Description Usage Arguments Details Value Author(s) References See Also Examples
This function provides variables representation for (regularized) CCA, (sparse) PLS regression, PCA and (sparse) Regularized generalised CCA.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  plotVar(
object,
comp = NULL,
comp.select = comp,
plot = TRUE,
var.names = NULL,
blocks = NULL,
X.label = NULL,
Y.label = NULL,
Z.label = NULL,
abline = TRUE,
col,
cex,
pch,
font,
cutoff = 0,
rad.in = 0.5,
title = "Correlation Circle Plots",
legend = FALSE,
legend.title = "Block",
style = "ggplot2",
overlap = TRUE,
axes.box = "all",
label.axes.box = "both"
)

object 
object of class inheriting from 
comp 
integer vector of length two. The components that will be used on the horizontal and the vertical axis respectively to project the variables. By default, comp=c(1,2) except when style='3d', comp=c(1:3) 
comp.select 
for the sparse versions, an input vector indicating the components on which the variables were selected. Only those selected variables are displayed. By default, comp.select=comp 
plot 
if TRUE (the default) then a plot is produced. If not, the summaries which the plots are based on are returned. 
var.names 
either a character vector of names for the variables to be
plotted, or 
blocks 
for an object of class 
X.label 
x axis titles. 
Y.label 
y axis titles. 
Z.label 
z axis titles (when style = '3d'). 
abline 
should the vertical and horizontal line through the center be
plotted? Default set to 
col 
character or integer vector of colors for plotted character and symbols, can be of length 2 (one for each data set) or of length (p+q) (i.e. the total number of variables). See Details. 
cex 
numeric vector of character expansion sizes for the plotted character and symbols, can be of length 2 (one for each data set) or of length (p+q) (i.e. the total number of variables). 
pch 
plot character. A vector of single characters or integers, can be
of length 2 (one for each data set) or of length (p+q) (i.e. the total
number of variables). See 
font 
numeric vector of font to be used, can be of length 2 (one for
each data set) or of length (p+q) (i.e. the total number of variables). See

cutoff 
numeric between 0 and 1. Variables with correlations below this cutoff in absolute value are not plotted (see Details). 
rad.in 
numeric between 0 and 1, the radius of the inner circle.
Defaults to 
title 
character indicating the title plot. 
legend 
boolean when more than 3 blocks. Can be a character vector when one or 2 blocks to customize the legend. See examples. Default is FALSE. 
legend.title 
title of the legend 
style 
argument to be set to either 
overlap 
boolean. Whether the variables should be plotted in one single figure. Default is TRUE. 
axes.box 
for style '3d', argument to be set to either 
label.axes.box 
for style '3d', argument to be set to either

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 (regularized) CCA the components
correspond to the equiangular vector between X and Yvariates.
For (sparse) PLS regression mode the components correspond to the
Xvariates. If mode is canonical, the components for X and
Y variables correspond to the X and Yvariates
respectively.
For plsda
and splsda
objects, only the X variables are
represented.
For spls
and splsda
objects, only the X and Y
variables selected on dimensions comp
are represented.
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
Xvariables and q is the number of Yvariables. In the
first case, the first and second component of the vector determine the
graphics attributes for the X and Yvariables respectively.
Otherwise, 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.
A list containing the following components:
x 
a vector of coordinates of the variables on the xaxis. 
y 
a vector of coordinates of the variables on the yaxis. 
Block 
the data block name each variable belongs to. 
names 
the name of each variable, matching their coordinates values. 
Ignacio González, Benoit Gautier, Francois Bartolo, Florian Rohart, KimAnh Lê Cao, Al J Abadi
González I., Lê Cao KA., Davis, M.J. and Déjean, S. (2012). Visualising associations between paired 'omics data sets. J. Data Mining 5:19. http://www.biodatamining.org/content/5/1/19/abstract
cim
, network
, par
and
http://www.mixOmics.org for more details.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132  ## variable representation 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)
plotVar(nutri.res) #(default)
plotVar(nutri.res, comp = c(1,3), cutoff = 0.5)
## Not run:
## 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, cex = c(1,0.8))
# with a customized legend
plotVar(toxicity.spls, legend = c("block 1", "my block 2"),
legend.title="my legend")
## variable representation for objects of class 'splsda'
# 
data(liver.toxicity)
X < liver.toxicity$gene
Y < as.factor(liver.toxicity$treatment[, 4])
ncomp < 2
keepX < rep(20, ncomp)
splsda.liver < splsda(X, Y, ncomp = ncomp, keepX = keepX)
plotVar(splsda.liver)
## variable representation for objects of class 'sgcca' (or 'rgcca')
# 
## see example in ??wrapper.sgcca
data(nutrimouse)
# need to unmap the Y factor diet
Y = unmap(nutrimouse$diet)
# set up the data as list
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
# set up the design matrix:
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
# 0,0,1,
# 1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
1,0,1,
1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
#note: the penalty parameters will need to be tuned
wrap.result.sgcca = wrapper.sgcca(X = data, design = design, penalty = c(.3,.3, 1),
ncomp = 2,
scheme = "centroid")
wrap.result.sgcca
#variables selected on component 1 for each block
selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'gene'$name
selectVar(wrap.result.sgcca, comp = 1, block = c(1,2))$'lipid'$name
#variables selected on component 2 for each block
selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'gene'$name
selectVar(wrap.result.sgcca, comp = 2, block = c(1,2))$'lipid'$name
plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2), comp.select = c(1,1),
title = c('Variables selected on component 1 only'))
plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2), comp.select = c(2,2),
title = c('Variables selected on component 2 only'))
# > this one shows the variables selected on both components
plotVar(wrap.result.sgcca, comp = c(1,2), block = c(1,2),
title = c('Variables selected on components 1 and 2'))
## variable representation for objects of class 'rgcca'
# 
data(nutrimouse)
# need to unmap Y for an unsupervised analysis, where Y is included as a data block in data
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
# with this design, all blocks are connected
design = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3,
byrow = TRUE, dimnames = list(names(data), names(data)))
nutrimouse.rgcca < wrapper.rgcca(X = data,
design = design,
tau = "optimal",
ncomp = 2,
scheme = "centroid")
plotVar(nutrimouse.rgcca, comp = c(1,2), block = c(1,2), cex = c(1.5, 1.5))
plotVar(nutrimouse.rgcca, comp = c(1,2), block = c(1,2))
# set up the data as list
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y =Y)
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
# 0,0,1,
# 1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
1,0,1,
1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
#note: the tau parameter is the regularization parameter
wrap.result.rgcca = wrapper.rgcca(X = data, design = design, tau = c(1, 1, 0),
ncomp = 2,
scheme = "centroid")
#wrap.result.rgcca
plotVar(wrap.result.rgcca, comp = c(1,2), block = c(1,2))
## End(Not run)

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