computePcaNbDims {RclusTool} | R Documentation |
Number of dimensions for PCA
Description
Compute the number of dimensions to keep after a Principal Components Analysis, according to a threshold on the cumulative variance.
Usage
computePcaNbDims(sdev, pca.variance.cum.min = 0.9)
Arguments
sdev |
standard deviation of the principal components (returned from prcomp). |
pca.variance.cum.min |
minimal cumulative variance to retain. |
Details
computePcaNbDims computes the number of dimensions to keep after a Principal Components Analysis, according to a threshold on the cumulative variance
Value
pca.nb.dims number of dimensions kept.
See Also
Examples
dat <- rbind(matrix(rnorm(100, mean = 0, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2),
matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
tf <- tempfile()
write.table(dat, tf, sep=",", dec=".")
x <- importSample(file.features=tf)
res.pca <- computePcaSample(x)
computePcaNbDims(res.pca$pca$sdev)
[Package RclusTool version 0.91.6 Index]