Spectra-trait PLSR example using leaf-level spectra and leaf nitrogen content (Narea, g/m2) data from 36 species growing in Rosa rugosa invaded coastal grassland communities in Belgium

Overview

This is an R Markdown Notebook to illustrate how to retrieve a dataset from the EcoSIS spectral database, choose the “optimal” number of plsr components, and fit a plsr model for leaf nitrogen content (Narea, g/m2)

Getting Started

Load libraries

list.of.packages <- c("pls","dplyr","here","plotrix","ggplot2","gridExtra","spectratrait")
invisible(lapply(list.of.packages, library, character.only = TRUE))

Attaching package: ‘pls’

The following object is masked from ‘package:stats’:

    loadings


Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

here() starts at /Users/sserbin/Data/GitHub/spectratrait

Attaching package: ‘gridExtra’

The following object is masked from ‘package:dplyr’:

    combine

Setup other functions and options

### Setup options

# Script options
pls::pls.options(plsralg = "oscorespls")
pls::pls.options("plsralg")

$plsralg
[1] "oscorespls"

# Default par options
opar <- par(no.readonly = T)

# What is the target variable?
inVar <- "Narea_g_m2"

# What is the source dataset from EcoSIS?
ecosis_id <- "9db4c5a2-7eac-4e1e-8859-009233648e89"

# Specify output directory, output_dir 
# Options: 
# tempdir - use a OS-specified temporary directory 
# user defined PATH - e.g. "~/scratch/PLSR"
output_dir <- "tempdir"

Set working directory (scratch space)

The working directory was changed to /private/var/folders/xp/h3k9vf3n2jx181ts786_yjrn9c2gjq/T/RtmpdFG9hz inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

[1] "/private/var/folders/xp/h3k9vf3n2jx181ts786_yjrn9c2gjq/T/RtmpdFG9hz"

Grab data from EcoSIS

print(paste0("Output directory: ",getwd()))  # check wd

[1] "Output directory: /Users/sserbin/Data/GitHub/spectratrait/vignettes"

dat_raw <- spectratrait::get_ecosis_data(ecosis_id = ecosis_id)

[1] "**** Downloading Ecosis data ****"

Downloading data...
Rows: 256 Columns: 2164── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr    (4): Latin Species, ids, plot code, species code
dbl (2160): Cw/EWT (cm3/cm2), Leaf area (mm2), Leaf calcium content per leaf area (mg/mm2), Leaf magnesium content per leaf area (mg/mm2), Leaf ...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Download complete!

head(dat_raw)

names(dat_raw)[1:40]

 [1] "Cw/EWT (cm3/cm2)"                               "Latin Species"                                 
 [3] "Leaf area (mm2)"                                "Leaf calcium content per leaf area (mg/mm2)"   
 [5] "Leaf magnesium content per leaf area (mg/mm2)"  "Leaf mass per area (g/cm2)"                    
 [7] "Leaf nitrogen content per leaf area (mg/mm2)"   "Leaf phosphorus content per leaf area (mg/mm2)"
 [9] "Leaf potassium content per leaf area (mg/mm2)"  "Plant height vegetative (cm)"                  
[11] "ids"                                            "plot code"                                     
[13] "species code"                                   "350"                                           
[15] "351"                                            "352"                                           
[17] "353"                                            "354"                                           
[19] "355"                                            "356"                                           
[21] "357"                                            "358"                                           
[23] "359"                                            "360"                                           
[25] "361"                                            "362"                                           
[27] "363"                                            "364"                                           
[29] "365"                                            "366"                                           
[31] "367"                                            "368"                                           
[33] "369"                                            "370"                                           
[35] "371"                                            "372"                                           
[37] "373"                                            "374"                                           
[39] "375"                                            "376"                                           

Create full plsr dataset

### Create plsr dataset
Start.wave <- 500
End.wave <- 2400
wv <- seq(Start.wave,End.wave,1)
Spectra <- as.matrix(dat_raw[,names(dat_raw) %in% wv])
colnames(Spectra) <- c(paste0("Wave_",wv))
sample_info <- dat_raw[,names(dat_raw) %notin% seq(350,2500,1)]
head(sample_info)

sample_info2 <- sample_info %>%
  select(Plant_Species=`Latin Species`,Species_Code=`species code`,Plot=`plot code`,
         Narea_mg_mm2=`Leaf nitrogen content per leaf area (mg/mm2)`)
sample_info2 <- sample_info2 %>%
#  mutate(Narea_g_m2=Narea_mg_mm2*(0.001/1e-6)) # based on orig units should be this but conversion wrong
  mutate(Narea_g_m2=Narea_mg_mm2*100) # this assumes orig units were g/mm2 or mg/cm2
head(sample_info2)

plsr_data <- data.frame(sample_info2,Spectra)
rm(sample_info,sample_info2,Spectra)

Example data cleaning.

#### End user needs to do what's appropriate for their data.  This may be an iterative process.
# Keep only complete rows of inVar and spec data before fitting
plsr_data <- plsr_data[complete.cases(plsr_data[,names(plsr_data) %in% 
                                                  c(inVar,paste0("Wave_",wv))]),]

Create cal/val datasets

### Create cal/val datasets
## Make a stratified random sampling in the strata USDA_Species_Code and Domain

method <- "dplyr" #base/dplyr
# base R - a bit slow
# dplyr - much faster
split_data <- spectratrait::create_data_split(dataset=plsr_data, approach=method, split_seed=1245565, 
                                              prop=0.8, group_variables="Species_Code")
names(split_data)

[1] "cal_data" "val_data"

cal.plsr.data <- split_data$cal_data
head(cal.plsr.data)[1:8]

val.plsr.data <- split_data$val_data
head(val.plsr.data)[1:8]

rm(split_data)

# Datasets:
print(paste("Cal observations: ",dim(cal.plsr.data)[1],sep=""))

[1] "Cal observations: 183"

print(paste("Val observations: ",dim(val.plsr.data)[1],sep=""))

[1] "Val observations: 73"

cal_hist_plot <- qplot(cal.plsr.data[,paste0(inVar)],geom="histogram",
                       main = paste0("Cal. Histogram for ",inVar),
                       xlab = paste0(inVar),ylab = "Count",fill=I("grey50"),col=I("black"),
                       alpha=I(.7))
val_hist_plot <- qplot(val.plsr.data[,paste0(inVar)],geom="histogram",
                       main = paste0("Val. Histogram for ",inVar),
                       xlab = paste0(inVar),ylab = "Count",fill=I("grey50"),col=I("black"),
                       alpha=I(.7))
histograms <- grid.arrange(cal_hist_plot, val_hist_plot, ncol=2)

ggsave(filename = file.path(outdir,paste0(inVar,"_Cal_Val_Histograms.png")), plot = histograms, 
       device="png", width = 30, 
       height = 12, units = "cm",
       dpi = 300)
# output cal/val data
write.csv(cal.plsr.data,file=file.path(outdir,paste0(inVar,'_Cal_PLSR_Dataset.csv')),
          row.names=FALSE)
write.csv(val.plsr.data,file=file.path(outdir,paste0(inVar,'_Val_PLSR_Dataset.csv')),
          row.names=FALSE)

Create calibration and validation PLSR datasets

### Format PLSR data for model fitting 
cal_spec <- as.matrix(cal.plsr.data[, which(names(cal.plsr.data) %in% paste0("Wave_",wv))])
cal.plsr.data <- data.frame(cal.plsr.data[, which(names(cal.plsr.data) %notin% paste0("Wave_",wv))],
                            Spectra=I(cal_spec))
head(cal.plsr.data)[1:5]

val_spec <- as.matrix(val.plsr.data[, which(names(val.plsr.data) %in% paste0("Wave_",wv))])
val.plsr.data <- data.frame(val.plsr.data[, which(names(val.plsr.data) %notin% paste0("Wave_",wv))],
                            Spectra=I(val_spec))
head(val.plsr.data)[1:5]

plot cal and val spectra

par(mfrow=c(1,2)) # B, L, T, R
spectratrait::f.plot.spec(Z=cal.plsr.data$Spectra,wv=wv,plot_label="Calibration")
spectratrait::f.plot.spec(Z=val.plsr.data$Spectra,wv=wv,plot_label="Validation")

dev.copy(png,file.path(outdir,paste0(inVar,'_Cal_Val_Spectra.png')), 
         height=2500,width=4900, res=340)

quartz_off_screen 
                3 

dev.off();

quartz_off_screen 
                2 

par(mfrow=c(1,1))

Use Jackknife permutation to determine optimal number of components

### Use permutation to determine the optimal number of components
if(grepl("Windows", sessionInfo()$running)){
  pls.options(parallel = NULL)
} else {
  pls.options(parallel = parallel::detectCores()-1)
}

method <- "pls" #pls, firstPlateau, firstMin
random_seed <- 1245565
seg <- 50
maxComps <- 16
iterations <- 80
prop <- 0.70
if (method=="pls") {
  # pls package approach - faster but estimates more components....
  nComps <- spectratrait::find_optimal_components(dataset=cal.plsr.data, targetVariable=inVar, 
                                                  method=method, 
                                                  maxComps=maxComps, seg=seg, 
                                                  random_seed=random_seed)
  print(paste0("*** Optimal number of components: ", nComps))
} else {
  nComps <- spectratrait::find_optimal_components(dataset=cal.plsr.data, targetVariable=inVar,
                                                  method=method, 
                                                  maxComps=maxComps, iterations=iterations, 
                                                  seg=seg, prop=prop, 
                                                  random_seed=random_seed)
}

[1] "*** Identifying optimal number of PLSR components ***"
[1] "*** Running PLS permutation test ***"
[1] "*** Optimal number of components: 10"

dev.copy(png,file.path(outdir,paste0(paste0(inVar,"_PLSR_Component_Selection.png"))), 
         height=2800, width=3400,  res=340)

quartz_off_screen 
                3 

dev.off();

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                2 

Fit final model

plsr.out <- plsr(as.formula(paste(inVar,"~","Spectra")),scale=FALSE,ncomp=nComps,validation="LOO",
                 trace=FALSE,data=cal.plsr.data)
fit <- plsr.out$fitted.values[,1,nComps]
pls.options(parallel = NULL)

# External validation fit stats
par(mfrow=c(1,2)) # B, L, T, R
pls::RMSEP(plsr.out, newdata = val.plsr.data)

(Intercept)      1 comps      2 comps      3 comps      4 comps      5 comps      6 comps      7 comps      8 comps      9 comps     10 comps  
     0.5594       0.6034       0.5448       0.3842       0.3481       0.3027       0.2429       0.2268       0.2852       0.2818       0.2780  

plot(pls::RMSEP(plsr.out,estimate=c("test"),newdata = val.plsr.data), main="MODEL RMSEP",
     xlab="Number of Components",ylab="Model Validation RMSEP",lty=1,col="black",cex=1.5,lwd=2)
box(lwd=2.2)

pls::R2(plsr.out, newdata = val.plsr.data)

(Intercept)      1 comps      2 comps      3 comps      4 comps      5 comps      6 comps      7 comps      8 comps      9 comps     10 comps  
  -0.007544    -0.172296     0.044153     0.524579     0.609920     0.704963     0.809962     0.834383     0.738093     0.744325     0.751224  

plot(pls::R2(plsr.out,estimate=c("test"),newdata = val.plsr.data), main="MODEL R2",
     xlab="Number of Components",ylab="Model Validation R2",lty=1,col="black",cex=1.5,lwd=2)
box(lwd=2.2)

dev.copy(png,file.path(outdir,paste0(paste0(inVar,"_Validation_RMSEP_R2_by_Component.png"))), 
         height=2800, width=4800,  res=340)

quartz_off_screen 
                3 

dev.off();

quartz_off_screen 
                2 

par(opar)

PLSR fit observed vs. predicted plot data

#calibration
cal.plsr.output <- data.frame(cal.plsr.data[, which(names(cal.plsr.data) %notin% "Spectra")],
                              PLSR_Predicted=fit,
                              PLSR_CV_Predicted=as.vector(plsr.out$validation$pred[,,nComps]))
cal.plsr.output <- cal.plsr.output %>%
  mutate(PLSR_CV_Residuals = PLSR_CV_Predicted-get(inVar))
head(cal.plsr.output)

cal.R2 <- round(pls::R2(plsr.out,intercept=F)[[1]][nComps],2)
cal.RMSEP <- round(sqrt(mean(cal.plsr.output$PLSR_CV_Residuals^2)),2)

val.plsr.output <- data.frame(val.plsr.data[, which(names(val.plsr.data) %notin% "Spectra")],
                              PLSR_Predicted=as.vector(predict(plsr.out, 
                                                               newdata = val.plsr.data, 
                                                               ncomp=nComps, type="response")[,,1]))
val.plsr.output <- val.plsr.output %>%
  mutate(PLSR_Residuals = PLSR_Predicted-get(inVar))
head(val.plsr.output)

val.R2 <- round(pls::R2(plsr.out,newdata=val.plsr.data,intercept=F)[[1]][nComps],2)
val.RMSEP <- round(sqrt(mean(val.plsr.output$PLSR_Residuals^2)),2)

rng_quant <- quantile(cal.plsr.output[,inVar], probs = c(0.001, 0.999))
cal_scatter_plot <- ggplot(cal.plsr.output, aes(x=PLSR_CV_Predicted, y=get(inVar))) + 
  theme_bw() + geom_point() + geom_abline(intercept = 0, slope = 1, color="dark grey", 
                                          linetype="dashed", size=1.5) + xlim(rng_quant[1], 
                                                                              rng_quant[2]) + 
  ylim(rng_quant[1], rng_quant[2]) +
  labs(x=paste0("Predicted ", paste(inVar), " (units)"),
       y=paste0("Observed ", paste(inVar), " (units)"),
       title=paste0("Calibration: ", paste0("Rsq = ", cal.R2), "; ", paste0("RMSEP = ", 
                                                                            cal.RMSEP))) +
  theme(axis.text=element_text(size=18), legend.position="none",
        axis.title=element_text(size=20, face="bold"), 
        axis.text.x = element_text(angle = 0,vjust = 0.5),
        panel.border = element_rect(linetype = "solid", fill = NA, size=1.5))

cal_resid_histogram <- ggplot(cal.plsr.output, aes(x=PLSR_CV_Residuals)) +
  geom_histogram(alpha=.5, position="identity") + 
  geom_vline(xintercept = 0, color="black", 
             linetype="dashed", size=1) + theme_bw() + 
  theme(axis.text=element_text(size=18), legend.position="none",
        axis.title=element_text(size=20, face="bold"), 
        axis.text.x = element_text(angle = 0,vjust = 0.5),
        panel.border = element_rect(linetype = "solid", fill = NA, size=1.5))

rng_quant <- quantile(val.plsr.output[,inVar], probs = c(0.001, 0.999))
val_scatter_plot <- ggplot(val.plsr.output, aes(x=PLSR_Predicted, y=get(inVar))) + 
  theme_bw() + geom_point() + geom_abline(intercept = 0, slope = 1, color="dark grey", 
                                          linetype="dashed", size=1.5) + xlim(rng_quant[1], 
                                                                              rng_quant[2]) + 
  ylim(rng_quant[1], rng_quant[2]) +
  labs(x=paste0("Predicted ", paste(inVar), " (units)"),
       y=paste0("Observed ", paste(inVar), " (units)"),
       title=paste0("Validation: ", paste0("Rsq = ", val.R2), "; ", paste0("RMSEP = ", 
                                                                           val.RMSEP))) +
  theme(axis.text=element_text(size=18), legend.position="none",
        axis.title=element_text(size=20, face="bold"), 
        axis.text.x = element_text(angle = 0,vjust = 0.5),
        panel.border = element_rect(linetype = "solid", fill = NA, size=1.5))

val_resid_histogram <- ggplot(val.plsr.output, aes(x=PLSR_Residuals)) +
  geom_histogram(alpha=.5, position="identity") + 
  geom_vline(xintercept = 0, color="black", 
             linetype="dashed", size=1) + theme_bw() + 
  theme(axis.text=element_text(size=18), legend.position="none",
        axis.title=element_text(size=20, face="bold"), 
        axis.text.x = element_text(angle = 0,vjust = 0.5),
        panel.border = element_rect(linetype = "solid", fill = NA, size=1.5))

# plot cal/val side-by-side
scatterplots <- grid.arrange(cal_scatter_plot, val_scatter_plot, cal_resid_histogram, 
                             val_resid_histogram, nrow=2,ncol=2)

ggsave(filename = file.path(outdir,paste0(inVar,"_Cal_Val_Scatterplots.png")), 
       plot = scatterplots, device="png", 
       width = 32, 
       height = 30, units = "cm",
       dpi = 300)

Generate Coefficient and VIP plots

vips <- spectratrait::VIP(plsr.out)[nComps,]
par(mfrow=c(2,1))
plot(plsr.out, plottype = "coef",xlab="Wavelength (nm)",
     ylab="Regression coefficients",legendpos = "bottomright",
     ncomp=nComps,lwd=2)
box(lwd=2.2)

plot(seq(Start.wave,End.wave,1),vips,xlab="Wavelength (nm)",ylab="VIP",cex=0.01)
lines(seq(Start.wave,End.wave,1),vips,lwd=3)

abline(h=0.8,lty=2,col="dark grey")
box(lwd=2.2)

dev.copy(png,file.path(outdir,paste0(inVar,'_Coefficient_VIP_plot.png')), 
         height=3100, width=4100, res=340)

quartz_off_screen 
                3 

dev.off();

quartz_off_screen 
                2 

Jackknife validation

if(grepl("Windows", sessionInfo()$running)){
  pls.options(parallel =NULL)
} else {
  pls.options(parallel = parallel::detectCores()-1)
}

jk.plsr.out <- pls::plsr(as.formula(paste(inVar,"~","Spectra")), scale=FALSE, 
                         center=TRUE, ncomp=nComps, validation="LOO", trace=FALSE, 
                         jackknife=TRUE, 
                         data=cal.plsr.data)
pls.options(parallel = NULL)

Jackknife_coef <- spectratrait::f.coef.valid(plsr.out = jk.plsr.out, data_plsr = cal.plsr.data, 
                               ncomp = nComps, inVar=inVar)
Jackknife_intercept <- Jackknife_coef[1,,,]
Jackknife_coef <- Jackknife_coef[2:dim(Jackknife_coef)[1],,,]

interval <- c(0.025,0.975)
Jackknife_Pred <- val.plsr.data$Spectra %*% Jackknife_coef + 
  matrix(rep(Jackknife_intercept, length(val.plsr.data[,inVar])), byrow=TRUE, 
         ncol=length(Jackknife_intercept))
Interval_Conf <- apply(X = Jackknife_Pred, MARGIN = 1, FUN = quantile, 
                       probs=c(interval[1], interval[2]))
sd_mean <- apply(X = Jackknife_Pred, MARGIN = 1, FUN =sd)
sd_res <- sd(val.plsr.output$PLSR_Residuals)
sd_tot <- sqrt(sd_mean^2+sd_res^2)
val.plsr.output$LCI <- Interval_Conf[1,]
val.plsr.output$UCI <- Interval_Conf[2,]
val.plsr.output$LPI <- val.plsr.output$PLSR_Predicted-1.96*sd_tot
val.plsr.output$UPI <- val.plsr.output$PLSR_Predicted+1.96*sd_tot
head(val.plsr.output)

val.plsr.output$LPI <- val.plsr.output$PLSR_Predicted-1.96*sd_tot
val.plsr.output$UPI <- val.plsr.output$PLSR_Predicted+1.96*sd_tot
head(val.plsr.output)

Jackknife coefficient plot

spectratrait::f.plot.coef(Z = t(Jackknife_coef), wv = wv, 
            plot_label="Jackknife regression coefficients",position = 'bottomleft')
abline(h=0,lty=2,col="grey50")

box(lwd=2.2)
dev.copy(png,file.path(outdir,paste0(inVar,'_Jackknife_Regression_Coefficients.png')), 
         height=2100, width=3800, res=340)

quartz_off_screen 
                3 

dev.off();

quartz_off_screen 
                2 

Jackknife validation plot

rmsep_percrmsep <- spectratrait::percent_rmse(plsr_dataset = val.plsr.output, 
                                              inVar = inVar, 
                                              residuals = val.plsr.output$PLSR_Residuals, 
                                              range="full")
RMSEP <- rmsep_percrmsep$rmse
perc_RMSEP <- rmsep_percrmsep$perc_rmse
r2 <- round(pls::R2(plsr.out, newdata = val.plsr.data,intercept=F)$val[nComps],2)
expr <- vector("expression", 3)
expr[[1]] <- bquote(R^2==.(r2))
expr[[2]] <- bquote(RMSEP==.(round(RMSEP,2)))
expr[[3]] <- bquote("%RMSEP"==.(round(perc_RMSEP,2)))
rng_vals <- c(min(val.plsr.output$LPI), max(val.plsr.output$UPI))
par(mfrow=c(1,1), mar=c(4.2,5.3,1,0.4), oma=c(0, 0.1, 0, 0.2))
plotrix::plotCI(val.plsr.output$PLSR_Predicted,val.plsr.output[,inVar], 
       li=val.plsr.output$LPI, ui=val.plsr.output$UPI, gap=0.009,sfrac=0.004, 
       lwd=1.6, xlim=c(rng_vals[1], rng_vals[2]), ylim=c(rng_vals[1], rng_vals[2]), 
       err="x", pch=21, col="black", pt.bg=scales::alpha("grey70",0.7), scol="grey50",
       cex=2, xlab=paste0("Predicted ", paste(inVar), " (units)"),
       ylab=paste0("Observed ", paste(inVar), " (units)"),
       cex.axis=1.5,cex.lab=1.8)
abline(0,1,lty=2,lw=2)

legend("topleft", legend=expr, bty="n", cex=1.5)
box(lwd=2.2)

dev.copy(png,file.path(outdir,paste0(inVar,"_PLSR_Validation_Scatterplot.png")), 
         height=2800, width=3200,  res=340)

quartz_off_screen 
                3 

dev.off();

quartz_off_screen 
                2 

Output jackknife results

out.jk.coefs <- data.frame(Iteration=seq(1,length(Jackknife_intercept),1),
                           Intercept=Jackknife_intercept,t(Jackknife_coef))
head(out.jk.coefs)[1:6]

write.csv(out.jk.coefs,file=file.path(outdir,
                                      paste0(inVar,
                                             '_Jackkife_PLSR_Coefficients.csv')),
          row.names=FALSE)

Create core PLSR outputs

print(paste("Output directory: ", outdir))

[1] "Output directory:  /var/folders/xp/h3k9vf3n2jx181ts786_yjrn9c2gjq/T//RtmpdFG9hz"

# Observed versus predicted
write.csv(cal.plsr.output,file=file.path(outdir,
                                         paste0(inVar,'_Observed_PLSR_CV_Pred_',
                                                nComps,'comp.csv')),
          row.names=FALSE)

# Validation data
write.csv(val.plsr.output,file=file.path(outdir,
                                         paste0(inVar,'_Validation_PLSR_Pred_',
                                                nComps,'comp.csv')),
          row.names=FALSE)

# Model coefficients
coefs <- coef(plsr.out,ncomp=nComps,intercept=TRUE)
write.csv(coefs,file=file.path(outdir,
                               paste0(inVar,'_PLSR_Coefficients_',
                                      nComps,'comp.csv')),
          row.names=TRUE)

# PLSR VIP
write.csv(vips,file=file.path(outdir,
                              paste0(inVar,'_PLSR_VIPs_',
                                     nComps,'comp.csv')))

Confirm files were written to temp space

print("**** PLSR output files: ")

[1] "**** PLSR output files: "

print(list.files(outdir)[grep(pattern = inVar, list.files(outdir))])

 [1] "Narea_g_m2_Cal_PLSR_Dataset.csv"                  "Narea_g_m2_Cal_Val_Histograms.png"               
 [3] "Narea_g_m2_Cal_Val_Scatterplots.png"              "Narea_g_m2_Cal_Val_Spectra.png"                  
 [5] "Narea_g_m2_Coefficient_VIP_plot.png"              "Narea_g_m2_Jackkife_PLSR_Coefficients.csv"       
 [7] "Narea_g_m2_Jackknife_Regression_Coefficients.png" "Narea_g_m2_Observed_PLSR_CV_Pred_10comp.csv"     
 [9] "Narea_g_m2_PLSR_Coefficients_10comp.csv"          "Narea_g_m2_PLSR_Component_Selection.png"         
[11] "Narea_g_m2_PLSR_Validation_Scatterplot.png"       "Narea_g_m2_PLSR_VIPs_10comp.csv"                 
[13] "Narea_g_m2_Val_PLSR_Dataset.csv"                  "Narea_g_m2_Validation_PLSR_Pred_10comp.csv"      
[15] "Narea_g_m2_Validation_RMSEP_R2_by_Component.png"