
Ok, so then the S gives the individual components, good. Thanks Tony!

But what about the principal components from the PCA plot, how are they calculated?

And are the linear mixing matrix A really the same as the loadings/weights? There must be different loadings for the PCA and ICA right?

Best regards,

Joel

> Date: Wed, 11 Nov 2009 14:29:06 -0700
> From: tplate@acm.org
> To: joel_furstenberg_hagg@hotmail.com
> CC: r-help@r-project.org
> Subject: Re: [R] Loadings and scores from fastICA?
> 
> The help for fastICA says:
> 
>      The data matrix X is considered to be a linear combination of
>      non-Gaussian (independent) components i.e. X = SA where columns of
>      S contain the independent components and A is a linear mixing
>      matrix.
> 
> The value of fastICA is a list with components "S" (the estimated source matrix) and "A" (the estimated mixing matrix).  Are these what you want?
> 
> -- Tony Plate
> 
> Joel Fürstenberg-Hägg wrote:
> > Hi all,
> > 
> >  
> > 
> > Does anyone know how to get the independent components and loadings from an Independent Component Analysis (ICA), as well as principal components and loadings from a Pricipal Component analysis (PCA) using the fastICA package? Or perhaps if there's another way to do ICAs in R?
> > 
> > 
> > Below is an example from the fastICA manual (http://cran.r-project.org/web/packages/fastICA/fastICA.pdf)
> > 
> >  
> > 
> > if(require(MASS))
> > {
> >      x <- mvrnorm(n = 1000, mu = c(0, 0), Sigma = matrix(c(10, 3, 3, 1), 2, 2))
> >      x1 <- mvrnorm(n = 1000, mu = c(-1, 2), Sigma = matrix(c(10, 3, 3, 1), 2, 2))
> >      X <- rbind(x, x1)
> >      a <- fastICA(X, 2, alg.typ = "deflation", fun = "logcosh", alpha = 1, method = "R", row.norm = FALSE, maxit = 200, tol = 0.0001, verbose = TRUE)
> >      par(mfrow = c(1, 3))
> >      plot(a$X, main = "Pre-processed data")
> >      plot(a$X%*%a$K, main = "PCA components")
> >      plot(a$S, main = "ICA components")
> > }
> > 
> >  
> > 
> > Best regards,
> > 
> >  
> > 
> > Joel
> >  		 	   		  
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