simData.pca

<p>Example data for users to apply the method ispca, ispca.cv, meta.spca or spca.</p>

datasets

The implement of integrative analysis methods based on a two-part penalization, which realizes dimension reduction analysis and mining the heterogeneity and association of multiple studies with compatible designs. The software package provides the integrative analysis methods including integrative sparse principal component analysis (Fang et al., 2018), integrative sparse partial least squares (Liang et al., 2021) and integrative sparse canonical correlation analysis, as well as corresponding individual analysis and meta-analysis versions. References: (1) Fang, K., Fan, X., Zhang, Q., and Ma, S. (2018). Integrative sparse principal component analysis. Journal of Multivariate Analysis, <doi:10.1016/j.jmva.2018.02.002>. (2) Liang, W., Ma, S., Zhang, Q., and Zhu, T. (2021). Integrative sparse partial least squares. Statistics in Medicine, <doi:10.1002/sim.8900>.

Rui Ren

iSFun

Integrative Dimension Reduction Analysis for Multi-Source Data

Kuangnan Fang

Qingzhao Zhang

Shuangge Ma

simData.pca function

Format

Example data for method ispca — simData.pca

Example data for method ispca

simData.pca: Example data for method ispca

Description

Arguments

Format