biscale {eimpute}

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(...), ...)

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.

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.

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

################# 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)