| 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)