Current literature on matrix completion focuses primarily on independent
sampling models under which the individual observed entries are sampled
independently. Motivated by applications in genomic data integration, we
propose a new framework of structured matrix completion (SMC) to treat
structured missingness by design. Specifically, our proposed method aims
at efficient matrix recovery when a subset of the rows and columns of an
approximately low-rank matrix are observed. The main function in our
package, smc.FUN, is for recovery of the missing block A22 of an
approximately low-rank matrix A given the other blocks A11, A12, A21.
Install stable version from CRAN:
install.packages("StructureMC")Install development version from GitHub:
# install.packages("remotes")
remotes::install_github("celehs/StructureMC")Cai, T., Cai, T. T., & Zhang, A. (2015). Structured Matrix Completion with Applications to Genomic Data Integration. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2015.1021005