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inst/doc/soplsda.R
In rchemo: Dimension Reduction, Regression and Discrimination for Chemometrics
## ----setup, class.source='fold-hide', warning=FALSE---------------------------
#install.packages("knitr")
library(knitr)
opts_chunk$set(echo = TRUE)
# To ensure reproducibility
set.seed(12)
## ----packages_installation, eval=FALSE, class.source='fold-hide'--------------
# pkgs <- c("rchemo")
# sapply(pkgs, function(x) {
# if (!requireNamespace(x, quietly = TRUE)) {
# install.packages(x)
# }
# })
## ----packages_load, warning=FALSE, message=FALSE, class.source='fold-hide'----
library(rchemo) # to load rchemo
## ----import_Zhang2023---------------------------------------------------------
data(Zhang2023, package = "rchemo")
## ----check_dims, class.source='fold-hide'-------------------------------------
# Check dimension
BlockNames <- names(Zhang2023)
nbrBlocs <- length(BlockNames)
dims <- lapply(X=Zhang2023[BlockNames], FUN=dim)
names(dims) <- BlockNames
dims
# Remove unuseful object for the next steps
rm(nbrBlocs, dims)
## ----check_orders_and_names, class.source='fold-hide'-------------------------
# Check rows names in any order
row_names <- lapply(X=Zhang2023[BlockNames], FUN=rownames)
rns <- do.call(cbind, row_names)
rns.unique <- apply(rns, 1, function(x) length(unique(x)))
if (max(rns.unique) > 1) {
stop("Rows names are not identical between blocks.")
}
# Check order of samples
check_row_names <- all(sapply(X=row_names, FUN=identical, y = row_names[[1]]))
if (!check_row_names && max(rns.unique) == 1) {
print("Rows names are not in the same order for all blocks.")
}
# Remove unuseful object for the next steps
rm(row_names, rns, rns.unique, check_row_names)
## ----change_data_format-------------------------------------------------------
GCTOF <- Zhang2023$GCTOF
HILICNEG <- Zhang2023$HILICNEG
sample_metadata <- Zhang2023$metadata
rm(Zhang2023)
## ----PCA_separate_datablocks, class.source='fold-hide'------------------------
# GCTOF with unit variance scaling
pcaGCTOF <- pcanipalsna(X = scale(GCTOF[,1:dim(GCTOF)[2]]), nlv = nrow(GCTOF),
gs = TRUE,
tol = .Machine$double.eps^0.5, maxit = 200)
## diagram of explained variance
barplot(summary(pcaGCTOF,X = scale(GCTOF[,1:dim(GCTOF)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(summary(pcaGCTOF,X = scale(GCTOF[,1:dim(GCTOF)[2]]))$explvar), main = "diagram of explained variance - PCA GCTOF")
## score plot
plotxy(X= pcaGCTOF$T, group = sample_metadata$Gender,
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - PCA GCTOF", ncol = 1,
pch=16)
## loading plot
plotxy(X= pcaGCTOF$P, group = NULL,
asp = 0, col = NULL, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = TRUE,
legend = FALSE, main = "loadings - PCA GCTOF", ncol = 1,
cex=0.8)
#HILICNEG with unit variance scaling
pcaHILICNEG <- pcanipalsna(X = scale(HILICNEG[,1:dim(HILICNEG)[2]]), nlv = nrow(HILICNEG),
gs = TRUE,
tol = .Machine$double.eps^0.5, maxit = 200)
## diagram of explained variance
barplot(summary(pcaHILICNEG,X = scale(HILICNEG[,1:dim(HILICNEG)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(summary(pcaHILICNEG,X = scale(HILICNEG[,1:dim(HILICNEG)[2]]))$explvar), main = "diagram of explained variance - PCA HILIC NEG")
## score plot
plotxy(X= pcaHILICNEG$T, group = sample_metadata$Gender,
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - PCA HILIC NEG", ncol = 1,
pch=16)
## loading plot
plotxy(X= pcaHILICNEG$P, group = NULL,
asp = 0, col = NULL, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = TRUE,
legend = FALSE, main = "loadings - PCA HILIC NEG", ncol = 1,
cex=0.8)
# Remove unuseful object for the next steps
rm(pcaGCTOF, pcaHILICNEG)
## ----run_nlvtest--------------------------------------------------------------
nlvtestsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[1],
# nlv = c(),
nlvlist = list(0:3, 0:3),
# modeloutput = c("scores","loadings","coef","vip"),
cvmethod = c("kfolds","loo")[1],
nbrep = 10,
seed = 123,
samplingk = NULL,
nfolds = 3,
# npermut = 30,
optimisation = c("global","sequential")[1],
criterion = c("err","rmse")[1],
selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
# to plot the results of the cross-validation
plot(nlvtestsoplsda[,"nlvsum"],nlvtestsoplsda[,"mean"], type = "n", xlab = "nlv sum", ylab = "classification error rate",
main = "error rates obtained from the different numbers \n of latent variable combinaisons of the 2 datablocks")
text(x=nlvtestsoplsda[,"nlvsum"], y=nlvtestsoplsda[,"mean"],
labels=paste0(nlvtestsoplsda[,"Xlist1"],",",nlvtestsoplsda[,"Xlist2"]))
nlvoptsoplsda <- nlvtestsoplsda[which(nlvtestsoplsda[,"optimum"]==1),c("Xlist1","Xlist2")] # to obtain the optimal number of LV.
# Remove unuseful object for the next steps
rm(nlvtestsoplsda)
## ----run_permutation----------------------------------------------------------
permutsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[2],
nlv = nlvoptsoplsda,
#nlvlist = list(),
#modeloutput = c("scores","loadings","coef","vip"),
cvmethod = c("kfolds","loo")[1],
nbrep = 10,
seed = 123,
samplingk = NULL,
nfolds = 3,
npermut = 10,
# optimisation = c("global","sequential")[1],
criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
#plot of the results
plot(permutsoplsda, pch = 16, ylab = "CV classification error rate", xlab = "dyssimilarity Y-Ypermuted")
# Remove unuseful object for the next steps
rm(permutsoplsda)
## ----run_model----------------------------------------------------------------
modelsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[3],
nlv = nlvoptsoplsda,
#nlvlist = list(),
modeloutput = c("scores","loadings","coef","vip"),
# cvmethod = c("kfolds","loo")[1],
# nbrep = 30,
# seed = 123,
# samplingk = NULL,
# nfolds = 5,
# npermut = 30,
#
# criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
# score plot
plotxy(do.call("cbind",modelsoplsda$scores)[,1:2], group = sample_metadata[,"Gender"],
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - SO PLS DA \n first model (GCTOF variables)", ncol = 1,
pch=16,
xlab="lv1 GCTOF",
ylab="lv2 GCTOF")
plotxy(do.call("cbind",modelsoplsda$scores)[,3:4], group = sample_metadata[,"Gender"],
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - SO PLS DA \n 2nd model (HILIC NEG variables)", ncol = 1,
pch=16,
xlab="lv1 HILICNEG",
ylab="lv2 HILICNEG")
# loading plot
plotxy(modelsoplsda$loadings[[1]], pch = 16, xlab="lv1 GCTOF", ylab="lv2 GCTOF",
main = "loadings - SO PLS DA \n first model (GCTOF variables)")
plotxy(modelsoplsda$loadings[[2]], pch = 16, xlab="lv1 HILIC NEG", ylab="lv2 HILIC NEG",
main = "loadings - SO PLS DA \n 2nd model (HILIC NEG variables)")
# vip curve
plot(modelsoplsda$vip[[1]][order(modelsoplsda$vip[[1]][,2], decreasing = TRUE),1,drop=FALSE], pch = 16, ylab = "VIP", xlab="GCTOF variables", main = "VIP curve - SO PLS DA \n first model (GCTOF variables)")
plot(modelsoplsda$vip[[2]][order(modelsoplsda$vip[[2]][,2], decreasing = TRUE),1,drop=FALSE], pch = 16, ylab = "VIP", xlab="HILIC NEG variables", main = "VIP curve - SO PLS DA \n 2nd model (HILIC NEG variables)")
# Remove unuseful object for the next steps
rm(modelsoplsda)
## ----run_prediction-----------------------------------------------------------
predsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1],
weights = NULL,
newXlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
newXnames = c("GCTOF", "HILICNEG"),
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[4],
nlv = nlvoptsoplsda,
# nlvlist = list(),
# modeloutput = c("scores","loadings","coef","vip"),
#
# cvmethod = c("kfolds","loo")[1],
# nbrep = 30,
# seed = 123,
# samplingk = NULL,
# nfolds = 5,
# npermut = 30,
#
# criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
predsoplsda
# Remove unuseful object for the next steps
rm(predsoplsda, nlvoptsoplsda)
## ----reproducibility----------------------------------------------------------
sessionInfo()
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rchemo documentation built on June 30, 2026, 5:10 p.m.
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## ----setup, class.source='fold-hide', warning=FALSE---------------------------
#install.packages("knitr")
library(knitr)
opts_chunk$set(echo = TRUE)
# To ensure reproducibility
set.seed(12)
## ----packages_installation, eval=FALSE, class.source='fold-hide'--------------
# pkgs <- c("rchemo")
# sapply(pkgs, function(x) {
# if (!requireNamespace(x, quietly = TRUE)) {
# install.packages(x)
# }
# })
## ----packages_load, warning=FALSE, message=FALSE, class.source='fold-hide'----
library(rchemo) # to load rchemo
## ----import_Zhang2023---------------------------------------------------------
data(Zhang2023, package = "rchemo")
## ----check_dims, class.source='fold-hide'-------------------------------------
# Check dimension
BlockNames <- names(Zhang2023)
nbrBlocs <- length(BlockNames)
dims <- lapply(X=Zhang2023[BlockNames], FUN=dim)
names(dims) <- BlockNames
dims
# Remove unuseful object for the next steps
rm(nbrBlocs, dims)
## ----check_orders_and_names, class.source='fold-hide'-------------------------
# Check rows names in any order
row_names <- lapply(X=Zhang2023[BlockNames], FUN=rownames)
rns <- do.call(cbind, row_names)
rns.unique <- apply(rns, 1, function(x) length(unique(x)))
if (max(rns.unique) > 1) {
stop("Rows names are not identical between blocks.")
}
# Check order of samples
check_row_names <- all(sapply(X=row_names, FUN=identical, y = row_names[[1]]))
if (!check_row_names && max(rns.unique) == 1) {
print("Rows names are not in the same order for all blocks.")
}
# Remove unuseful object for the next steps
rm(row_names, rns, rns.unique, check_row_names)
## ----change_data_format-------------------------------------------------------
GCTOF <- Zhang2023$GCTOF
HILICNEG <- Zhang2023$HILICNEG
sample_metadata <- Zhang2023$metadata
rm(Zhang2023)
## ----PCA_separate_datablocks, class.source='fold-hide'------------------------
# GCTOF with unit variance scaling
pcaGCTOF <- pcanipalsna(X = scale(GCTOF[,1:dim(GCTOF)[2]]), nlv = nrow(GCTOF),
gs = TRUE,
tol = .Machine$double.eps^0.5, maxit = 200)
## diagram of explained variance
barplot(summary(pcaGCTOF,X = scale(GCTOF[,1:dim(GCTOF)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(summary(pcaGCTOF,X = scale(GCTOF[,1:dim(GCTOF)[2]]))$explvar), main = "diagram of explained variance - PCA GCTOF")
## score plot
plotxy(X= pcaGCTOF$T, group = sample_metadata$Gender,
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - PCA GCTOF", ncol = 1,
pch=16)
## loading plot
plotxy(X= pcaGCTOF$P, group = NULL,
asp = 0, col = NULL, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = TRUE,
legend = FALSE, main = "loadings - PCA GCTOF", ncol = 1,
cex=0.8)
#HILICNEG with unit variance scaling
pcaHILICNEG <- pcanipalsna(X = scale(HILICNEG[,1:dim(HILICNEG)[2]]), nlv = nrow(HILICNEG),
gs = TRUE,
tol = .Machine$double.eps^0.5, maxit = 200)
## diagram of explained variance
barplot(summary(pcaHILICNEG,X = scale(HILICNEG[,1:dim(HILICNEG)[2]]))$explvar$pvar * 100, names.arg = 1:nrow(summary(pcaHILICNEG,X = scale(HILICNEG[,1:dim(HILICNEG)[2]]))$explvar), main = "diagram of explained variance - PCA HILIC NEG")
## score plot
plotxy(X= pcaHILICNEG$T, group = sample_metadata$Gender,
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - PCA HILIC NEG", ncol = 1,
pch=16)
## loading plot
plotxy(X= pcaHILICNEG$P, group = NULL,
asp = 0, col = NULL, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = TRUE,
legend = FALSE, main = "loadings - PCA HILIC NEG", ncol = 1,
cex=0.8)
# Remove unuseful object for the next steps
rm(pcaGCTOF, pcaHILICNEG)
## ----run_nlvtest--------------------------------------------------------------
nlvtestsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[1],
# nlv = c(),
nlvlist = list(0:3, 0:3),
# modeloutput = c("scores","loadings","coef","vip"),
cvmethod = c("kfolds","loo")[1],
nbrep = 10,
seed = 123,
samplingk = NULL,
nfolds = 3,
# npermut = 30,
optimisation = c("global","sequential")[1],
criterion = c("err","rmse")[1],
selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
# to plot the results of the cross-validation
plot(nlvtestsoplsda[,"nlvsum"],nlvtestsoplsda[,"mean"], type = "n", xlab = "nlv sum", ylab = "classification error rate",
main = "error rates obtained from the different numbers \n of latent variable combinaisons of the 2 datablocks")
text(x=nlvtestsoplsda[,"nlvsum"], y=nlvtestsoplsda[,"mean"],
labels=paste0(nlvtestsoplsda[,"Xlist1"],",",nlvtestsoplsda[,"Xlist2"]))
nlvoptsoplsda <- nlvtestsoplsda[which(nlvtestsoplsda[,"optimum"]==1),c("Xlist1","Xlist2")] # to obtain the optimal number of LV.
# Remove unuseful object for the next steps
rm(nlvtestsoplsda)
## ----run_permutation----------------------------------------------------------
permutsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[2],
nlv = nlvoptsoplsda,
#nlvlist = list(),
#modeloutput = c("scores","loadings","coef","vip"),
cvmethod = c("kfolds","loo")[1],
nbrep = 10,
seed = 123,
samplingk = NULL,
nfolds = 3,
npermut = 10,
# optimisation = c("global","sequential")[1],
criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
#plot of the results
plot(permutsoplsda, pch = 16, ylab = "CV classification error rate", xlab = "dyssimilarity Y-Ypermuted")
# Remove unuseful object for the next steps
rm(permutsoplsda)
## ----run_model----------------------------------------------------------------
modelsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1], weights = NULL,
#newXlist = NULL, newXnames = NULL,
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[3],
nlv = nlvoptsoplsda,
#nlvlist = list(),
modeloutput = c("scores","loadings","coef","vip"),
# cvmethod = c("kfolds","loo")[1],
# nbrep = 30,
# seed = 123,
# samplingk = NULL,
# nfolds = 5,
# npermut = 30,
#
# criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
# score plot
plotxy(do.call("cbind",modelsoplsda$scores)[,1:2], group = sample_metadata[,"Gender"],
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - SO PLS DA \n first model (GCTOF variables)", ncol = 1,
pch=16,
xlab="lv1 GCTOF",
ylab="lv2 GCTOF")
plotxy(do.call("cbind",modelsoplsda$scores)[,3:4], group = sample_metadata[,"Gender"],
asp = 0, col = 3:4, alpha.f = .8,
zeroes = TRUE, circle = FALSE, ellipse = FALSE,
labels = FALSE,
legend = TRUE, main = "scores - SO PLS DA \n 2nd model (HILIC NEG variables)", ncol = 1,
pch=16,
xlab="lv1 HILICNEG",
ylab="lv2 HILICNEG")
# loading plot
plotxy(modelsoplsda$loadings[[1]], pch = 16, xlab="lv1 GCTOF", ylab="lv2 GCTOF",
main = "loadings - SO PLS DA \n first model (GCTOF variables)")
plotxy(modelsoplsda$loadings[[2]], pch = 16, xlab="lv1 HILIC NEG", ylab="lv2 HILIC NEG",
main = "loadings - SO PLS DA \n 2nd model (HILIC NEG variables)")
# vip curve
plot(modelsoplsda$vip[[1]][order(modelsoplsda$vip[[1]][,2], decreasing = TRUE),1,drop=FALSE], pch = 16, ylab = "VIP", xlab="GCTOF variables", main = "VIP curve - SO PLS DA \n first model (GCTOF variables)")
plot(modelsoplsda$vip[[2]][order(modelsoplsda$vip[[2]][,2], decreasing = TRUE),1,drop=FALSE], pch = 16, ylab = "VIP", xlab="HILIC NEG variables", main = "VIP curve - SO PLS DA \n 2nd model (HILIC NEG variables)")
# Remove unuseful object for the next steps
rm(modelsoplsda)
## ----run_prediction-----------------------------------------------------------
predsoplsda <- soplsr_soplsda_allsteps(Xlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
Xnames = c("GCTOF", "HILICNEG"),
Xscaling = c("none","pareto","sd")[3],
Y = sample_metadata[,"Gender",drop=FALSE],
Yscaling = c("none","pareto","sd")[1],
weights = NULL,
newXlist = list(GCTOF = GCTOF[,1:dim(GCTOF)[2]],
HILICNEG = HILICNEG[,1:dim(HILICNEG)[2]]),
newXnames = c("GCTOF", "HILICNEG"),
method = c("soplsrda","soplslda","soplsqda")[2],
prior = c("unif", "prop")[1],
step = c("nlvtest","permutation","model","prediction")[4],
nlv = nlvoptsoplsda,
# nlvlist = list(),
# modeloutput = c("scores","loadings","coef","vip"),
#
# cvmethod = c("kfolds","loo")[1],
# nbrep = 30,
# seed = 123,
# samplingk = NULL,
# nfolds = 5,
# npermut = 30,
#
# criterion = c("err","rmse")[1],
# selection = c("localmin","globalmin","1std")[1],
#
#import = c("R","ChemFlow","W4M")[1],
outputfilename = NULL)
predsoplsda
# Remove unuseful object for the next steps
rm(predsoplsda, nlvoptsoplsda)
## ----reproducibility----------------------------------------------------------
sessionInfo()
Try the rchemo package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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