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.