load some data for an autocartControl example
snow <- na.omit(read.csv(system.file("extdata", "ut2017_snow.csv", package = "autocart")))
y <- snow$yr50[1:40]
X <- data.frame(snow$ELEVATION, snow$MCMT, snow$PPTWT)[1:40, ]
locations <- as.matrix(cbind(snow$LONGITUDE, snow$LATITUDE))[1:40, ]

# Create a control object that disallows the tree from having a depth more
# than 10 and give spatial weights only to observations that are a third of the
# distance of the longest distance between any two points in the dataset.
snow_control <- autocartControl(maxdepth = 10, spatialBandwidthProportion = 0.33)

# Pass the created control object to an autocart model
snow_model <- autocart(y, X, locations, 0.30, 0, snow_control)