Represents samples from multiple coordinates.
plotArrow( object,
comp = NULL,
abline = FALSE,
xlim = NULL,
ylim = NULL,
group=NULL,
col,
cex,
pch,
title=NULL,
plot.arrows=TRUE,
legend=FALSE,
X.label = NULL,
Y.label = NULL,
ind.names=FALSE,
position.names='centroid'
)object of class inheriting from mixOmics: PLS, sPLS, rCC, rGCCA, sGCCA, sGCCDA
integer vector of length two indicating the components represented on the horizontal and the vertical axis to project the individuals.
should the vertical and horizontal line through the center be plotted? Default set to FALSE
the ranges to be encompassed by the NULL they are computed.
the ranges to be encompassed by the NULL they are computed.
factor indicating the group membership for each sample. Coded as default for the supervised method sGCCDA, sPLSDA, but needs to be input for the unsupervised methods PLS, sPLS, rCC, rGCCA, sGCCA
character (or symbol) color to be used, color vector also possible.
numeric character (or symbol) expansion, , color vector also possible.
plot character. A character string or a vector of single characters
or integers. See points for all alternatives.
set of characters for the title plot.
boolean. Whether arrows should be added or not. Default is TRUE.
boolean. Whether the legend should be added. Only for the supervised methods and if group!=NULL. Default is FALSE.
x axis titles.
y axis titles.
If TRUE, the row names of the first (or second) data matrix are used as sample names (see Details). Can be a vector of length the sample size to display sample names.
One of "centroid", "start", "end". Define where sample names are plotted when ind.names=TRUE. In a multiblock analysis, centroid and start will display similarly.
Graphical of the samples (individuals) is displayed in a superimposed manner where each sample will be
indicated using an arrow. The start of the arrow indicates the location of the sample in
For objects of class "GCCA" and if there are more than 3 blocks, the start of the arrow indicates the centroid between all data sets for a given individual and the tips of the arrows the location of that individual in each block.
Short arrows indicate a strong agreement between the matching data sets, long arrows a disagreement between the matching data sets.
L<U+00EA> Cao, K.-A., Martin, P.G.P., Robert-Granie, C. and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinformatics 10:34.
arrows, text, points and http://mixOmics.org/graphics for more details.
# NOT RUN {
## plot of individuals 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)
plotArrow(nutri.res)
# names indicate genotype
plotArrow(nutri.res,
group = nutrimouse$genotype, ind.names = nutrimouse$genotype)
# }
# NOT RUN {
plotArrow(nutri.res, group = nutrimouse$genotype,
legend = TRUE)
# }
# NOT RUN {
## plot of individuals for objects of class 'pls' or 'spls'
# ----------------------------------------------------
data(liver.toxicity)
X <- liver.toxicity$gene
Y <- liver.toxicity$clinic
toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50),
keepY = c(10, 10, 10))
#default
plotArrow(toxicity.spls)
# }
# NOT RUN {
# colors indicate time of necropsy, text is the dose
plotArrow(toxicity.spls, group = liver.toxicity$treatment[, 'Time.Group'],
ind.names = liver.toxicity$treatment[, 'Dose.Group'],
legend = TRUE)
# colors indicate time of necropsy, text is the dose, label at start of arrow
plotArrow(toxicity.spls, group = liver.toxicity$treatment[, 'Time.Group'],
ind.names = liver.toxicity$treatment[, 'Dose.Group'],
legend = TRUE, position.names = 'start')
# }
# NOT RUN {
## variable representation for objects of class 'sgcca' (or 'rgcca')
# ----------------------------------------------------
data(nutrimouse)
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
design1 = matrix(c(0,1,1,1,0,1,1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
nutrimouse.sgcca <- wrapper.sgcca(X = data,
design = design1,
penalty = c(0.3, 0.5, 1),
ncomp = 3,
scheme = "centroid")
# default style: same color for all samples
plotArrow(nutrimouse.sgcca)
# }
# NOT RUN {
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot')
# ind.names to visualise the unique individuals
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = TRUE)
# ind.names to visualise the unique individuals
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = TRUE,position.names = 'start')
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = TRUE,position.names = 'end')
# ind.names indicates the diet
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = nutrimouse$diet, position.names= 'start')
# ind.names to visualise the unique individuals, start position
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = TRUE, position.names = 'start')
# end position
plotArrow(nutrimouse.sgcca, group = nutrimouse$diet, legend =TRUE,
title = 'my plot', ind.names = TRUE, position.names = 'end')
# }
# NOT RUN {
## variable representation for objects of class 'sgccda'
# ----------------------------------------------------
# Note: the code differs from above as we use a 'supervised' GCCA analysis
data(nutrimouse)
Y = nutrimouse$diet
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid)
design1 = matrix(c(0,1,0,1), ncol = 2, nrow = 2, byrow = TRUE)
nutrimouse.sgccda1 <- wrapper.sgccda(X = data,
Y = Y,
design = design1,
ncomp = 2,
keepX = list(gene = c(10,10), lipid = c(15,15)),
scheme = "centroid")
# default colors correspond to outcome Y
plotArrow(nutrimouse.sgccda1)
# }
# NOT RUN {
# with legend and title and indiv ID
plotArrow(nutrimouse.sgccda1, legend = TRUE, title = 'my sample plot',
ind.names = TRUE, position.names = 'start')
# }
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