preview.pca: Statistical description before using function ispca

In iSFun: Integrative Dimension Reduction Analysis for Multi-Source Data

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)

iSFun documentation built on March 18, 2022, 7:41 p.m.