summary: Summary Methods for CCA and PLS objects

Description Usage Arguments Details Value Author(s) See Also Examples

Description

Produce summary methods for class "rcc", "pls" and "spls".

Usage

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## S3 method for class 'mixo_pls'
summary(
  object,
  what = c("all", "communalities", "redundancy", "VIP"),
  digits = 4,
  keep.var = FALSE,
  ...
)

## S3 method for class 'mixo_spls'
summary(
  object,
  what = c("all", "communalities", "redundancy", "VIP"),
  digits = 4,
  keep.var = FALSE,
  ...
)

## S3 method for class 'rcc'
summary(
  object,
  what = c("all", "communalities", "redundancy"),
  cutoff = NULL,
  digits = 4,
  ...
)

## S3 method for class 'pca'
summary(object, ...)

Arguments

object

object of class inherited from "rcc", "pls" or "spls".

what

character string or vector. Should be a subset of c("all", "summarised", "communalities", "redundancy", "VIP"). "VIP" is only available for (s)PLS. See Details.

digits

integer, the number of significant digits to use when printing. Defaults to 4.

keep.var

boolean. If TRUE only the variables with loadings not zero (as selected by spls) are showed. Defaults to FALSE.

...

not used currently.

cutoff

real between 0 and 1. Variables with all correlations components below this cut-off in absolute value are not showed (see Details).

Details

The information in the rcc, pls or spls object is summarised, it includes: the dimensions of X and Y data, the number of variates considered, the canonical correlations (if object of class "rcc") and the (s)PLS algorithm used (if object of class "pls" or "spls") and the number of variables selected on each of the sPLS components (if x of class "spls").

"communalities" in what gives Communalities Analysis. "redundancy" display Redundancy Analysis. "VIP" gives the Variable Importance in the Projection (VIP) coefficients fit by pls or spls. If what is "all", all are given.

For class "rcc", when a value to cutoff is specified, the correlations between each variable and the equiangular vector between X- and Y-variates are computed. Variables with at least one correlation componente bigger than cutoff are showed. The defaults is cutoff=NULL all the variables are given.

Value

The function summary returns a list with components:

ncomp

the number of components in the model.

cor

the canonical correlations.

cutoff

the cutoff used.

keep.var

list containing the name of the variables selected.

mode

the algoritm used in pls or spls.

Cm

list containing the communalities.

Rd

list containing the redundancy.

VIP

matrix of VIP coefficients.

what

subset of c("all", "communalities", "redundancy", "VIP").

digits

the number of significant digits to use when printing.

method

method used: rcc, pls or spls.

Author(s)

Sébastien Déjean, Ignacio González, Kim-Anh Lê Cao, Al J Abadi

See Also

rcc, pls, spls, vip.

Examples

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## summary 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)
more <- summary(nutri.res, cutoff = 0.65)

## Not run: 
## summary for objects of class 'pls'
data(linnerud)
X <- linnerud$exercise
Y <- linnerud$physiological
linn.pls <- pls(X, Y)
more <- summary(linn.pls)

## summary 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))
more <- summary(toxicity.spls, what = "redundancy", keep.var = TRUE)

## End(Not run)

mixOmics documentation built on Nov. 8, 2020, 11:12 p.m.