ML-Lab-2: What the package does (short line)

library(XLConnect)
library(fastICA)
library(pls)
#FROM THIS FILE LOCATION, EXCEL FILES SHOULD BE FOUND IN A SUBFOLDER IN THIS FILE LOCATION CALLED DATA
wb = loadWorkbook("C:/Users/Dator/Documents/R_HW/ML-Lab-2/data/NIRSpectra.xls")
data2 = readWorksheet(wb, sheet = "NIRSpectra", header = TRUE)
data2 <- data2[,-1]
data2 <- data2[,-length(data2)]
#data2 <-data2[complete.cases(data2),]
#head(data2)
set.seed(12345)
res=prcomp(data2,scale=TRUE)
resul <- princomp(data2,scale=TRUE)
lambda=res$sdev^2
#eigenvalues
# lambda
#proportion of variation
sprintf("%2.3f",lambda/sum(lambda)*100)
# two pcs are needed to explain 99% of 
screeplot(res,main="Largest contributions to variance",xlab="Principal components")
plot(res$x[,1],res$x[,2],pch="+",main="PCA scoreplot of PC2 against PC1, unequal axes",xlab="PC1",ylab="PC2",cex=0.5)
# biplot(res)
# U=res$rotation
# head(U)
U=loadings(resul)
plot(res$rotation[-nrow(res$rotation),1],main="PCA Traceplot, PC1",xlab="component",ylab="coefficients")
plot(res$rotation[-nrow(res$rotation),2],main="PCA Traceplot, PC2",xlab="component",ylab="coefficients")
# plot(U[,1], main="PCA Traceplot, PC1")
# plot(U[,2],main="PCA Traceplot, PC2")

set.seed(12345)
res2 <- fastICA(data2,2)
#res2$W is the unmixing matrix "X"W = S where S contain independent components
#res2$K is a pre-whitening matrix which projects data onto first 2 principal components XKW = S
projectmatr <- res2$K %*% res2$W
plot(res2$K[,1], main="ICA K-matrix Traceplot, PC1")
plot(res2$K[,2], main="ICA K-matrix Traceplot, PC2")
#^^These are same as for PCA
plot(projectmatr[,1], main="ICA Traceplot, PC1")
plot(projectmatr[,2],main="ICA Traceplot, PC2")
plot(res2$S,pch="+",main="ICA scoreplot of PC2 against PC1, unequal axes",xlab="PC1",ylab="PC2",cex=0.5)


data2 <- readWorksheet(wb, sheet = "NIRSpectra", header = TRUE)
data2 <- data2[,-1]
#data2 <-data2[complete.cases(data2),]

set.seed(12345)
ind <- sample(1:784,392)
train <- data2[ind,]
test <- data2[-ind,]
n <- dim(test[complete.cases(test),])[1]

set.seed(12345)
pcr.fit=pcr(Viscosity~., data=train, validation="CV") 
validationplot(pcr.fit,val.type="MSEP",main="MSEP scores for CV:ed PCR models") 
pcrmodel=pcr(Viscosity~., 20,data=train, validation="none") 
pcr.pred <- predict(pcrmodel, newdata=test[complete.cases(test),], ncomp = 20) 
pcr.mse <- 1/n * sum((test[complete.cases(test),]$Viscosity - pcr.pred)^2, na.rm=TRUE)
paste("MSE for test data for PCR model with 20 components:", signif(pcr.mse,3)) 

set.seed(12345)
plsr.fit <- plsr(Viscosity~., data=train, validation="CV")
validationplot(plsr.fit,val.type="MSEP",main="MSEP scores for CV:ed PLS models") 
plsmodel=plsr(Viscosity~., 10,data=train, validation="none") 
pls.pred <- predict(plsmodel, newdata=test[complete.cases(test),], ncomp = 10) 
pls.mse <- 1/n * sum((test[complete.cases(test),]$Viscosity - pls.pred)^2, na.rm=TRUE) 
paste("MSE for test data for PLS model with 10 components:", signif(pls.mse,3))