preview.pca {iSFun} | R Documentation |
Statistical description before using function ispca
Description
The function describes the basic statistical information of the data, including sample mean, sample co-variance of X and Y, the first eigenvector, eigenvalue and principal component, etc.
Usage
preview.pca(x, L, scale.x = TRUE)
Arguments
x |
list of data matrices, L datasets of explanatory variables. |
L |
numeric, number of data sets. |
scale.x |
character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE. |
Value
An 'preview.pca' object that contains the list of the following items.
x: list of data matrices, L datasets of explanatory variables with centered columns. If scale.x is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1.
eigenvalue: the estimated first eigenvalue.
eigenvector: the estimated first eigenvector.
component: the estimated first component.
meanx: list of numeric vectors, column mean of the original datasets x.
normx: list of numeric vectors, column standard deviation of the original datasets x.
See Also
See Also as ispca
.
Examples
# Load a list with 3 data sets
library(iSFun)
data("simData.pca")
x <- simData.pca$x
L <- length(x)
prev.pca <- preview.pca(x = x, L = L, scale.x = TRUE)