RegularizedSCA (version 0.5.4)

pca_gca: PCA-GCA method for selecting the number of common and distinctive components.

Description

Use PCA-GCA method to identify the number of common and distinctive components.

Usage

pca_gca(DATA, Jk, cor_min, return_scores)

Arguments

DATA

A concatenated data matrix with the same number of rows.

Jk

A vector containing number of variables in the concatinated data matrix. Please see the example below.

cor_min

The minimum correlation between two components. The default value is .7; thus, it means that if the correlation between the two component is at least .7, then these two components are regarded as forming a single common component.

return_scores

If TRUE, then the function will return the component scores for each block for further analysis.

Value

It prints out the number of components of each block and the number of common components. It also returns the component scores for each block for further analysis, if return_scores = TRUE.

References

Tenenhaus, A., & Tenenhaus, M. (2011). Regularized generalized canonical correlation analysis. Psychometrika, 76(2), 257-284.

Smilde, A.K., Mage, I., Naes, T., Hankemeier, T., Lips, M.A., Kiers, H.A., Acar, E., & Bro, R. (2016). Common and distinct components in data fusion. arXiv preprint arXiv:1607.02328.

Examples

# NOT RUN {
DATA1 <- matrix(rnorm(50), nrow=5)
DATA2 <- matrix(rnorm(100), nrow=5)
DATA <- cbind(DATA1, DATA2)
R <- 5
Jk <- c(10, 20) 
pca_gca(DATA, Jk, cor_min = .8)
# }