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

In mixOmics: Omics Data Integration Project

Description

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

Usage

## S3 method for class 'rcc'
print(x, ...)

## S3 method for class 'pls'
print(x, ...)

## S3 method for class 'spls'
print(x, ...)

## S3 method for class 'pca'
print(x, ...)

## S3 method for class 'spca'
print(x, ...)

## S3 method for class 'rgcca'
print(x, ...)

## S3 method for class 'sgcca'
print(x, ...)

## S3 method for class 'summary'
print(x, ...)

Arguments

x	object of class inheriting from "rcc", "pls", "spls", "pca", "spca", "rgcca", "sgcca"or "summary".
...	not used currently.

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).

Author(s)

Sébastien Déjean, Ignacio González and Kim-Anh Lê Cao.

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)

Example output

Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2

Loaded mixOmics 6.3.2

Thank you for using mixOmics!

How to apply our methods: http://www.mixOmics.org for some examples.
Questions or comments: email us at mixomics[at]math.univ-toulouse.fr  
Any bugs? https://bitbucket.org/klecao/package-mixomics/issues
Cite us:  citation('mixOmics')
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 

Call:
 rcc(X = X, Y = Y, ncomp = 3, lambda1 = 0.064, lambda2 = 0.008) 

 rCCA with 3 components and regularization parameters 0.064 and 0.008 for the X and Y data. 
 You entered data X of dimensions : 40 21 
 You entered data Y of dimensions : 40 120 

 Main numerical outputs: 
 -------------------- 
 canonical correlations: see object$cor 
 loading vectors: see object$loadings 
 variates: see object$variates 
 variable names: see object$names 

Call:
 pls(X = X, Y = Y) 

 PLS with a 'regression' mode with 2 PLS components. 
 You entered data X of dimensions: 20 3 
 You entered data Y of dimensions: 20 3 

 No variable selection. 

 Main numerical outputs: 
 -------------------- 
 loading vectors: see object$loadings 
 variates: see object$variates 
 variable names: see object$names 

 Functions to visualise samples: 
 -------------------- 
 plotIndiv, plotArrow 

 Functions to visualise variables: 
 -------------------- 
 plotVar, plotLoadings, network, cim 

Call:
 spls(X = X, Y = Y, ncomp = 3, keepX = c(50, 50, 50), keepY = c(10, 10, 10)) 

 sPLS with a 'regression' mode with 3 sPLS components. 
 You entered data X of dimensions: 64 3116 
 You entered data Y of dimensions: 64 10 

 Selection of [50] [50] [50] variables on each of the sPLS components on the X data set. 
 Selection of [10] [10] [10] variables on each of the sPLS components on the Y data set. 

 Main numerical outputs: 
 -------------------- 
 loading vectors: see object$loadings 
 variates: see object$variates 
 variable names: see object$names 

 Functions to visualise samples: 
 -------------------- 
 plotIndiv, plotArrow 

 Functions to visualise variables: 
 -------------------- 
 plotVar, plotLoadings, network, cim 

mixOmics documentation built on June 1, 2018, 5:06 p.m.