computePcaSample {RclusTool} | R Documentation |
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
)
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
Value
features list containing the results of PCA, returned by prcomp.
See Also
computeSpectralEmbeddingSample
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 <- computePcaSample(x, echo = TRUE)
plot(res$pca_full$x[,1], res$pca_full$x[,2], main="PCA", xlab="PC1", ylab="PC2")
[Package RclusTool version 0.91.6 Index]