SCE (version 1.1.2)

SCA: Stepwise Cluster Analysis (SCA)

Description

Builds a single Stepwise Cluster Analysis (SCA) tree model that recursively partitions the data space based on Wilks' Lambda statistic.

Usage

SCA(Training_data, X, Y, Nmin, alpha = 0.05, resolution = 1000, verbose = FALSE)

Value

An S3 object of class "SCA" containing the tree model.

Arguments

Training_data

A data.frame containing the training data

X

Character vector of predictor variable names

Y

Character vector of predictant variable names

Nmin

Minimum number of samples in a leaf node

alpha

Significance level for clustering (default: 0.05)

resolution

Resolution for splitting (default: 1000)

verbose

Print progress information (default: FALSE)

See Also

SCE, predict, importance, evaluate

Examples

# \donttest{
  # Load example data
  data(Streamflow_training_10var)
  data(Streamflow_testing_10var)
  
  # Define variables
  Predictors <- c("Prcp","SRad","Tmax","Tmin","VP","smlt","swvl1","swvl2","swvl3","swvl4")
  Predictants <- c("Flow")
  
  # Build SCA model
  sca_model <- SCA(
    Training_data = Streamflow_training_10var,
    X = Predictors,
    Y = Predictants,
    Nmin = 5,
    alpha = 0.05,
    resolution = 1000
  )
  
  # Use S3 methods
  print(sca_model)
  summary(sca_model)
  sca_predictions <- predict(sca_model, Streamflow_testing_10var)
  sca_importance <- importance(sca_model)
  sca_evaluation <- evaluate(sca_model, Streamflow_testing_10var, Streamflow_training_10var)
# }