R/get_plsplots.R
In kuppal2/xmsPANDA: R Package for Biomarker Discovery, Network, and Data Exploratory Analysis
Defines functions
get_plsplots
get_plsplots <-
function(X,plsres,plsvar,samplelabels,filename=NA,ncomp=5,center=TRUE,scale=TRUE,legendcex=0.5,
outloc=getwd(),col_vec=NA,sample.col.opt="default",alphacol=0.3,legendlocation="topright",
class_levels=NA,pca.cex.val=0.8,pls.ellipse=TRUE,ellipse.conf.level=0.95,main_text="PLS-DA score plots",alphabetical.order=FALSE){
result<-plsres
r1<-plsvar
pch.val=19
legendlocation="bottomleft"
samplelabels<-as.data.frame(samplelabels)
samplelabels<-as.factor(samplelabels[,1])
if(alphabetical.order==FALSE){
samplelabels=factor(samplelabels,levels=unique(samplelabels))
}
l2<-levels(as.factor(samplelabels))
col_all=topo.colors(256)
t1<-table(samplelabels)
t1=t1[which(t1>0)]
if(is.na(class_levels)==TRUE){
l1<-levels(as.factor(samplelabels))
}else{
l1<-class_levels
}
class_labels_levels<-l1
ncomp=min(dim(X)[1],dim(X)[2])
if(is.na(col_vec)==TRUE){
col_vec<-c("mediumpurple4","mediumpurple1","blueviolet","darkblue","blue","cornflowerblue","cyan4","skyblue",
"darkgreen", "seagreen1", "green","yellow","orange","pink", "coral1", "palevioletred2",
"red","saddlebrown","brown","brown3","white","darkgray","aliceblue",
"aquamarine","aquamarine3","bisque","burlywood1","lavender","khaki3","black")
}
if(sample.col.opt=="default"){
col_vec<-c("#CC0000","#AAC000","blue","mediumpurple4","mediumpurple1","blueviolet","cornflowerblue","cyan4","skyblue",
"darkgreen", "seagreen1", "green","yellow","orange","pink", "coral1", "palevioletred2",
"red","saddlebrown","brown","brown3","white","darkgray","aliceblue",
"aquamarine","aquamarine3","bisque","burlywood1","lavender","khaki3","black")
}else{
if(sample.col.opt=="topo"){
#col_vec<-topo.colors(256) #length(class_labels_levels))
#col_vec<-col_vec[seq(1,length(col_vec),)]
col_vec <- topo.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="heat"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
col_vec <- heat.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="rainbow"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
col_vec<-rainbow(length(class_labels_levels), start = 0, end = alphacol)
#col_vec <- heat.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="terrain"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
#col_vec<-rainbow(length(class_labels_levels), start = 0, end = alphacol)
col_vec <- cm.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="colorblind"){
#col_vec <-c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")
# col_vec <- c("#0072B2", "#E69F00", "#009E73", "gold1", "#56B4E9", "#D55E00", "#CC79A7","black")
if(length(class_labels_levels)<9){
col_vec <- c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7", "#E64B35FF", "grey57")
}else{
#col_vec<-colorRampPalette(brewer.pal(10, "RdBu"))(length(class_labels_levels))
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35B2", "#4DBBD5B2","#00A087B2","#3C5488B2","#F39B7FB2","#8491B4B2","#91D1C2B2","#DC0000B2","#7E6148B2",
"#374E55B2","#DF8F44B2","#00A1D5B2","#B24745B2","#79AF97B2","#6A6599B2","#80796BB2","#0073C2B2","#EFC000B2", "#868686B2","#CD534CB2","#7AA6DCB2","#003C67B2","grey57")
}
}else{
check_brewer<-grep(pattern="brewer",x=sample.col.opt)
if(length(check_brewer)>0){
sample.col.opt_temp=gsub(x=sample.col.opt,pattern="brewer.",replacement="")
col_vec <- colorRampPalette(brewer.pal(10, sample.col.opt_temp))(length(class_labels_levels))
}else{
#colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
if(sample.col.opt=="journal"){
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35FF","#3C5488FF","#F39B7FFF",
"#8491B4FF","#91D1C2FF","#DC0000FF","#B09C85FF","#5F559BFF",
"#808180FF","#20854EFF","#FFDC91FF","#B24745FF",
"#374E55FF","#8F7700FF","#5050FFFF","#6BD76BFF",
"#E64B3519","#4DBBD519","#631879E5","grey75")
if(length(class_labels_levels)<8){
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","grey75")
#col_vec2<-brewer.pal(n = 8, name = "Dark2")
}else{
if(length(class_labels_levels)<=28){
# col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7", "grey75","#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666","#1B9E77", "#7570B3", "#E7298A", "#A6761D", "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666")
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35FF","#3C5488FF","#F39B7FFF",
"#8491B4FF","#91D1C2FF","#DC0000FF","#B09C85FF","#5F559BFF",
"#808180FF","#20854EFF","#FFDC91FF","#B24745FF",
"#374E55FF","#8F7700FF","#5050FFFF","#6BD76BFF", "#8BD76BFF",
"#E64B3519","#9DBBD0FF","#631879E5","#666666","grey75")
}else{
colfunc <-colorRampPalette(c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","grey75"));col_vec<-colfunc(length(class_labels_levels))
col_vec<-col_vec[sample(col_vec)]
}
}
}else{
# if(length(sample.col.opt)==1){
# col_vec <-rep(sample.col.opt,length(class_labels_levels))
#}else{
# colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
#}
if(length(sample.col.opt)==1){
col_vec <-rep(sample.col.opt,length(class_labels_levels))
}else{
if(length(sample.col.opt)>=length(class_labels_levels)){
col_vec <-sample.col.opt
col_vec <- rep(col_vec,length(class_labels_levels))
}else{
colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
}
}
}
}
}
}
}
}
}
}
#col_vec<-col_vec[sample(1:length(col_vec),length(col_vec))]
l1<-gsub(x=l1,pattern="Class",replacement="")
dir.create(outloc,showWarnings=FALSE)
setwd(outloc)
## 1) raw data
#tiff(fname,res=300, width=2000,height=2000)
col <- rep(col_vec[1:length(t1)], t1)
#col<-rep(col_all[1:length(l1)],t1)
## Choose different size of points
cex <- rep(pca.cex.val, dim(X)[1])
## Choose the form of the points (square, circle, triangle and diamond-shaped
# pch_vec=c(3,5,7,9,12,13,2,17,21,23)
#}else{
# set.seed(999)
pch_vec<-rep(seq(0,25),2) #sample(seq(1,50),50) #c(3,5,7,9,12,13,2,17,21) # #
#}
#pch_vec <- rep(pch.val,dim(X)[1])
#pch=pch_vec
pch <- rep(pch_vec[1:length(t1)], t1)
cex <- rep(pca.cex.val, dim(X)[1])
col_per_group<-{}
pch_per_group<-{}
for(p1 in 1:length(l2)){
pch[which(samplelabels==l2[p1])]=pch_vec[p1]
col[which(samplelabels==l2[p1])]=col_vec[p1]
col_per_group<-c(col_per_group,col_vec[p1])
pch_per_group<-c(pch_per_group,pch_vec[p1])
}
#pch_vec<-seq(1,50) #c(3,5,7,9,12,13,2,17,21) #seq(1,50) #
#pch_vec <- rep(pch.val,dim(X)[1])
cex <- rep(pca.cex.val, dim(X)[1])
####savelist=ls(),file="plsdebug.Rda")
#print(plotIndiv(result, comp = c(1,2),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=FALSE, ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""),add.legend=TRUE))
#save(result,samplelabels,col_per_group,col_vec,main_text,col,cex,pch,pls.ellipse,r1,file="plsdebug.Rda")
if(result$ncomp>1){
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(1,2),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(1,2),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""))
}
if(result$ncomp>2){
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(1,3),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(1,3),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""))
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(2,3),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS2 (",r1[2],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(2,3),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS2 (",r1[2],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""))
}
}
kuppal2/xmsPANDA documentation built on May 15, 2021, 5:48 a.m.
R Package Documentation
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Defines functions get_plsplots
get_plsplots <-
function(X,plsres,plsvar,samplelabels,filename=NA,ncomp=5,center=TRUE,scale=TRUE,legendcex=0.5,
outloc=getwd(),col_vec=NA,sample.col.opt="default",alphacol=0.3,legendlocation="topright",
class_levels=NA,pca.cex.val=0.8,pls.ellipse=TRUE,ellipse.conf.level=0.95,main_text="PLS-DA score plots",alphabetical.order=FALSE){
result<-plsres
r1<-plsvar
pch.val=19
legendlocation="bottomleft"
samplelabels<-as.data.frame(samplelabels)
samplelabels<-as.factor(samplelabels[,1])
if(alphabetical.order==FALSE){
samplelabels=factor(samplelabels,levels=unique(samplelabels))
}
l2<-levels(as.factor(samplelabels))
col_all=topo.colors(256)
t1<-table(samplelabels)
t1=t1[which(t1>0)]
if(is.na(class_levels)==TRUE){
l1<-levels(as.factor(samplelabels))
}else{
l1<-class_levels
}
class_labels_levels<-l1
ncomp=min(dim(X)[1],dim(X)[2])
if(is.na(col_vec)==TRUE){
col_vec<-c("mediumpurple4","mediumpurple1","blueviolet","darkblue","blue","cornflowerblue","cyan4","skyblue",
"darkgreen", "seagreen1", "green","yellow","orange","pink", "coral1", "palevioletred2",
"red","saddlebrown","brown","brown3","white","darkgray","aliceblue",
"aquamarine","aquamarine3","bisque","burlywood1","lavender","khaki3","black")
}
if(sample.col.opt=="default"){
col_vec<-c("#CC0000","#AAC000","blue","mediumpurple4","mediumpurple1","blueviolet","cornflowerblue","cyan4","skyblue",
"darkgreen", "seagreen1", "green","yellow","orange","pink", "coral1", "palevioletred2",
"red","saddlebrown","brown","brown3","white","darkgray","aliceblue",
"aquamarine","aquamarine3","bisque","burlywood1","lavender","khaki3","black")
}else{
if(sample.col.opt=="topo"){
#col_vec<-topo.colors(256) #length(class_labels_levels))
#col_vec<-col_vec[seq(1,length(col_vec),)]
col_vec <- topo.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="heat"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
col_vec <- heat.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="rainbow"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
col_vec<-rainbow(length(class_labels_levels), start = 0, end = alphacol)
#col_vec <- heat.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="terrain"){
#col_vec<-heat.colors(256) #length(class_labels_levels))
#col_vec<-rainbow(length(class_labels_levels), start = 0, end = alphacol)
col_vec <- cm.colors(length(class_labels_levels), alpha=alphacol)
}else{
if(sample.col.opt=="colorblind"){
#col_vec <-c("#386cb0","#fdb462","#7fc97f","#ef3b2c","#662506","#a6cee3","#fb9a99","#984ea3","#ffff33")
# col_vec <- c("#0072B2", "#E69F00", "#009E73", "gold1", "#56B4E9", "#D55E00", "#CC79A7","black")
if(length(class_labels_levels)<9){
col_vec <- c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7", "#E64B35FF", "grey57")
}else{
#col_vec<-colorRampPalette(brewer.pal(10, "RdBu"))(length(class_labels_levels))
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35B2", "#4DBBD5B2","#00A087B2","#3C5488B2","#F39B7FB2","#8491B4B2","#91D1C2B2","#DC0000B2","#7E6148B2",
"#374E55B2","#DF8F44B2","#00A1D5B2","#B24745B2","#79AF97B2","#6A6599B2","#80796BB2","#0073C2B2","#EFC000B2", "#868686B2","#CD534CB2","#7AA6DCB2","#003C67B2","grey57")
}
}else{
check_brewer<-grep(pattern="brewer",x=sample.col.opt)
if(length(check_brewer)>0){
sample.col.opt_temp=gsub(x=sample.col.opt,pattern="brewer.",replacement="")
col_vec <- colorRampPalette(brewer.pal(10, sample.col.opt_temp))(length(class_labels_levels))
}else{
#colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
if(sample.col.opt=="journal"){
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35FF","#3C5488FF","#F39B7FFF",
"#8491B4FF","#91D1C2FF","#DC0000FF","#B09C85FF","#5F559BFF",
"#808180FF","#20854EFF","#FFDC91FF","#B24745FF",
"#374E55FF","#8F7700FF","#5050FFFF","#6BD76BFF",
"#E64B3519","#4DBBD519","#631879E5","grey75")
if(length(class_labels_levels)<8){
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","grey75")
#col_vec2<-brewer.pal(n = 8, name = "Dark2")
}else{
if(length(class_labels_levels)<=28){
# col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7", "grey75","#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666","#1B9E77", "#7570B3", "#E7298A", "#A6761D", "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666")
col_vec<-c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","#E64B35FF","#3C5488FF","#F39B7FFF",
"#8491B4FF","#91D1C2FF","#DC0000FF","#B09C85FF","#5F559BFF",
"#808180FF","#20854EFF","#FFDC91FF","#B24745FF",
"#374E55FF","#8F7700FF","#5050FFFF","#6BD76BFF", "#8BD76BFF",
"#E64B3519","#9DBBD0FF","#631879E5","#666666","grey75")
}else{
colfunc <-colorRampPalette(c("#0072B2", "#E69F00", "#009E73", "#56B4E9", "#D55E00", "#CC79A7","grey75"));col_vec<-colfunc(length(class_labels_levels))
col_vec<-col_vec[sample(col_vec)]
}
}
}else{
# if(length(sample.col.opt)==1){
# col_vec <-rep(sample.col.opt,length(class_labels_levels))
#}else{
# colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
#}
if(length(sample.col.opt)==1){
col_vec <-rep(sample.col.opt,length(class_labels_levels))
}else{
if(length(sample.col.opt)>=length(class_labels_levels)){
col_vec <-sample.col.opt
col_vec <- rep(col_vec,length(class_labels_levels))
}else{
colfunc <-colorRampPalette(sample.col.opt);col_vec<-colfunc(length(class_labels_levels))
}
}
}
}
}
}
}
}
}
}
#col_vec<-col_vec[sample(1:length(col_vec),length(col_vec))]
l1<-gsub(x=l1,pattern="Class",replacement="")
dir.create(outloc,showWarnings=FALSE)
setwd(outloc)
## 1) raw data
#tiff(fname,res=300, width=2000,height=2000)
col <- rep(col_vec[1:length(t1)], t1)
#col<-rep(col_all[1:length(l1)],t1)
## Choose different size of points
cex <- rep(pca.cex.val, dim(X)[1])
## Choose the form of the points (square, circle, triangle and diamond-shaped
# pch_vec=c(3,5,7,9,12,13,2,17,21,23)
#}else{
# set.seed(999)
pch_vec<-rep(seq(0,25),2) #sample(seq(1,50),50) #c(3,5,7,9,12,13,2,17,21) # #
#}
#pch_vec <- rep(pch.val,dim(X)[1])
#pch=pch_vec
pch <- rep(pch_vec[1:length(t1)], t1)
cex <- rep(pca.cex.val, dim(X)[1])
col_per_group<-{}
pch_per_group<-{}
for(p1 in 1:length(l2)){
pch[which(samplelabels==l2[p1])]=pch_vec[p1]
col[which(samplelabels==l2[p1])]=col_vec[p1]
col_per_group<-c(col_per_group,col_vec[p1])
pch_per_group<-c(pch_per_group,pch_vec[p1])
}
#pch_vec<-seq(1,50) #c(3,5,7,9,12,13,2,17,21) #seq(1,50) #
#pch_vec <- rep(pch.val,dim(X)[1])
cex <- rep(pca.cex.val, dim(X)[1])
####savelist=ls(),file="plsdebug.Rda")
#print(plotIndiv(result, comp = c(1,2),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=FALSE, ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""),add.legend=TRUE))
#save(result,samplelabels,col_per_group,col_vec,main_text,col,cex,pch,pls.ellipse,r1,file="plsdebug.Rda")
if(result$ncomp>1){
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(1,2),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(1,2),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS2 (",r1[2],"% variation)",sep=""))
}
if(result$ncomp>2){
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(1,3),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(1,3),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS1 (",r1[1],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""))
# w <- 0.1
#par(omd=c(0, 1-w, 0, 1),cex.main=0.7)
# print(plotIndiv(result, comp = c(2,3),ind.names = FALSE, group=samplelabels, cex = cex[1], pch = pch, ellipse=pls.ellipse, style="lattice",col.per.group=col_per_group,
# ellipse.level = 0.95, X.label=paste("PLS2 (",r1[2],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""),legend=TRUE,title=main_text))
plotIndiv(result,comp=c(2,3),group=as.factor(samplelabels),legend = TRUE,
ellipse=pls.ellipse,style="lattice",
title = main_text,col.per.group=col_per_group,
X.label=paste("PLS2 (",r1[2],"% variation)",sep=""),Y.label=paste("PLS3 (",r1[3],"% variation)",sep=""))
}
}
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