computePcaNbDims: Number of dimensions for PCA

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

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

computePcaSample

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)

RclusTool documentation built on Aug. 29, 2022, 9:07 a.m.