| svd_centralize {tempted} | R Documentation |
Remove the mean structure of the temporal tensor
Description
This function first average the feature value of all time points for each subject to form a subject by feature matrix. Next, it performs a singular value decomposition of this matrix and construct the matrix's rank-r approximation. Then, it subtracts this rank-r subject by feature matrix from the temporal tensor.
Usage
svd_centralize(datlist, r = 1)
Arguments
datlist |
A length n list of matrices. Each matrix represents a subject, with columns representing samples from this subject, the first row representing the sampling time points, and the following rows representing the feature values. |
r |
The number of ranks in the mean structure. Default is 1. |
Value
A list of results.
- datlist
The new temporal tensor after mean structure is removed.
- A_tilde
The subject singular vector of the mean structure, a subject by r matrix.
- B_tilde
The feature singular vector of the mean structure, a feature by r matrix.
- lambda_tilde
The singular value of the mean structure, a length r vector.
References
Shi P, Martino C, Han R, Janssen S, Buck G, Serrano M, Owzar K, Knight R, Shenhav L, Zhang AR. (2023) Time-Informed Dimensionality Reduction for Longitudinal Microbiome Studies. bioRxiv. doi: 10.1101/550749. https://www.biorxiv.org/content/10.1101/550749.
See Also
Examples can be found in tempted.