Robust Angle Based Joint and Individual Variation Explained
Simulation of data blocks
Decomposition Heatmaps
Decomposition Heatmaps
Block Loadings
Block Scores
Computes the final JIVE decomposition.
Computes the individual matrix for a data block.
Individual Rank
Computes the individual matrix for a data block
Joint Rank
Computes the joint scores.
Estimate the wedin bound for a data matrix.
The singular value threshold.
Computes the robust SVD of a matrix Using robRsvd
Gets the wedin bounds
Robust Angle based Joint and Individual Variation Explained
Computes the robust SVD of a matrix
Proportions of variance explained
Simulation of single data block from distribution
Reconstruces the original matrix from its robust SVD.
Truncates a robust SVD.
Resampling procedure for the wedin bound
A robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.