qcca: Quadratically Regularized Canonical Correlation Analysis

In QRFCCA: Quadratically Regularized Functional Canonical Correlation Analysis

Description

Conduct quadratically regularized canonical correlation analysis.

Usage

qcca(A, A_thres, B, B_thres, Z = NULL)

Arguments

A	The first input data matrix.
A_thres	The cut-off threshold for the low rank approximation of the first input data matrix.
B	The second input data matrix.
B_thres	The cut-off threshold for the low rank approximation of the second input data matrix.
Z	The potential covariates for the canonical correaltion analysis. The default value for Z is NULL.

Value

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

References

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.

See Also

CCA_chisq_test,cca,qcca_p

Examples

data(snp_data)
data(phe_data)
## Not run: 
#obtain snp position
sp = as.numeric(colnames(snp_data));
fs = fpca.score(snp_data,pos=sp,gename="Gene",percentage = 0.9,nbasis=45);
rlt = qcca(phe_data,1,fs$score,0.01,Z=NULL);

## End(Not run)

QRFCCA documentation built on Nov. 17, 2017, 7:20 a.m.