R: Ridge Redundancy Analysis for High-Dimensional Omics Data

# Ridge Redundancy Analysis for High-Dimensional Omics Data

## Documentation for package ‘rrda’ version 0.2.3

- DESCRIPTION file

## Help Pages

Bhat_mat_rlist | Generate a list of rank-specific Bhat matrices (the coefficient of Ridge Redundancy Analysis for each parameter lambda and nrank).
get_Bhat_comp | Compute the components of the coefficient Bhat using SVD.
get_lambda | Estimate an appropriate value for the ridge penalty (lambda).
get_rlist | Generate rank-specific matrices by combining the left and right components.
MSE_lambda_rank | Compute MSE for different ranks of the coefficient Bhat and lambda.
rdasim1 | Generate simulated data for Ridge Redundancy Analysis (RDA).
rdasim2 | Generate simulated data for Ridge Redundancy Analysis (RDA).
rrda.coef | Calculate the Bhat matrix from the return of the 'rrda.fit' function.
rrda.cv | Cross-validation for Ridge Redundancy Analysis
rrda.fit | Calculate the coefficient Bhat by Ridge Redundancy Analysis.
rrda.heatmap | Heatmap of the results of cross-validation for Bhat obtained from the 'rrda.cv' function.
rrda.plot | Plot the results of cross-validation for Bhat obtained from the 'rrda.cv' function.
rrda.predict | Calculate the predicted matrix Yhat using the coefficient Bhat obtained from the 'rrda.fit' function.
rrda.summary | Summarize the results of cross-validation for the coefficient Bhat obtained from the 'rrda.cv' function.
rrda.top | Top feature interactions visualization with rank and lambda penalty
sqrt_inv_d2_lambda | Compute the square root of the inverse of (d^2 + lambda).
unbiased_scale | Scale a matrix using unbiased estimators for the mean and standard deviation.
unscale_matrices | Unscale a matrix based on provided mean and standard deviation values.
unscale_nested_matrices_map | Apply unscaling to a nested list of matrices using specified mean and standard deviation values.
Yhat_mat_rlist | Generate a list of rank-specific Yhat matrices.