plotArrow {mixOmics} | R Documentation |
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 |
object of class inheriting from mixOmics: |
comp |
integer vector of length two indicating the components represented on the horizontal and the vertical axis to project the individuals. |
abline |
should the vertical and horizontal line through the center be plotted? Default set to |
xlim |
the ranges to be encompassed by the x axis, if |
ylim |
the ranges to be encompassed by the y axis, if |
group |
factor indicating the group membership for each sample. Coded as default for the supervised method |
col |
character (or symbol) color to be used, color vector also possible. |
cex |
numeric character (or symbol) expansion, , color vector also possible. |
pch |
plot character. A character string or a vector of single characters
or integers. See |
title |
set of characters for the title plot. |
plot.arrows |
boolean. Whether arrows should be added or not. Default is |
legend |
boolean. Whether the legend should be added. Only for the supervised methods and if group!=NULL. Default is |
X.label |
x axis titles. |
Y.label |
y axis titles. |
ind.names |
If |
position.names |
One of |
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 X in one plot, and the tip the location of the sample in Y in the other plot.
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.
none
Francois Bartolo, Kim-Anh Lê Cao.
Lê 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.
## plot of individuals for objects of class 'rcc' # ---------------------------------------------------- dev.off() 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) 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$clinic toxicity.spls <- spls(X, Y, ncomp = 3, keepX = c(50, 50, 50), keepY = c(10, 10, 10)) #default plotArrow(toxicity.spls) # colors indicate time of necropsy, text is the dose plotArrow(toxicity.spls, group = liver.toxicity$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$treatment[, 'Dose.Group'], legend = TRUE, position.names = 'start') ## variable representation for objects of class 'sgcca' (or 'rgcca') # ---------------------------------------------------- data(nutrimouse) Y = unmap(nutrimouse$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) 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') ## 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) # with legend and title and indiv ID plotArrow(nutrimouse.sgccda1, legend = TRUE, title = 'my sample plot', ind.names = TRUE, position.names = 'start') ## End(Not run)