fpca.score: Functional Principal Component Scores
In QRFCCA: Quadratically Regularized Functional Canonical Correlation Analysis

Description Usage Arguments Value References See Also Examples

Calcualate the functional principal component scores

1	fpca.score(x, pos = NULL, gename, percentage, nbasis)

`x`	Input data matrix. Each row represents the observations of a single individual. Each column represents the variables.
`pos`	Number of basis function for Fourier expansion and it should be an odd number.
`gename`	The name of the gene that the snp data belongs.
`percentage`	The propotion of the variance that the functional principal component scores can explain in the functional domain.
`nbasis`	The location or time information for each variables.

The output is a list.

`score`	The calculated functional principal component scores
`prop`	The proportion of variance that the corresponding principal component scores can explain in the functional domain.
`eigen`	The calculated eigen value when calculating the functional principal component scores.

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.

fourier.expansion,fourier.expansion.smoothed

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

## End(Not run)