Usage
## S3 method for class 'default':
pca(X, ncomp = 3, center = TRUE, scale = FALSE,
comp.tol = NULL, max.iter = 500, tol = 1e-09,\dots)Arguments
X
a numeric matrix (or data frame) which provides the
data for the principal components analysis. It can contain missing values.
ncomp
integer, if data is complete ncomp decides the number of components and associated
eigenvalues to display from the pcasvd algorithm and if the data has missing values,
ncomp gives the number of components to k
center
a logical value indicating whether the variables should be shifted to be zero centered.
Alternately, a vector of length equal the number of columns of X can be supplied.
The value is passed to sca scale
a logical value indicating whether the variables should be scaled to have
unit variance before the analysis takes place. The default is FALSE for consistency with prcomp
function, but in general scaling is advisable. Al
comp.tol
a value indicating the magnitude below which components should be omitted.
max.iter
integer, the maximum number of iterations in the NIPALS algorithm.
tol
a positive real, the tolerance used in the NIPALS algorithm.