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