Rdimtools (version 0.3.2)

do.ica: Independent Component Analysis

Description

do.ica is an R implementation of FastICA algorithm, which aims at finding weight vectors that maximize a measure of non-Gaussianity of projected data. FastICA is initiated with pre-whitening of the data. Single and multiple component extraction are both supported. For more detailed information on ICA and FastICA algorithm, see this Wikipedia page.

Usage

do.ica(X, ndim = 2, type = "logcosh", tpar = 1, sym = FALSE,
  tol = 1e-06, redundancy = TRUE, maxiter = 100)

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

type

nonquadratic function, one of "logcosh","exp", or "poly" be chosen.

tpar

a numeric parameter for logcosh and exp parameters that should be close to 1.

sym

a logical value; FALSE for not using symmetric decorrelation, TRUE otherwise.

tol

stopping criterion for iterative update.

redundancy

a logical value; TRUE for removing NA values after prewhitening, FALSE otherwise.

maxiter

maximum number of iterations allowed.

Y

an \((n\times ndim)\) matrix whose rows are embedded observations.

trfinfo

a list containing information for out-of-sample prediction.

projection

a \((p\times ndim)\) whose columns are basis for projection.

Details

In most of ICA literature, we have $$S = X*W$$ where \(W\) is an unmixing matrix for the given data \(X\). In order to preserve consistency throughout our package, we changed the notation; \(Y\) a projected matrix for \(S\), and projection for unmixing matrix \(W\).

References

hyvarinen_independent_2001Rdimtools

Examples

# NOT RUN {
# generate data
## in order to pass CRAN pretest, n is set to be small.
X <- aux.gensamples(n=28)

## 1. use logcosh function for transformation
output1 <- do.ica(X,ndim=2,type="logcosh")

## 2. use exponential function for transformation
output2 <- do.ica(X,ndim=2,type="exp")

## 3. use polynomial function for transformation
output3 <- do.ica(X,ndim=2,type="poly")

## Visualize three different projections
par(mfrow=c(1,3))
plot(output1$Y[,1],output1$Y[,2],main="logcosh")
plot(output2$Y[,1],output2$Y[,2],main="exp")
plot(output3$Y[,1],output3$Y[,2],main="poly")

# }