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Apply imputation to the dataset by the nuclear-norm regularization (Hastie et al. 2015) .

Usage

impute.nuc_norm(
  dataSet,
  reportImputing = FALSE,
  rank.max = NULL,
  lambda = NULL,
  thresh = 1e-05,
  maxit = 100,
  final.svd = TRUE,
  seed = 362436069
)

Arguments

dataSet

The 2d dataset of experimental values.

reportImputing

A boolean (default = FALSE) specifying whether to provide a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise. Alters the return structure.

rank.max

An integer specifying the restriction on the rank of the solution. The default is set to one less than the minimum dimension of the dataset.

lambda

A scalar specifying the nuclear-norm regularization parameter. If lambda = 0, the algorithm convergence is typically slower. The default is set to the maximum singular value obtained from the singular value decomposition (SVD) of the dataset.

thresh

A scalar (default = 1e-5) specifying the convergence threshold, measured as the relative change in the Frobenius norm between two successive estimates.

maxit

An integer (default = 100) specifying the maximum number of iterations before the convergence is reached.

final.svd

A boolean (default = TRUE) specifying whether to perform a one-step unregularized iteration at the final iteration, followed by soft-thresholding of the singular values, resulting in hard zeros.

seed

An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

Value

  • If reportImputing = FALSE, the function returns the imputed 2d dataframe.

  • If reportImputing = TRUE, the function returns a list of the imputed 2d dataframe and a shadow matrix showing which proteins by replicate were imputed.

References

Hastie T, Mazumder R, Lee JD, Zadeh R (2015). “Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares.” Journal of Machine Learning Research, 16(104), 3367—3402. http://jmlr.org/papers/v16/hastie15a.html.