RobRSVD-package: The Robust Regularized Singular Value Decomposition Package

Description Details Author(s) References See Also Examples

Description

This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.

Details

Package: RobRSVD
Type: Package
Version: 1.0
Date: 2013-12-15
License: GPL

The most important function in this package is RobRSVD

Author(s)

Authors are Lingsong Zhang (lingsong@purdue.edu) and Chao Pan (panc@purdue.edu) Maintainer: Lingsong Zhang <lingsong@purdue.edu>

References

Zhang, L., Shen, H., & Huang, J. Z. (2013). Robust regularized singular value decomposition with application to mortality data. The Annals of Applied Statistics, 7(3), 1540-1561.

See Also

See also in svd3dplot

Examples

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#generate a simulated data set, which is provided in Zhang et al (2013) AoAS paper.
u0<-log(10/9)*10^seq(0, 1, length=100)
v0<-sin(2*pi*seq(0, 1, length=100))/(1+1/pi)
s0<-773
data0<-s0*u0 %*% t(v0)
data<-data0+matrix(rnorm(10000, sd=20), nrow=100)
data[ceiling(10000*runif(50))]<-max(data0)+max(data0)*runif(50)
#the above provides random outlying cell simulation

#the svd calculation
data.svd<-RobRSVD(data, irobust=FALSE, uspar=0, vspar=0)

#the robsvd calculation
data.robsvd<-RobRSVD(data, irobust=TRUE, uspar=0, vspar=0)

#the ssvd calculation
data.ssvd<-RobRSVD(data, irobust=FALSE, iugcv=TRUE, ivgcv=TRUE)

#the robrsvd calculation
data.robrsvd<-RobRSVD(data, irobust=TRUE, iugcv=TRUE, ivgcv=TRUE)

#compare v's
plot(data.svd$v, type='l', ylab='V')
lines(data.robrsvd$v, col=2)
lines(data.ssvd$v, col=6)
lines(data.robsvd$v, col=4)

#compare u's
plot(data.svd$u, type='l', ylab='U')
lines(data.robrsvd$u, col=2)
lines(data.ssvd$u, col=6)
lines(data.robsvd$u, col=4)

#compare approximation matrices
#app.svd=data.svd$s * data.svd$u %*% t(data.svd$v)
#app.ssvd=data.ssvd$s * data.ssvd$u %*% t(data.ssvd$v)
#app.robsvd=data.robsvd$s * data.robsvd$u %*% t(data.robsvd$v)
#app.robrsvd=data.robrsvd$s * data.robrsvd$u %*% t(data.robrsvd$v)

#par(mfrow=c(2, 2))
#persp(app.svd, main='SVD', theta=-45, phi=40, xlab='', ylab='', zlab='')
#persp(app.ssvd, main='Regularized SVD', theta=-45, phi=40, xlab='', ylab='', zlab='');
#persp(app.robsvd, main='Robust SVD', theta=-45, phi=40, xlab='', ylab='', zlab='');
#persp(app.robrsvd, main='RobRSVD', theta=-45, phi=40, xlab='', ylab='', zlab='');
#dev.off()

RobRSVD documentation built on May 19, 2017, 11:07 p.m.

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