preview.cca: Statistical description before using function iscca

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

Statistical description before using function iscca

Description

The function describes the basic statistical information of the data, including sample mean, sample variance of X and Y, and the first pair of canonical vectors.

Usage

preview.cca(x, y, L, scale.x = TRUE, scale.y = TRUE)

Arguments

x	list of data matrices, L datasets of explanatory variables.
y	list of data matrices, L datasets of dependent variables.
L	numeric, number of datasets.
scale.x	character, "TRUE" or "FALSE", whether or not to scale the variables x. The default is TRUE.
scale.y	character, "TRUE" or "FALSE", whether or not to scale the variables y. The default is TRUE.

Value

An 'preview.cca' 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.

• y: list of data matrices, L datasets of dependent variables with centered columns. If scale.y is TRUE, the columns of L datasets are standardized to have mean 0 and standard deviation 1.

• loading.x: the estimated canonical vector of variables x.

• loading.y: the estimated canonical vector of variables y.

• 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.

• meany: list of numeric vectors, column mean of the original datasets y.

• normy: list of numeric vectors, column standard deviation of the original datasets y.

See Also

See Also as iscca.

Examples

# Load a list with 3 data sets
library(iSFun)
data("simData.cca")
x <- simData.cca$x
y <- simData.cca$y
L <- length(x)

prev_cca <- preview.cca(x = x, y = y, L = L, scale.x = TRUE, scale.y = TRUE)

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