moma_pg_settings: Algorithm settings for solving the penalized SVD problem
In DataSlingers/MoMA: MoMA - Modern Multivariate Analysis in R

Description Usage Arguments Details Value

This function is used to specify the pg_settings argument in the moma_*pca, moma_*cca, and moma_*lda family of functions.

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moma_pg_settings(..., EPS = 1e-10, MAX_ITER = 1000,
  EPS_inner = 1e-10, MAX_ITER_inner = 1e+05, solver = c("ista",
  "fista", "onestepista"))

`...`	To force users to specify arguments by names.
`EPS`	Precision for outer loop.
`MAX_ITER`	The maximum number of iterations for outer loop.
`EPS_inner`	Precision for inner loop.
`MAX_ITER_inner`	The maximum number of iterations for inner loop.
`solver`	A string in `c("ista", "fista", "onestepista")`, representing ISTA (Iterative Shrinkage-Thresholding Algorithm), FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) and One-step ISTA (an approximated version of ISTA) respectively.

To find an (approximate) solution to a penalized SVD (Singular Value Decomposition) problem is to solve two penalized regression problems iteratively (outer loop). Each penalized regression (inner loop) is solved using one of the three algorithms: ISTA (Iterative Shrinkage-Thresholding Algorithm), FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) and One-step ISTA (an approximated version of ISTA).

A moma_pg_settings object, which is a list containing the above parameters.

DataSlingers/MoMA documentation built on Oct. 30, 2019, 5:55 a.m.