CCI.pretuner: CCI tuner function for CCI test
In CCI: Computational Test for Conditional Independence

CCI.pretuner

R Documentation

CCI tuner function for CCI test

Description

The CCI.tuner function performs a grid search over parameters for a conditional independence test using machine learning model supported by CCI.test. The tuner use the caret package for tuning.

Usage

CCI.pretuner(
  formula,
  data,
  method = "rf",
  metric = "RMSE",
  validation_method = "cv",
  folds = 4,
  training_share = 0.7,
  tune_length = 4,
  random_grid = TRUE,
  samples = 35,
  poly = TRUE,
  degree = 3,
  interaction = TRUE,
  verboseIter = FALSE,
  include_explanatory = FALSE,
  verbose = FALSE,
  parallel = FALSE,
  mtry = 1:10,
  nrounds = c(100, 200, 300, 400, 500, 600, 700, 800, 900, 1000),
  eta = seq(0.01, 0.3, by = 0.05),
  max_depth = 2:6,
  gamma = c(0, 1, 2, 3),
  colsample_bytree = c(0.8, 0.9, 1),
  min_child_weight = c(1, 3),
  subsample = 1,
  sigma = seq(0.1, 2, by = 0.3),
  C = seq(0.1, 2, by = 0.5),
  ...
)

Arguments

`formula`	Model formula specifying the relationship between dependent and independent variables.
`data`	A data frame containing the variables specified in the formula.
`method`	Character. Specifies the machine learning method to use. Supported methods are random forest "rf", extreme gradient boosting "xgboost" and Support Vector Machine "svm".
`metric`	Character. The performance metric to optimize during tuning. Default is "RMSE".
`validation_method`	Character. Specifies the resampling method. Default is "cv".
`folds`	Integer. The number of folds for cross-validation during the tuning process. Default is 10.
`training_share`	Numeric. For leave-group out cross-validation: the training percentage. Default is 0.7.
`tune_length`	Integer. The number of parameter combinations to try during the tuning process. Default is 10.
`random_grid`	Logical. If TRUE, a random grid search is performed. If FALSE, a full grid search is performed. Default is TRUE.
`samples`	Integer. The number of random samples to take from the grid. Default is 30.
`poly`	Logical. If TRUE, polynomial terms of the conditional variables are included in the model. Default is TRUE.
`degree`	Integer. The degree of polynomial terms to include if poly is TRUE. Default is 3.
`interaction`	Logical. If TRUE, interaction terms of the conditional variables are included in the model. Default is TRUE.
`verboseIter`	Logical. If TRUE, the function will print the tuning process. Default is FALSE.
`include_explanatory`	Logical. If TRUE, given the condition Y \|\| X \| Z, the function will include explanatory variable X in the model for Y. Default is FALSE
`verbose`	Logical. If TRUE, the function will print the tuning process. Default is FALSE..
`parallel`	Logical. If TRUE, the function will use parallel processing. Default is TRUE.
`mtry`	Integer. The number of variables randomly sampled as candidates at each split for random forest. Default is 1:5.
`nrounds`	Integer. The number of rounds (trees) for methods such as xgboost and random forest. Default is seq(50, 200, by = 25).
`eta`	Numeric. The learning rate for xgboost. Default is seq(0.01, 0.3, by = 0.05).
`max_depth`	Integer. The maximum depth of the tree for xgboost. Default is 1:6.
`gamma`	Numeric. The minimum loss reduction required to make a further partition on a leaf node for xgboost. Default is seq(0, 5, by = 1).
`colsample_bytree`	Numeric. The subsample ratio of columns when constructing each tree for xgboost. Default is seq(0.5, 1, by = 0.1).
`min_child_weight`	Integer. The minimum sum of instance weight (hessian) needed in a child for xgboost. Default is 1:5.
`subsample`	Numeric. The subsample ratio of the training. Default is 1.
`sigma`	Numeric. The standard deviation of the Gaussian kernel for Gaussian Process Regression. Default is seq(0.1, 2, by = 0.3).
`C`	Numeric. The regularization parameter for Support Vector Machine. Default is seq(0.1, 2, by = 0.5).
`...`	Additional arguments to pass to the `CCI.tuner` function.

Value

A list containing:

best_param: A data frame with the best parameters.
tuning_result: A data frame with all tested parameter combinations and their performance metrics.
warnings: A character vector of warnings issued during tuning.

Examples

set.seed(123)
data <- data.frame(x1 = rnorm(100), x2 = rnorm(100), x3 = rnorm(100), y = rnorm(100))
# Tune random forest parameters
result <- CCI.pretuner(formula = y ~ x1 | x2 + x3,
data = data,
samples = 5,
folds = 3,
method = "rf")

CCI documentation built on Aug. 29, 2025, 5:17 p.m.