autotune: Find the best alpha, beta, and bandwidth values with k-fold...

In autocart: Autocorrelation Regression Trees

Description

Find the best alpha, beta, and bandwidth values with k-fold cross-validation

Usage

autotune(
  response,
  data,
  locations,
  k = 8,
  control = NULL,
  customGroups = NULL,
  alphaVals = NULL,
  betaVals = NULL,
  bandwidthVals = NULL,
  outputProgress = FALSE,
  useSpatialNodes = FALSE,
  spatialNodesMethod = "idw",
  spatialNodesDistPower = 2,
  spatialNodesDistPowerRange = c(0, 2),
  spatialNodesModelByResidual = FALSE
)

Arguments

response	The vector of response values to test on.
data	The data frame of predictor variables.
locations	The n by 2 matrix of coordinate information for the known observations
k	The number of folds to create in k-fold cross-validation for tuning
control	An optional control function to send to the autocart creation function
customGroups	Here, you may supply custom groups for cross-validation. This parameter must be supplied as a factor and labeled from 1:numLevels.
alphaVals	Override the alpha values that are expanded in the grid in this function.
betaVals	Override the beta values that are expanded in the grid in this function.
bandwidthVals	Override the bandwidth values that are expanded in the grid in this function.
outputProgress	Print the result of the cross-validations as you are going. This is useful when the cross-validation will be very long and you do not wish to wait.
useSpatialNodes	Use an interpolative process at the terminal nodes of the autocart tree for the prediction process
spatialNodesMethod	The type of interpolation to use at the terminal nodes
spatialNodesDistPower	The power parameter to use in inverse distance weighting at terminal nodes
spatialNodesDistPowerRange	The ranged power parameter p1, p2 to use for a varying power parameter
spatialNodesModelByResidual	Do the interpolative process on the residuals of the prediction formed by response average at terminal nodes

Value

A list of the labeled optimal parameters that were chosen for the best predictive accuracy on cross-validation.

Examples

# Load some data for an autotune example
# (Note that a low sample size is used here for quick example computation.
#  In a practical application this function can be quite computationally
#  demanding due to the grid-search nature of the function.)
snow <- na.omit(read.csv(system.file("extdata", "ut2017_snow.csv", package = "autocart")))
y <- snow$yr50[1:35]
X <- data.frame(snow$ELEVATION, snow$MCMT, snow$PPTWT)[1:35, ]
locations <- as.matrix(cbind(snow$LONGITUDE, snow$LATITUDE))[1:35, ]

# Find optimal parameters via cross-validation. We'll search through the
# following alpha/beta/bandwidth values:
alphaVec <- c(0.0, 0.5)
betaVec <- c(0.0, 0.2)
bandwidthVec <- c(1.0)

# We'll find the optimal values with 3-fold cross validation:
# (Due to the large number of cross-validations and trainings that occur,
# this can take a few minutes.)
myTune <- autotune(y, X, locations, k = 3, alphaVals = alphaVec,
                   betaVals = betaVec, bandwidthVals = bandwidthVec)
# Inspect the results
myTune

autocart documentation built on May 27, 2021, 5:07 p.m.