Description Usage Arguments Value References See Also Examples
Conduct quadratically regularized canonical correlation analysis by specifying the proportion of variance that the low rank approximation can explain.
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A |
The first input data matrix. |
A_prop |
The proportion of variance that the low rank approximation can explain in matrix A. |
B |
The second input data matrix. |
B_prop |
The proportion of variance that the low rank approximation can explain in matrix B. |
Z |
The potential covariates for the canonical correaltion analysis. The default value for Z is NULL. |
The output is a list.
rho |
a numeric vector of canonical correlation coefficients |
chisq_p |
p_value between 0 and 1 by using chi-square test |
A_thres |
The corresponding cut-off point for A_prop |
B_thres |
The corresponding cut-off point for B_prop |
Lin N, Zhu Y, Fan R, Xiong M. A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data. PLOS Computational Biology. 2017;13(10):e1005788. doi: 10.1371/journal.pcbi.1005788.
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