summary {integrOmics} | R Documentation |
Produce summary
methods for class "rcc"
,
"pls"
and "spls"
.
## S3 method for class 'rcc': summary(object, what = c("all", "communalities", "redundancy"), ncomp, cutoff = NULL, digits = 4, ...) ## S3 method for class 'pls': summary(object, what = c("all", "communalities", "redundancy", "VIP"), digits = 4, keep.var = FALSE, ...) ## S3 method for class 'spls': summary(object, what = c("all", "communalities", "redundancy", "VIP"), digits = 4, keep.var = FALSE, ...)
object |
object of class inheriting from "rcc" , "pls" or "spls" . |
ncomp |
the number of components to include in the model. |
cutoff |
real between 0 and 1. Variables with all correlations components below this cutoff in absolute value are not showed (see Details). |
digits |
integer, the number of significant digits to use when printing.
Defaults to 4 . |
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. |
keep.var |
boolean. If TRUE only the variables with loadings not zero
(as selected by spls ) are showed. Defaults to FALSE . |
... |
not used currently. |
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 bisector 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.
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 . |
Sébastien Déjean Ignacio González and Kim-Anh Lę Cao.
## summary for objects of class 'rcc' data(nutrimouse) X <- nutrimouse$lipid Y <- nutrimouse$gene nutri.res <- rcc(X, Y, lambda1 = 0.064, lambda2 = 0.008) more <- summary(nutri.res, ncomp = 3, cutoff = 0.65) ## 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)