biscale: Data standardization

Description

Standardize a matrix rows and/or columns to have zero mean or unit variance

Usage

biscale(x, thresh.sd = 1e-05, maxit.sd = 100, control = list(...), ...)

Value

A list is returned

x.st: The matrix after standardization.
alpha: The row mean after iterative process.
beta: The column mean after iterative process.
tau: The row standard deviation after iterative process.
gamma: The column standard deviation after iterative process.

Arguments

x: an \(m\) by \(n\) matrix possibly with NAs.
thresh.sd: convergence threshold, measured as the relative change in the Frobenius norm between two successive estimates.
maxit.sd: maximum number of iterations.
control: a list of parameters that control details of standard procedure. See biscale.control.
...: arguments to be used to form the default control argument if it is not supplied directly.

References

Hastie, Trevor, Rahul Mazumder, Jason D. Lee, and Reza Zadeh. Matrix completion and low-rank SVD via fast alternating least squares. The Journal of Machine Learning Research 16, no. 1 (2015): 3367-3402.

Examples

Run this code

################# Quick Start #################
m <- 100
n <- 100
r <- 10
x_na <- incomplete.generator(m, n, r)

###### Standardize both mean and variance
xs <- biscale(x_na)

###### Only standardize mean ######
xs_mean <- biscale(x_na, row.mean = TRUE, col.mean = TRUE)

###### Only standardize variance ######
xs_std <- biscale(x_na, row.std = TRUE, col.std = TRUE)

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