QRFCCA (version 1.0)

qcca: Quadratically Regularized 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

# NOT RUN {
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);
# }