Signal to Interference Ratio

Description

Computes the signal to interference ratio between true and estimated signals

Usage

SIR(S, S.hat)

Arguments

Details

The signal to interference ratio is measured in dB and values over 20 are thought to be good. It is scale and permutation invariant and can be seen as measuring the correlation between the matched true and estimated signals.

Value

The value of the signal to interference ratio.

Author(s)

Klaus Nordhausen

References

Eriksson, J., Karvanen, J. and Koivunen, V. (2000), Source distribution adaptive maximum likelihood estimation in ICA model, Proceedings of the second international workshop on independent component analysis and blind source separation (ICA 2000), 227–232.

See Also

amari.error, ComonGAP

Examples

S <- cbind(rt(1000, 4), rnorm(1000), runif(1000))
A <- matrix(rnorm(9), ncol = 3)
X <- S %*% t(A)

S.hat <- JADE(X, 3)$S
SIR(S, S.hat)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(JADE)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/JADE/SIR.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SIR
> ### Title: Signal to Interference Ratio
> ### Aliases: SIR
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> S <- cbind(rt(1000, 4), rnorm(1000), runif(1000))
> A <- matrix(rnorm(9), ncol = 3)
> X <- S %*% t(A)
> 
> S.hat <- JADE(X, 3)$S
> SIR(S, S.hat)
[1] 23.39866
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>