Hi all,
I wonder what the difference is between the functions prcomp and the PCA
plotting method used in example 3 from the fastICA package. They give totally
different plots. The reason for asking is that I've earlier used prcomp, but
now I should do an ICA, and I guess I cannot compare the PCA plot from prcomp
with the ICA plot if the two PCA plots looks different?
Does anyone knows anything about this? Maybe there's a different approach
that's better?
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")
}
PC=prcomp (X, center=T, scale=T)
hcl=hclust(dist(df))
plot(PC$x[,1],PC$x[,2], main="PCA components (prcomp)")
Best regards,
Joel
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