qcca_p: Quadratically Regularized Canonical Correlation Analysis with...
In QRFCCA: Quadratically Regularized Functional Canonical Correlation Analysis

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.

1	qcca_p(A, A_prop, B, B_prop, Z = NULL)

`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.

CCA_chisq_test,cca,qcca

data(snp_data)
data(phe_data)
## Not run: 
#obtain the snp position
sp = as.numeric(colnames(snp_data));
fs = fpca.score(snp_data,pos=sp,gename="Gene",percentage = 0.9,nbasis=45);
rlt = qcca_p(phe_data,0.8,fs$score,0.8,Z=NULL);

## End(Not run)