plotVar {integrOmics} | R Documentation |
This function provides variables representation for (regularized) CCA and (sparse) PLS regression.
## S3 method for class 'rcc': plotVar(object, comp1 = 1, comp2 = 2, rad.in = 0.5, cutoff = NULL, X.label = FALSE, Y.label = FALSE, pch = NULL, cex = NULL, col = NULL, font = NULL, ...) ## S3 method for class 'pls': plotVar(object, comp1 = 1, comp2 = 2, rad.in = 0.5, X.label = FALSE, Y.label = FALSE, keep.var = FALSE, pch = NULL, cex = NULL, col = NULL, font = NULL, ...) ## S3 method for class 'spls': plotVar(object, comp1 = 1, comp2 = 2, rad.in = 0.5, X.label = FALSE, Y.label = FALSE, keep.var = FALSE, pch = NULL, cex = NULL, col = NULL, font = NULL, ...)
object |
object of class inheriting from "rcc" , "pls" or "spls" . |
comp1, comp2 |
an integer, the component that will be used on the horizontal and the vertical axis respectively to project the variables. |
rad.in |
numeric between 0 and 1, the radius of the inner circle. Defaults to 0.5 . |
cutoff |
numeric between 0 and 1. Variables with correlations below this cutoff in absolute value are not plotted (see Details). |
X.label, Y.label |
either a character vector of names for the X- and
Y-variables or FALSE for no names. If TRUE , the columns names
of the matrice are used as labels. |
col |
character or integer vector of colors for plotted character and symbols. See Details. |
pch |
plot character. A vector of single characters
or integers. See points for all alternatives. |
cex |
numeric vector of character expansion sizes for the plotted character and symbols. |
font |
numeric vector of font to be used. See par for details. |
keep.var |
boolean. If TRUE only the variables with loadings not zero are plotted
(as selected by spls ). Defaults to FALSE . |
... |
not used currently. |
plotVar
produce a "correlation circle", i.e. the correlations
between each variable and the selected components are plotted as scatter plot,
with concentric circles of radius one et radius given by rad.in
. Each point
corresponds to a variable. For (sparse) PLS regression the components correspond to
the X-variates components and for (regularized) CCA the components correspond
to the bisector vector components between X- and Y-variates.
The arguments col
, pch
, cex
and font
can be either vectors of
length two or a list with two vector components of length p and q respectively,
where p is the number of X-variables and q
is the number of Y-variables. In the first case, the first and second component of the
vector determine the graphics attributes for the X- and Y-variables respectively.
O-ther-wise, multiple arguments values can be specified so that each point (variable)
can be given its own graphic attributes. In this case, the first component of the list
correspond to the X attributs and the second component correspond to
the Y attributs. Default values exist for this arguments.
Sébastien Déjean and Ignacio González.
## variable representation for objects of class 'rcc' data(nutrimouse) X <- nutrimouse$lipid Y <- nutrimouse$gene nutri.res <- rcc(X, Y, lambda1 = 0.064, lambda2 = 0.008) plotVar(nutri.res, comp1 = 1, comp2 = 2) #(default) plotVar(nutri.res, comp1 = 1, comp2 = 2, cutoff = 0.5, X.label = TRUE, Y.label = TRUE) ## variable representation for objects of class 'pls' or '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)) plotVar(toxicity.spls, comp1 = 1, comp2 = 2, keep.var = TRUE, Y.label = TRUE)