Package 'ARTtransfer' reference manual

Title:	Adaptive and Robust Pipeline for Transfer Learning
Description:	ART is a flexible framework for transfer learning that integrates information from auxiliary data sources to improve model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets are less informative. See the paper, Wang, Wu, and Ye <doi:10.1002/sta4.582>.
Authors:	Boxiang Wang [aut, cre], Yunan Wu [aut], Chenglong Ye [aut]
Maintainer:	Boxiang Wang <[email protected]>
License:	GPL-2
Version:	1.0.0
Built:	2024-10-25 06:40:43 UTC
Source:	https://github.com/boxiang-wang/arttransfer

ART: Adaptive and Robust Transfer Learning

Description

ART is a flexible framework for transfer learning that leverages information from auxiliary data sources to enhance model performance on primary tasks. It is designed to be robust against negative transfer by including the non-transfer model in the candidate pool, ensuring stable performance even when auxiliary datasets provide limited or no useful information. The ART framework supports both regression and classification tasks, aggregating predictions from multiple auxiliary models and the primary model using an adaptive exponential weighting mechanism to prevent negative transfer. Variable importance is also provided to indicate the contribution of each variable in the final model.

Usage

ART(
  X,
  y,
  X_aux,
  y_aux,
  X_test,
  func,
  lam = 1,
  maxit = 5000L,
  eps = 1e-06,
  type = c("regression", "classification"),
  is_coef = TRUE,
  importance = TRUE,
  ...
)
ART(
  X,
  y,
  X_aux,
  y_aux,
  X_test,
  func,
  lam = 1,
  maxit = 5000L,
  eps = 1e-06,
  type = c("regression", "classification"),
  is_coef = TRUE,
  importance = TRUE,
  ...
)

Arguments

`X`	A matrix for the primary dataset (target domain) predictors.
`y`	A vector for the primary dataset (target domain) responses.
`X_aux`	A list of matrices for the auxiliary datasets (source domains) predictors.
`y_aux`	A list of vectors for the auxiliary datasets (source domains) responses.
`X_test`	A matrix for the test dataset predictors.
`func`	A function used to fit the model on each dataset. The function must have the following signature: `func(X, y, X_val, y_val, X_test, min_prod = 1e-5, max_prod = 1-1e-5, ...)`. The function should return a list with the following elements: `dev`: The deviance (or loss) on the validation set if provided. `pred`: The predictions on the test set if `X_test` is provided. `coef` (optional): The model coefficients (only for regression models when `is_coef = TRUE`). Pre-built wrapper functions, such as `fit_lm`, `fit_logit`, `fit_glmnet_lm`, `fit_glmnet_logit`, `fit_random_forest`, `fit_gbm`, and `fit_nnet`, can be used. Users may also provide their own model-fitting functions, but the input and output structure must follow the described signature and format.
`lam`	A regularization parameter for weighting the auxiliary models. Default is 1.
`maxit`	The maximum number of iterations for the model. Default is 5000.
`eps`	A convergence threshold for stopping the iterations. Default is 1e-6.
`type`	A string specifying the task type. Options are "regression" or "classification". Default is "regression".
`is_coef`	Logical; if TRUE, coefficients from the model are returned. Default is TRUE.
`importance`	Logical; if TRUE, variable importance is calculated. Only applicable if 'is_coef' is TRUE. Default is TRUE.
`...`	Additional arguments passed to the model-fitting function.

Details

The ART function performs adaptive and robust transfer learning by iteratively combining predictions from the primary dataset and auxiliary datasets. It updates the weights of each dataset's predictions through an aggregation process, eventually yielding a final set of predictions based on weighted contributions from the source and target models.

The auxiliary datasets ('X_aux' and 'y_aux') must be provided as lists, with each element corresponding to a dataset from a different source domain.

Value

A list containing:

`pred_ART`	The predictions for the test dataset.
`coef_ART`	The coefficients of the final model, if 'is_coef' is TRUE.
`W_ART`	The final weights for each dataset (including the primary dataset).
`iter_ART`	The number of iterations performed until convergence.
`VI_ART`	The variable importance, if 'importance' is TRUE.

Examples

# Example usage
dat <- generate_data(n0=50, K=3, nk=50, K_noise=2, nk_noise=30, p=10, 
       mu_trgt=1, xi_aux=0.5, ro=0.5, err_sig=1)
fit <- ART(dat$X, dat$y, dat$X_aux, dat$y_aux, dat$X_test, func=fit_lm, lam=1, type="regression")

# Example usage
dat <- generate_data(n0=50, K=3, nk=50, K_noise=2, nk_noise=30, p=10, 
       mu_trgt=1, xi_aux=0.5, ro=0.5, err_sig=1)
fit <- ART(dat$X, dat$y, dat$X_aux, dat$y_aux, dat$X_test, func=fit_lm, lam=1, type="regression")

ART_I_AM: ART-Integrated-Aggregrating Machines

Description

'ART_I_AM' performs adaptive and robust transfer learning through the aggregration of multiple machine learning models, specifically random forests, AdaBoost, and neural networks. This method aggregates the predictions from these models across multiple auxiliary datasets and the primary dataset to enhance model performance on the primary task. Users do not need to specify the models in the function, while the framework is general and users can write their own function integrating other machine learning models.

Usage

ART_I_AM(X, y, X_aux, y_aux, X_test, lam = 1, maxit = 5000L, eps = 1e-06, ...)
ART_I_AM(X, y, X_aux, y_aux, X_test, lam = 1, maxit = 5000L, eps = 1e-06, ...)

Arguments

`X`	A matrix for the primary dataset (target domain) predictors.
`y`	A vector for the primary dataset (target domain) responses.
`X_aux`	A list of matrices for the auxiliary datasets (source domains) predictors.
`y_aux`	A list of vectors for the auxiliary datasets (source domains) responses.
`X_test`	A matrix for the test dataset predictors.
`lam`	A regularization parameter for weighting the auxiliary models. Default is 1.
`maxit`	The maximum number of iterations for the aggregation process. Default is 5000.
`eps`	A convergence threshold for stopping the iterations. Default is 1e-6.
`...`	Not used in ART_I_AM.

Details

The 'ART_I_AM' function automatically integrates three machine learning models: - Random Forest ('fit_rf') - AdaBoost ('fit_gbm') - Neural Network ('fit_nnet')

These models are applied to both the primary dataset and auxiliary datasets. The function aggregates the predictions of each model using adaptive weights determined by the exponential weighting scheme. The aggregation improves the prediction power by considering different models and data simultaneously.

Value

A list containing:

`pred_ART`	The predictions for the test dataset aggregated from the different models and datasets.
`W_ART`	The final weights for each model and dataset combination.
`iter_ART`	The number of iterations performed until convergence.

Examples

# Generate synthetic datasets for transfer learning
dat <- generate_data(n0=50, K=2, nk=50, p=5, 
                     mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, is_test=TRUE, task="classification")
## Not run: 
# Fit ART_I_AM aggregating three models (random forest, AdaBoost, and neural network)
fit <- ART_I_AM(X = dat$X, y = dat$y, X_aux = dat$X_aux, y_aux = dat$y_aux, X_test = dat$X_test)

# View the predictions and weights
fit$pred_ART
fit$W_ART

## End(Not run)

# Generate synthetic datasets for transfer learning
dat <- generate_data(n0=50, K=2, nk=50, p=5, 
                     mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, is_test=TRUE, task="classification")
## Not run: 
# Fit ART_I_AM aggregating three models (random forest, AdaBoost, and neural network)
fit <- ART_I_AM(X = dat$X, y = dat$y, X_aux = dat$X_aux, y_aux = dat$y_aux, X_test = dat$X_test)

# View the predictions and weights
fit$pred_ART
fit$W_ART

## End(Not run)

fit_gbm: Gradient Boosting Wrapper for the ARTtransfer package

Description

This function fits a gradient boosting model using 'gbm()' from the R package gbm. It returns the deviance on a validation set and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_gbm(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)
fit_gbm(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of binary responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of binary responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions. Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions. Default is '1-1e-5'.
`...`	Additional arguments passed to the 'gbm()' function.

Value

A list containing:

`dev`	The deviance (negative log-likelihood) on the validation set if provided, otherwise 'NULL'.
`pred`	The predicted probabilities on the test set if 'X_test' is provided, otherwise 'NULL'.

Examples

# Fit a gradient boosting model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_gbm(X_train, y_train, X_val, y_val, X_test)

# Fit a gradient boosting model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_gbm(X_train, y_train, X_val, y_val, X_test)

fit_glmnet_lm: Sparse Linear Regression Wrapper for the ARTtransfer package

Description

This function fits a sparse linear regression model using 'glmnet()' from the R package glmnet for regression. It returns the coefficients, deviance on a validation set, and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_glmnet_lm(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  nfolds = 5,
  ...
)
fit_glmnet_lm(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  nfolds = 5,
  ...
)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions (not used in this function but passed for compatibility). Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions (not used in this function but passed for compatibility). Default is '1-1e-5'.
`nfolds`	An integer specifying the number of folds for cross-validation. Default is 5.
`...`	Additional arguments passed to the function.

Value

A list containing:

`dev`	The mean squared error (deviance) on the validation set if provided, otherwise 'NULL'.
`pred`	The predictions on the test set if 'X_test' is provided, otherwise 'NULL'.
`coef`	The fitted coefficients of the sparse linear model.

Examples

# Fit a sparse linear model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- X_train %*% rnorm(5) + rnorm(100)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- X_val %*% rnorm(5) + rnorm(50)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_glmnet_lm(X_train, y_train, X_val, y_val, X_test)

# Fit a sparse linear model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- X_train %*% rnorm(5) + rnorm(100)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- X_val %*% rnorm(5) + rnorm(50)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_glmnet_lm(X_train, y_train, X_val, y_val, X_test)

fit_glmnet_logit: Sparse Logistic Regression Wrapper for the ARTtransfer package

Description

This function fits a sparse logistic regression model using 'glmnet()' from the R package glmnet for classification. It returns the coefficients, deviance on a validation set, and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_glmnet_logit(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  nfolds = 5,
  ...
)
fit_glmnet_logit(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  nfolds = 5,
  ...
)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of binary responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of binary responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions. Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions. Default is '1-1e-5'.
`nfolds`	An integer specifying the number of folds for cross-validation. Default is 5.
`...`	Additional arguments passed to the function.

Value

A list containing:

`dev`	The deviance (negative log-likelihood) on the validation set if provided, otherwise 'NULL'.
`pred`	The predicted probabilities on the test set if 'X_test' is provided, otherwise 'NULL'.
`coef`	The fitted coefficients of the sparse logistic model.

Examples

# Fit a sparse logistic regression model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_glmnet_logit(X_train, y_train, X_val, y_val, X_test)

# Fit a sparse logistic regression model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_glmnet_logit(X_train, y_train, X_val, y_val, X_test)

fit_lm: Linear Regression Wrapper for the ARTtransfer package

Description

This function fits a linear regression model using 'lm()' and returns the coefficients, deviance on a validation set, and predictions on a test set. It is specifically designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_lm(X, y, X_val, y_val, X_test, min_prod = 1e-05, max_prod = 1 - 1e-05, ...)
fit_lm(X, y, X_val, y_val, X_test, min_prod = 1e-05, max_prod = 1 - 1e-05, ...)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions (not used in this function but passed for compatibility). Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions (not used in this function but passed for compatibility). Default is '1-1e-5'.
`...`	Additional arguments passed to the function (currently unused).

Value

A list containing:

`dev`	The mean squared error (deviance) on the validation set if provided, otherwise 'NULL'.
`pred`	The predictions on the test set if 'X_test' is provided, otherwise 'NULL'.
`coef`	The fitted coefficients of the linear model.

Examples

# Fit a linear model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- X_train %*% rnorm(5) + rnorm(100)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- X_val %*% rnorm(5) + rnorm(50)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_lm(X_train, y_train, X_val, y_val, X_test)

# Fit a linear model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- X_train %*% rnorm(5) + rnorm(100)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- X_val %*% rnorm(5) + rnorm(50)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_lm(X_train, y_train, X_val, y_val, X_test)

fit_logit: Logistic Regression Wrapper for the ARTtransfer package

Description

This function fits a logistic regression model using 'glm()' and returns the coefficients, deviance on a validation set, and predictions on a test set. It is specifically designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_logit(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)
fit_logit(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of binary responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of binary responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions. Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions. Default is '1-1e-5'.
`...`	Additional arguments passed to the function (currently unused).

Value

A list containing:

`dev`	The deviance (negative log-likelihood) on the validation set if provided, otherwise 'NULL'.
`pred`	The predicted probabilities on the test set if 'X_test' is provided, otherwise 'NULL'.
`coef`	The fitted coefficients of the logistic model.

Examples

# Fit a logistic regression model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_logit(X_train, y_train, X_val, y_val, X_test)

# Fit a logistic regression model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_logit(X_train, y_train, X_val, y_val, X_test)

fit_nnet: Neural Network Wrapper for the ARTtransfer package

Description

This function fits a neural network model using 'nnet()' from the R package nnet. It returns the deviance on a validation set and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_nnet(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)
fit_nnet(
  X,
  y,
  X_val,
  y_val,
  X_test,
  min_prod = 1e-05,
  max_prod = 1 - 1e-05,
  ...
)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of binary responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of binary responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions. Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions. Default is '1-1e-5'.
`...`	Additional arguments passed to 'nnet()'.

Value

A list containing:

`dev`	The deviance (negative log-likelihood) on the validation set if provided, otherwise 'NULL'.
`pred`	The predicted probabilities on the test set if 'X_test' is provided, otherwise 'NULL'.

Examples

# Fit a neural network model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_nnet(X_train, y_train, X_val, y_val, X_test)

# Fit a neural network model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_nnet(X_train, y_train, X_val, y_val, X_test)

fit_rf: Random Forest Wrapper for the ARTtransfer package

Description

This function fits a random forest classification model using 'randomForest()' from the R package randomForest. It returns the deviance on a validation set and predictions on a test set. It is designed for use in the 'ART' adaptive and robust transfer learning framework.

Usage

fit_rf(X, y, X_val, y_val, X_test, min_prod = 1e-05, max_prod = 1 - 1e-05, ...)
fit_rf(X, y, X_val, y_val, X_test, min_prod = 1e-05, max_prod = 1 - 1e-05, ...)

Arguments

`X`	A matrix of predictors for the training set.
`y`	A vector of binary responses for the training set.
`X_val`	A matrix of predictors for the validation set. If 'NULL', deviance is not calculated.
`y_val`	A vector of binary responses for the validation set. If 'NULL', deviance is not calculated.
`X_test`	A matrix of predictors for the test set. If 'NULL', predictions are not generated.
`min_prod`	A numeric value indicating the minimum probability bound for predictions. Default is '1e-5'.
`max_prod`	A numeric value indicating the maximum probability bound for predictions. Default is '1-1e-5'.
`...`	Additional arguments passed to the 'randomForest()' function.

Value

A list containing:

`dev`	The deviance (negative log-likelihood) on the validation set if provided, otherwise 'NULL'.
`pred`	The predicted probabilities on the test set if 'X_test' is provided, otherwise 'NULL'.

Examples

# Fit a random forest model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_rf(X_train, y_train, X_val, y_val, X_test)

# Fit a random forest model with validation and test data
X_train <- matrix(rnorm(100 * 5), 100, 5)
y_train <- rbinom(100, 1, 0.5)
X_val <- matrix(rnorm(50 * 5), 50, 5)
y_val <- rbinom(50, 1, 0.5)
X_test <- matrix(rnorm(20 * 5), 20, 5)

fit <- fit_rf(X_train, y_train, X_val, y_val, X_test)

generate_data: Generate synthetic primary, auxiliary, and noisy datasets for transfer learning

Description

This function generates synthetic datasets for the primary task (target domain), auxiliary datasets (source domains), and noisy datasets for use in transfer learning simulations. It allows flexible input for the sizes of the auxiliary and noisy datasets, supports different covariance structures, and can optionally generate test datasets. Users can specify true coefficients or rely on random generation. The function supports generating datasets for both regression and binary classification tasks.

Usage

generate_data(
  n0,
  p,
  K,
  nk,
  is_noise = TRUE,
  K_noise = 2,
  nk_noise = 30,
  mu_trgt,
  xi_aux,
  ro,
  err_sig,
  true_beta = NULL,
  noise_beta = NULL,
  Sigma_type = "AR",
  is_test = TRUE,
  n_test = n0,
  task = "regression"
)
generate_data(
  n0,
  p,
  K,
  nk,
  is_noise = TRUE,
  K_noise = 2,
  nk_noise = 30,
  mu_trgt,
  xi_aux,
  ro,
  err_sig,
  true_beta = NULL,
  noise_beta = NULL,
  Sigma_type = "AR",
  is_test = TRUE,
  n_test = n0,
  task = "regression"
)

Arguments

`n0`	An integer specifying the number of observations in the primary dataset (target domain).
`p`	An integer specifying the dimension, namely the number of predictors. All the generated data must have the same dimension.
`K`	An integer specifying the number of auxiliary datasets (source domains).
`nk`	Either an integer specifying the number of observations in each auxiliary dataset (source domains), or a vector where each element specifies the size of the corresponding auxiliary dataset. If 'nk' is a vector, its length must match the number of auxiliary datasets ('K').
`is_noise`	Logical; if TRUE, includes noisy data. If FALSE, 'K_noise' and 'nk_noise' are ignored. Default is TRUE.
`K_noise`	An integer specifying the number of noisy auxiliary datasets. If 'K_noise = 0', noisy datasets are skipped. If 'is_noise = FALSE', this argument is not used.
`nk_noise`	Either an integer specifying the number of observations in each noisy dataset, or a vector where each element specifies the size of the corresponding noisy dataset. If 'nk_noise' is a vector, its length must match the number of noisy datasets ('K_noise').
`mu_trgt`	A numeric value specifying the mean of the true coefficients in the primary dataset.
`xi_aux`	A numeric value representing the shift applied to the true coefficients in the auxiliary datasets.
`ro`	A numeric value representing the correlation between predictors (applies to the covariance matrix).
`err_sig`	A numeric value specifying the standard deviation of the noise added to the response.
`true_beta`	A vector of true coefficients for the primary dataset. If 'NULL', it is randomly generated. Default is 'NULL'.
`noise_beta`	A vector of noise coefficients. If 'NULL', it is set to '-true_beta'. Default is 'NULL'.
`Sigma_type`	A string specifying the covariance structure for the predictors. Options are: "AR" (auto-regressive structure) or "CS" (compound symmetry structure). Default is "AR".
`is_test`	Logical; if TRUE, generates test dataset ('X_test', 'y_test'). Default is TRUE.
`n_test`	An integer specifying the number of observations in the test data. Default is n0.
`task`	A string specifying the type of task. Options are "regression" or "classification". Default is "regression".

Details

The function first generates a covariance matrix based on the specified 'Sigma_type', then creates the primary dataset ('X', 'y'), the auxiliary datasets ('X_aux', 'y_aux'), and optionally generates test datasets ('X_test', 'y_test'). The auxiliary datasets are combined with noisy datasets into 'X_aux' and 'y_aux' for transfer learning use.

If 'is_noise = FALSE', then no noisy data is generated and 'K_noise' and 'nk_noise' are ignored. If 'K_noise = 0', noisy data is skipped regardless of the value of 'is_noise'. The task can be either "regression" or "classification". In classification mode, binary response variables are generated using a logistic function.

If 'nk' or 'nk_noise' is a vector, it checks if its length matches the number of auxiliary or noisy +, respectively. If the lengths do not match, an error is returned.

Value

A list containing:

`X`	The primary dataset predictors (target domain).
`y`	The primary dataset responses (target domain).
`X_aux`	A list of matrices combining auxiliary and noisy dataset predictors.
`y_aux`	A list of vectors combining auxiliary and noisy dataset responses.
`X_test`	The test dataset predictors, if 'is_test=TRUE'.
`y_test`	The test dataset responses, if 'is_test=TRUE'.

Examples

# Example: Generate data with auxiliary, noisy, and test datasets for regression
dat_reg <- generate_data(n0=100, p=10, K=3, nk=50, is_noise=TRUE, K_noise=2, nk_noise=30, 
                         mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, 
                         is_test=TRUE, task="regression")

# Example: Generate data with auxiliary, noisy, and test datasets for classification
dat_class <- generate_data(n0=100, p=10, K=3, nk=50, is_noise=TRUE, K_noise=2, nk_noise=30, 
                           mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, 
                           is_test=TRUE, task="classification")

# Display the dimensions of the generated data
cat("Primary dataset (X):", dim(dat_reg$X), "\n")   # Should print 100 x 10 for regression
cat("Primary dataset (y):", length(dat_reg$y), "\n") # Should print length 100 for regression

# Display the dimensions of auxiliary datasets
cat("Auxiliary dataset 1 (X_aux[[1]]):", dim(dat_reg$X_aux[[1]]), "\n") # Should print 50 x 10
cat("Auxiliary dataset 2 (X_aux[[2]]):", dim(dat_reg$X_aux[[2]]), "\n") # Should print 50 x 10

# Display the dimensions of noisy datasets (if generated)
cat("Noisy dataset 1 (X_aux[[4]]):", dim(dat_reg$X_aux[[4]]), "\n") # Should print 30 x 10

# Display test data dimensions (if generated)
if (!is.null(dat_reg$X_test)) {
  cat("Test dataset (X_test):", dim(dat_reg$X_test), "\n") # Should print 100 x 10
  cat("Test dataset (y_test):", length(dat_reg$y_test), "\n") # Should print length 100
}
# Example: Generate data with auxiliary, noisy, and test datasets for regression
dat_reg <- generate_data(n0=100, p=10, K=3, nk=50, is_noise=TRUE, K_noise=2, nk_noise=30, 
                         mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, 
                         is_test=TRUE, task="regression")

# Example: Generate data with auxiliary, noisy, and test datasets for classification
dat_class <- generate_data(n0=100, p=10, K=3, nk=50, is_noise=TRUE, K_noise=2, nk_noise=30, 
                           mu_trgt=1, xi_aux=0.5, ro=0.3, err_sig=1, 
                           is_test=TRUE, task="classification")

# Display the dimensions of the generated data
cat("Primary dataset (X):", dim(dat_reg$X), "\n")   # Should print 100 x 10 for regression
cat("Primary dataset (y):", length(dat_reg$y), "\n") # Should print length 100 for regression

# Display the dimensions of auxiliary datasets
cat("Auxiliary dataset 1 (X_aux[[1]]):", dim(dat_reg$X_aux[[1]]), "\n") # Should print 50 x 10
cat("Auxiliary dataset 2 (X_aux[[2]]):", dim(dat_reg$X_aux[[2]]), "\n") # Should print 50 x 10

# Display the dimensions of noisy datasets (if generated)
cat("Noisy dataset 1 (X_aux[[4]]):", dim(dat_reg$X_aux[[4]]), "\n") # Should print 30 x 10

# Display test data dimensions (if generated)
if (!is.null(dat_reg$X_test)) {
  cat("Test dataset (X_test):", dim(dat_reg$X_test), "\n") # Should print 100 x 10
  cat("Test dataset (y_test):", length(dat_reg$y_test), "\n") # Should print length 100
}

stan: Standardize Training, Validation, and Test Datasets

Description

This function standardizes the training, validation, and test datasets by centering and scaling them using the mean and standard deviation from the training set. It ensures that the validation and test sets are transformed using the same parameters derived from the training data.

Usage

stan(train, validation = NULL, test = NULL)
stan(train, validation = NULL, test = NULL)

Arguments

`train`	A list containing the training set. The list must have a component 'X' for predictors.
`validation`	A list containing the validation set. The list must have a component 'X' for predictors. If 'NULL', the validation set is not standardized. Default is 'NULL'.
`test`	A list containing the test set. The list must have a component 'X' for predictors. If 'NULL', the test set is not standardized. Default is 'NULL'.

Value

A list with the following components:

`train`	The standardized training set, with predictors centered and scaled.
`validation`	The standardized validation set (if provided), standardized using the training set's mean and standard deviation.
`test`	The standardized test set (if provided), standardized using the training set's mean and standard deviation.

Examples

# Example usage
train_data <- list(X = matrix(rnorm(100), ncol=10))
validation_data <- list(X = matrix(rnorm(50), ncol=10))
test_data <- list(X = matrix(rnorm(50), ncol=10))

standardized <- stan(train = train_data, validation = validation_data, test = test_data)

# Example usage
train_data <- list(X = matrix(rnorm(100), ncol=10))
validation_data <- list(X = matrix(rnorm(50), ncol=10))
test_data <- list(X = matrix(rnorm(50), ncol=10))

standardized <- stan(train = train_data, validation = validation_data, test = test_data)

Package 'ARTtransfer'

Help Index

ART: Adaptive and Robust Transfer Learning

Description

Usage

Arguments

Details

Value

Examples

ART_I_AM: ART-Integrated-Aggregrating Machines

Description

Usage

Arguments

Details

Value

Examples

fit_gbm: Gradient Boosting Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_glmnet_lm: Sparse Linear Regression Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_glmnet_logit: Sparse Logistic Regression Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_lm: Linear Regression Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_logit: Logistic Regression Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_nnet: Neural Network Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

fit_rf: Random Forest Wrapper for the ARTtransfer package

Description

Usage

Arguments

Value

Examples

generate_data: Generate synthetic primary, auxiliary, and noisy datasets for transfer learning

Description

Usage

Arguments

Details

Value

Examples

stan: Standardize Training, Validation, and Test Datasets

Description

Usage

Arguments

Value

Examples