Description Usage Arguments Value Examples
Create the object used for the controlling of the splits in the autocart model
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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
)
|
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 |
What is the maximum distance where spatial influence can be assumed? Cannot be simultaneously set with |
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. |
An object passed in to the autocart
function that controls the splitting.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # 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)
|
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