computePcaSample: Principal Components Analysis

Description

Perform Principal Components Analysis, dealing with the number of dimensions, automatically or not.

Usage

computePcaSample(
  data.sample,
  pca.nb.dims = 0,
  selected.var = NULL,
  echo = FALSE,
  prcomp.options = list(center = TRUE, scale = TRUE),
  pca.variance.cum.min = 0.95
)

Value

features list containing the results of PCA, returned by prcomp.

Arguments

data.sample: list containing features, profiles and clustering results.
pca.nb.dims: number of dimensions to keep after PCA. If pca.nb.dims=0 (default), this number is automatically computed.
selected.var: vector of features names to consider for the PCA.
echo: boolean: if FALSE (default), no description printed in the console.
prcomp.options: list of default parameters values for the function prcomp.
pca.variance.cum.min: minimal cumulative variance to retain in PCA.

Details

computePcaSample performs Principal Components Analysis, dealing with the number of dimensions, automatically or not

Examples

Run this code

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 <- computePcaSample(x, echo = TRUE)

plot(res$pca_full$x[,1], res$pca_full$x[,2], main="PCA", xlab="PC1", ylab="PC2")