mixOmics (version 5.1.2)

print: Print Methods for CCA, (s)PLS, PCA and Summary objects

Description

Produce print methods for class "rcc", "pls", "spls", "pca", "rgcca", "sgcca" and "summary".

Usage

## S3 method for class 'rcc':
print(x, \ldots)
## S3 method for class 'pls':
print(x, \ldots)
## S3 method for class 'spls':
print(x, \ldots)
## S3 method for class 'pca':
print(x, \ldots)
## S3 method for class 'spca':
print(x, \ldots)
## S3 method for class 'rgcca':
print(x, \ldots)
## S3 method for class 'sgcca':
print(x, \ldots)
## S3 method for class 'summary':
print(x, \ldots)

Arguments

x

object of class inheriting from "rcc", "pls", "spls", "pca", "spca", "rgcca", "sgcca"or "summary".

...

not used currently.

encoding

latin1

Details

print method for "rcc", "pls", "spls" "pca", "rgcca", "sgcca" class, returns a description of the x object including: the function used, the regularization parameters (if x of class "rcc"), the (s)PLS algorithm used (if x of class "pls" or "spls"), the samples size, the number of variables selected on each of the sPLS components (if x of class "spls") and the available components of the object.

print method for "summary" class, gives the (s)PLS algorithm used (if x of class "pls" or "spls"), the number of variates considered, the canonical correlations (if x of class "rcc"), the number of variables selected on each of the sPLS components (if x of class "spls") and the available components for Communalities Analysis, Redundancy Analysis and Variable Importance in the Projection (VIP).

See Also

rcc, pls, spls, vip.

Examples

## print for objects of class 'rcc'
data(nutrimouse)
X <- nutrimouse$lipid
Y <- nutrimouse$gene
nutri.res <- rcc(X, Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008)
print(nutri.res)

## print for objects of class 'summary'
more <- summary(nutri.res, cutoff = 0.65)
print(more)

## print for objects of class 'pls'
data(linnerud)
X <- linnerud$exercise
Y <- linnerud$physiological
linn.pls <- pls(X, Y)
print(linn.pls)

## print for objects of class 'spls'
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50), 
                      keepY = c(10, 10, 10))
print(toxicity.spls)