biscale | R Documentation |
Standardize a matrix rows and/or columns to have zero mean or unit variance
biscale(x, thresh.sd = 1e-05, maxit.sd = 100, control = list(...), ...)
x |
an m by n matrix possibly with |
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. |
A list is returned
|
The matrix after standardization. |
|
The row mean after iterative process. |
|
The column mean after iterative process. |
|
The row standard deviation after iterative process. |
|
The column standard deviation after iterative process. |
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.
################# 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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