ngca {fmri}R Documentation

Non-Gaussian Component Analysis

Description

The function performs Non-Gaussian Component Analysis as described in Blanchard et.al. (2005).

Usage

ngca(data, L = 1000, T = 10, m = 3, eps = 1.5, npca=min(dim(x)[2],dim(x)[1]),method="spatial",sweepmean=NULL,keepv=FALSE)

Arguments

data Observation matrix (dimension Nxd)
L Number basis functions in each of four classes.
T Number of Fast ICA iterations
m Number of non-Gaussian components.
eps Threshold (defaults to 1.5)
npca Reduce space to npca principal components. This can be used to avoid standardizing by numerically singular covariance matrices. In fMRI this allows to reduce the dimensionality assuming that the interesting non-Gaussian directions are also characterized by larger variances.
method Either "spatial" or "temporal". Specifies the type of NGCA to perform.
sweepmean either NULL, "none", "global", "spatial" or "spatial". If sweepmean==NULL the value used is determined by method.
keepv if TRUE intermediate results from fast ICA step are kept.

Details

The function performs Non-Gaussian Component Analysis as described in Blanchard et.al. (2006). The procedure uses four classes of basis functions, i.e. Gauss-Power3, Hyperbolic Tangent and the real and complex part of the Fourier class. See Blanchard et.al. (2005) for details.

Value

The function returns a list with components

ihat Matrix containing the first m NGCA directions as columns.
sdev Standard deviations of the principal components of the thresholded ICA directions
xhat first m components of the rotated data
v If keepv==TRUE the set of directions v^{(k)}
normv If keepv==TRUE the norm of each v^{(k)}.

...

Author(s)

J"org Polzehl polzehl@wias-berlin.de

References

Blanchard, G., Kawanabe, M., Sugiyama, M., Spokoiny, V. and M"uller K.-R. (2005). In Search of Non-Gaussian Components of a High-Dimensional Distribution. Journal of Machine Learning Research. pp. 1-48.


[Package fmri version 1.2-6 Index]