qcca_p: Quadratically Regularized Canonical Correlation Analysis with...

Description Usage Arguments Value References See Also Examples

View source: R/qcca_p.R

Description

Conduct quadratically regularized canonical correlation analysis by specifying the proportion of variance that the low rank approximation can explain.

Usage

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

Arguments

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.

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

A_thres

The corresponding cut-off point for A_prop

B_thres

The corresponding cut-off point for B_prop

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

Examples

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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)

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